Demonstration of UAV deployment and control of mobile wireless sensing networks for modal analysis of structures

NASA Astrophysics Data System (ADS)

Zhou, Hao; Hirose, Mitsuhito; Greenwood, William; Xiao, Yong; Lynch, Jerome; Zekkos, Dimitrios; Kamat, Vineet

2016-04-01

Unmanned aerial vehicles (UAVs) can serve as a powerful mobile sensing platform for assessing the health of civil infrastructure systems. To date, the majority of their uses have been dedicated to vision and laser-based spatial imaging using on-board cameras and LiDAR units, respectively. Comparatively less work has focused on integration of other sensing modalities relevant to structural monitoring applications. The overarching goal of this study is to explore the ability for UAVs to deploy a network of wireless sensors on structures for controlled vibration testing. The study develops a UAV platform with an integrated robotic gripper that can be used to install wireless sensors in structures, drop a heavy weight for the introduction of impact loads, and to uninstall wireless sensors for reinstallation elsewhere. A pose estimation algorithm is embedded in the UAV to estimate the location of the UAV during sensor placement and impact load introduction. The Martlet wireless sensor network architecture is integrated with the UAV to provide the UAV a mobile sensing capability. The UAV is programmed to command field deployed Martlets, aggregate and temporarily store data from the wireless sensor network, and to communicate data to a fixed base station on site. This study demonstrates the integrated UAV system using a simply supported beam in the lab with Martlet wireless sensors placed by the UAV and impact load testing performed. The study verifies the feasibility of the integrated UAV-wireless monitoring system architecture with accurate modal characteristics of the beam estimated by modal analysis.

Using Public Network Infrastructures for UAV Remote Sensing in Civilian Security Operations

DTIC Science & Technology

2011-03-01

leveraging public wireless communication networks for UAV-based sensor networks with respect to existing constraints and user requirements...Detection with an Autonomous Micro UAV Mesh Network . In the near future police departments, fire brigades and other homeland security ...UAV-based sensor networks with respect to existing constraints and user requirements. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

Smart Cruise Control: UAV sensor operator intent estimation and its application

NASA Astrophysics Data System (ADS)

Cheng, Hui; Butler, Darren; Kumar, Rakesh

2006-05-01

Due to their long endurance, superior mobility and the low risk posed to the pilot and sensor operator, UAVs have become the preferred platform for persistent ISR missions. However, currently most UAV based ISR missions are conducted through manual operation. Event the simplest tasks, such as vehicle tracking, route reconnaissance and site monitoring, need the sensor operator's undivided attention and constant adjustment of the sensor control. The lack of autonomous behaviour greatly limits of the effectiveness and the capability of UAV-based ISR, especially the use of a large number of UAVs simultaneously. Although fully autonomous UAV based ISR system is desirable, it is still a distant dream due to the complexity and diversity of combat and ISR missions. In this paper, we propose a Smart Cruise Control system that can learn UAV sensor operator's intent and use it to complete tasks automatically, such as route reconnaissance and site monitoring. Using an operator attention model, the proposed system can estimate the operator's intent from how they control the sensor (e.g. camera) and the content of the imagery that is acquired. Therefore, for example, from initially manually controlling the UAV sensor to follow a road, the system can learn not only the preferred operation, "tracking", but also the road appearance, "what to track" in real-time. Then, the learnt models of both road and the desired operation can be used to complete the task automatically. We have demonstrated the Smart Cruise Control system using real UAV videos where roads need to be tracked and buildings need to be monitored.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Multiple UAV Cooperation for Wildfire Monitoring

NASA Astrophysics Data System (ADS)

Lin, Zhongjie

Wildfires have been a major factor in the development and management of the world's forest. An accurate assessment of wildfire status is imperative for fire management. This thesis is dedicated to the topic of utilizing multiple unmanned aerial vehicles (UAVs) to cooperatively monitor a large-scale wildfire. This is achieved through wildfire spreading situation estimation based on on-line measurements and wise cooperation strategy to ensure efficiency. First, based on the understanding of the physical characteristics of the wildfire propagation behavior, a wildfire model and a Kalman filter-based method are proposed to estimate the wildfire rate of spread and the fire front contour profile. With the enormous on-line measurements from on-board sensors of UAVs, the proposed method allows a wildfire monitoring mission to benefit from on-line information updating, increased flexibility, and accurate estimation. An independent wildfire simulator is utilized to verify the effectiveness of the proposed method. Second, based on the filter analysis, wildfire spreading situation and vehicle dynamics, the influence of different cooperation strategies of UAVs to the overall mission performance is studied. The multi-UAV cooperation problem is formulated in a distributed network. A consensus-based method is proposed to help address the problem. The optimal cooperation strategy of UAVs is obtained through mathematical analysis. The derived optimal cooperation strategy is then verified in an independent fire simulation environment to verify its effectiveness.

Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs

PubMed Central

Cao, Huiru; Liu, Yongxin; Yue, Xuejun; Zhu, Wenjian

2017-01-01

In recent years, UAVs (Unmanned Aerial Vehicles) have been widely applied for data collection and image capture. Specifically, UAVs have been integrated with wireless sensor networks (WSNs) to create data collection platforms with high flexibility. However, most studies in this domain focus on system architecture and UAVs’ flight trajectory planning while event-related factors and other important issues are neglected. To address these challenges, we propose a cloud-assisted data gathering strategy for UAV-based WSN in the light of emerging events. We also provide a cloud-assisted approach for deriving UAV’s optimal flying and data acquisition sequence of a WSN cluster. We validate our approach through simulations and experiments. It has been proved that our methodology outperforms conventional approaches in terms of flying time, energy consumption, and integrity of data acquisition. We also conducted a real-world experiment using a UAV to collect data wirelessly from multiple clusters of sensor nodes for monitoring an emerging event, which are deployed in a farm. Compared against the traditional method, this proposed approach requires less than half the flying time and achieves almost perfect data integrity. PMID:28783100

Sensor-driven area coverage for an autonomous fixed-wing unmanned aerial vehicle.

PubMed

Paull, Liam; Thibault, Carl; Nagaty, Amr; Seto, Mae; Li, Howard

2014-09-01

Area coverage with an onboard sensor is an important task for an unmanned aerial vehicle (UAV) with many applications. Autonomous fixed-wing UAVs are more appropriate for larger scale area surveying since they can cover ground more quickly. However, their non-holonomic dynamics and susceptibility to disturbances make sensor coverage a challenging task. Most previous approaches to area coverage planning are offline and assume that the UAV can follow the planned trajectory exactly. In this paper, this restriction is removed as the aircraft maintains a coverage map based on its actual pose trajectory and makes control decisions based on that map. The aircraft is able to plan paths in situ based on sensor data and an accurate model of the on-board camera used for coverage. An information theoretic approach is used that selects desired headings that maximize the expected information gain over the coverage map. In addition, the branch entropy concept previously developed for autonomous underwater vehicles is extended to UAVs and ensures that the vehicle is able to achieve its global coverage mission. The coverage map over the workspace uses the projective camera model and compares the expected area of the target on the ground and the actual area covered on the ground by each pixel in the image. The camera is mounted on a two-axis gimbal and can either be stabilized or optimized for maximal coverage. Hardware-in-the-loop simulation results and real hardware implementation on a fixed-wing UAV show the effectiveness of the approach. By including the already developed automatic takeoff and landing capabilities, we now have a fully automated and robust platform for performing aerial imagery surveys.

Precise Positioning of Uavs - Dealing with Challenging Rtk-Gps Measurement Conditions during Automated Uav Flights

NASA Astrophysics Data System (ADS)

Zimmermann, F.; Eling, C.; Klingbeil, L.; Kuhlmann, H.

2017-08-01

For some years now, UAVs (unmanned aerial vehicles) are commonly used for different mobile mapping applications, such as in the fields of surveying, mining or archeology. To improve the efficiency of these applications an automation of the flight as well as the processing of the collected data is currently aimed at. One precondition for an automated mapping with UAVs is that the georeferencing is performed directly with cm-accuracies or better. Usually, a cm-accurate direct positioning of UAVs is based on an onboard multi-sensor system, which consists of an RTK-capable (real-time kinematic) GPS (global positioning system) receiver and additional sensors (e.g. inertial sensors). In this case, the absolute positioning accuracy essentially depends on the local GPS measurement conditions. Especially during mobile mapping applications in urban areas, these conditions can be very challenging, due to a satellite shadowing, non-line-of sight receptions, signal diffraction or multipath effects. In this paper, two straightforward and easy to implement strategies will be described and analyzed, which improve the direct positioning accuracies for UAV-based mapping and surveying applications under challenging GPS measurement conditions. Based on a 3D model of the surrounding buildings and vegetation in the area of interest, a GPS geometry map is determined, which can be integrated in the flight planning process, to avoid GPS challenging environments as far as possible. If these challenging environments cannot be avoided, the GPS positioning solution is improved by using obstruction adaptive elevation masks, to mitigate systematic GPS errors in the RTK-GPS positioning. Simulations and results of field tests demonstrate the profit of both strategies.

Terrain mapping and control of unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Kang, Yeonsik

In this thesis, methods for terrain mapping and control of unmanned aerial vehicles (UAVs) are proposed. First, robust obstacle detection and tracking algorithm are introduced to eliminate the clutter noise uncorrelated with the real obstacle. This is an important problem since most types of sensor measurements are vulnerable to noise. In order to eliminate such noise, a Kalman filter-based interacting multiple model (IMM) algorithm is employed to effectively detect obstacles and estimate their positions precisely. Using the outcome of the IMM-based obstacle detection algorithm, a new method of building a probabilistic occupancy grid map is proposed based on Bayes rule in probability theory. Since the proposed map update law uses the outputs of the IMM-based obstacle detection algorithm, simultaneous tracking of moving targets and mapping of stationary obstacles are possible. This can be helpful especially in a noisy outdoor environment where different types of obstacles exist. Another feature of the algorithm is its capability to eliminate clutter noise as well as measurement noise. The proposed algorithm is simulated in Matlab using realistic sensor models. The results show close agreement with the layout of real obstacles. An efficient method called "quadtree" is used to process massive geographical information in a convenient manner. The algorithm is evaluated in a realistic simulation environment called RIPTIDE, which the NASA Ames Research Center developed to access the performance of complicated software for UAVs. Supposing that a UAV is equipped with abovementioned obstacle detection and mapping algorithm, the control problem of a small fixed-wing UAV is studied. A Nonlinear Model Predictive Control (NMPC is designed as a high level controller for the fixed-wing UAV using a kinematic model of the UAV. The kinematic model is employed because of the assumption that there exist low level controls on the UAV. The UAV dynamics are nonlinear with input constraints which is the main challenge explored in this thesis. The control objective of the NMPC is determined to track a desired line, and the analysis of the designed NMPC's stability is followed to find the conditions that can assure stability. Then, the control objective is extended to track adjoined multiple line segments with obstacle avoidance capability. In simulation, the performance of the NMPC is superb with fast convergence and small overshoot. The computation time is not a burden for a fixed-wing UAV controller with a Pentium level on-board computer that provides a reasonable control update rate.

A Camera-Based Target Detection and Positioning UAV System for Search and Rescue (SAR) Purposes

PubMed Central

Sun, Jingxuan; Li, Boyang; Jiang, Yifan; Wen, Chih-yung

2016-01-01

Wilderness search and rescue entails performing a wide-range of work in complex environments and large regions. Given the concerns inherent in large regions due to limited rescue distribution, unmanned aerial vehicle (UAV)-based frameworks are a promising platform for providing aerial imaging. In recent years, technological advances in areas such as micro-technology, sensors and navigation have influenced the various applications of UAVs. In this study, an all-in-one camera-based target detection and positioning system is developed and integrated into a fully autonomous fixed-wing UAV. The system presented in this paper is capable of on-board, real-time target identification, post-target identification and location and aerial image collection for further mapping applications. Its performance is examined using several simulated search and rescue missions, and the test results demonstrate its reliability and efficiency. PMID:27792156

A Camera-Based Target Detection and Positioning UAV System for Search and Rescue (SAR) Purposes.

PubMed

Sun, Jingxuan; Li, Boyang; Jiang, Yifan; Wen, Chih-Yung

2016-10-25

Wilderness search and rescue entails performing a wide-range of work in complex environments and large regions. Given the concerns inherent in large regions due to limited rescue distribution, unmanned aerial vehicle (UAV)-based frameworks are a promising platform for providing aerial imaging. In recent years, technological advances in areas such as micro-technology, sensors and navigation have influenced the various applications of UAVs. In this study, an all-in-one camera-based target detection and positioning system is developed and integrated into a fully autonomous fixed-wing UAV. The system presented in this paper is capable of on-board, real-time target identification, post-target identification and location and aerial image collection for further mapping applications. Its performance is examined using several simulated search and rescue missions, and the test results demonstrate its reliability and efficiency.

Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems.

PubMed

Vetrella, Amedeo Rodi; Fasano, Giancarmine; Accardo, Domenico; Moccia, Antonio

2016-12-17

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information.

Autonomous UAV-Based Mapping of Large-Scale Urban Firefights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Snarski, S; Scheibner, K F; Shaw, S

2006-03-09

This paper describes experimental results from a live-fire data collect designed to demonstrate the ability of IR and acoustic sensing systems to detect and map high-volume gunfire events from tactical UAVs. The data collect supports an exploratory study of the FightSight concept in which an autonomous UAV-based sensor exploitation and decision support capability is being proposed to provide dynamic situational awareness for large-scale battalion-level firefights in cluttered urban environments. FightSight integrates IR imagery, acoustic data, and 3D scene context data with prior time information in a multi-level, multi-step probabilistic-based fusion process to reliably locate and map the array of urbanmore » firing events and firepower movements and trends associated with the evolving urban battlefield situation. Described here are sensor results from live-fire experiments involving simultaneous firing of multiple sub/super-sonic weapons (2-AK47, 2-M16, 1 Beretta, 1 Mortar, 1 rocket) with high optical and acoustic clutter at ranges up to 400m. Sensor-shooter-target configurations and clutter were designed to simulate UAV sensing conditions for a high-intensity firefight in an urban environment. Sensor systems evaluated were an IR bullet tracking system by Lawrence Livermore National Laboratory (LLNL) and an acoustic gunshot detection system by Planning Systems, Inc. (PSI). The results demonstrate convincingly the ability for the LLNL and PSI sensor systems to accurately detect, separate, and localize multiple shooters and the associated shot directions during a high-intensity firefight (77 rounds in 5 sec) in a high acoustic and optical clutter environment with no false alarms. Preliminary fusion processing was also examined that demonstrated an ability to distinguish co-located shooters (shooter density), range to <0.5 m accuracy at 400m, and weapon type.« less

Using Distance Sensors to Perform Collision Avoidance Maneuvres on Uav Applications

NASA Astrophysics Data System (ADS)

Raimundo, A.; Peres, D.; Santos, N.; Sebastião, P.; Souto, N.

2017-08-01

The Unmanned Aerial Vehicles (UAV) and its applications are growing for both civilian and military purposes. The operability of an UAV proved that some tasks and operations can be done easily and at a good cost-efficiency ratio. Nowadays, an UAV can perform autonomous missions. It is very useful to certain UAV applications, such as meteorology, vigilance systems, agriculture, environment mapping and search and rescue operations. One of the biggest problems that an UAV faces is the possibility of collision with other objects in the flight area. To avoid this, an algorithm was developed and implemented in order to prevent UAV collision with other objects. "Sense and Avoid" algorithm was developed as a system for UAVs to avoid objects in collision course. This algorithm uses a Light Detection and Ranging (LiDAR), to detect objects facing the UAV in mid-flights. This light sensor is connected to an on-board hardware, Pixhawk's flight controller, which interfaces its communications with another hardware: Raspberry Pi. Communications between Ground Control Station and UAV are made via Wi-Fi or cellular third or fourth generation (3G/4G). Some tests were made in order to evaluate the "Sense and Avoid" algorithm's overall performance. These tests were done in two different environments: A 3D simulated environment and a real outdoor environment. Both modes worked successfully on a simulated 3D environment, and "Brake" mode on a real outdoor, proving its concepts.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

PubMed

Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

2017-09-29

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

Diverse Planning for UAV Control and Remote Sensing

PubMed Central

Tožička, Jan; Komenda, Antonín

2016-01-01

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs. PMID:28009831

Diverse Planning for UAV Control and Remote Sensing.

PubMed

Tožička, Jan; Komenda, Antonín

2016-12-21

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs.

An Application of UAV Attitude Estimation Using a Low-Cost Inertial Navigation System

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Quach, Cuong Chi; Vazquez, Sixto L.; Hogge, Edward F.; Hill, Boyd L.

2013-01-01

Unmanned Aerial Vehicles (UAV) are playing an increasing role in aviation. Various methods exist for the computation of UAV attitude based on low cost microelectromechanical systems (MEMS) and Global Positioning System (GPS) receivers. There has been a recent increase in UAV autonomy as sensors are becoming more compact and onboard processing power has increased significantly. Correct UAV attitude estimation will play a critical role in navigation and separation assurance as UAVs share airspace with civil air traffic. This paper describes attitude estimation derived by post-processing data from a small low cost Inertial Navigation System (INS) recorded during the flight of a subscale commercial off the shelf (COTS) UAV. Two discrete time attitude estimation schemes are presented here in detail. The first is an adaptation of the Kalman Filter to accommodate nonlinear systems, the Extended Kalman Filter (EKF). The EKF returns quaternion estimates of the UAV attitude based on MEMS gyro, magnetometer, accelerometer, and pitot tube inputs. The second scheme is the complementary filter which is a simpler algorithm that splits the sensor frequency spectrum based on noise characteristics. The necessity to correct both filters for gravity measurement errors during turning maneuvers is demonstrated. It is shown that the proposed algorithms may be used to estimate UAV attitude. The effects of vibration on sensor measurements are discussed. Heuristic tuning comments pertaining to sensor filtering and gain selection to achieve acceptable performance during flight are given. Comparisons of attitude estimation performance are made between the EKF and the complementary filter.

Enabling UAV Navigation with Sensor and Environmental Uncertainty in Cluttered and GPS-Denied Environments

PubMed Central

Vanegas, Fernando; Gonzalez, Felipe

2016-01-01

Unmanned Aerial Vehicles (UAV) can navigate with low risk in obstacle-free environments using ground control stations that plan a series of GPS waypoints as a path to follow. This GPS waypoint navigation does however become dangerous in environments where the GPS signal is faulty or is only present in some places and when the airspace is filled with obstacles. UAV navigation then becomes challenging because the UAV uses other sensors, which in turn generate uncertainty about its localisation and motion systems, especially if the UAV is a low cost platform. Additional uncertainty affects the mission when the UAV goal location is only partially known and can only be discovered by exploring and detecting a target. This navigation problem is established in this research as a Partially-Observable Markov Decision Process (POMDP), so as to produce a policy that maps a set of motion commands to belief states and observations. The policy is calculated and updated on-line while flying with a newly-developed system for UAV Uncertainty-Based Navigation (UBNAV), to navigate in cluttered and GPS-denied environments using observations and executing motion commands instead of waypoints. Experimental results in both simulation and real flight tests show that the UAV finds a path on-line to a region where it can explore and detect a target without colliding with obstacles. UBNAV provides a new method and an enabling technology for scientists to implement and test UAV navigation missions with uncertainty where targets must be detected using on-line POMDP in real flight scenarios. PMID:27171096

Enabling UAV Navigation with Sensor and Environmental Uncertainty in Cluttered and GPS-Denied Environments.

PubMed

Vanegas, Fernando; Gonzalez, Felipe

2016-05-10

Unmanned Aerial Vehicles (UAV) can navigate with low risk in obstacle-free environments using ground control stations that plan a series of GPS waypoints as a path to follow. This GPS waypoint navigation does however become dangerous in environments where the GPS signal is faulty or is only present in some places and when the airspace is filled with obstacles. UAV navigation then becomes challenging because the UAV uses other sensors, which in turn generate uncertainty about its localisation and motion systems, especially if the UAV is a low cost platform. Additional uncertainty affects the mission when the UAV goal location is only partially known and can only be discovered by exploring and detecting a target. This navigation problem is established in this research as a Partially-Observable Markov Decision Process (POMDP), so as to produce a policy that maps a set of motion commands to belief states and observations. The policy is calculated and updated on-line while flying with a newly-developed system for UAV Uncertainty-Based Navigation (UBNAV), to navigate in cluttered and GPS-denied environments using observations and executing motion commands instead of waypoints. Experimental results in both simulation and real flight tests show that the UAV finds a path on-line to a region where it can explore and detect a target without colliding with obstacles. UBNAV provides a new method and an enabling technology for scientists to implement and test UAV navigation missions with uncertainty where targets must be detected using on-line POMDP in real flight scenarios.

Comparison of Uncalibrated Rgbvi with Spectrometer-Based Ndvi Derived from Uav Sensing Systems on Field Scale

NASA Astrophysics Data System (ADS)

Bareth, G.; Bolten, A.; Gnyp, M. L.; Reusch, S.; Jasper, J.

2016-06-01

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers' field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

Three-dimensional estimates of tree canopies: Scaling from high-resolution UAV data to satellite observations

NASA Astrophysics Data System (ADS)

Sankey, T.; Donald, J.; McVay, J.

2015-12-01

High resolution remote sensing images and datasets are typically acquired at a large cost, which poses big a challenge for many scientists. Northern Arizona University recently acquired a custom-engineered, cutting-edge UAV and we can now generate our own images with the instrument. The UAV has a unique capability to carry a large payload including a hyperspectral sensor, which images the Earth surface in over 350 spectral bands at 5 cm resolution, and a lidar scanner, which images the land surface and vegetation in 3-dimensions. Both sensors represent the newest available technology with very high resolution, precision, and accuracy. Using the UAV sensors, we are monitoring the effects of regional forest restoration treatment efforts. Individual tree canopy width and height are measured in the field and via the UAV sensors. The high-resolution UAV images are then used to segment individual tree canopies and to derive 3-dimensional estimates. The UAV image-derived variables are then correlated to the field-based measurements and scaled to satellite-derived tree canopy measurements. The relationships between the field-based and UAV-derived estimates are then extrapolated to a larger area to scale the tree canopy dimensions and to estimate tree density within restored and control forest sites.

Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems

PubMed Central

Vetrella, Amedeo Rodi; Fasano, Giancarmine; Accardo, Domenico; Moccia, Antonio

2016-01-01

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information. PMID:27999318

Development and Validation of a UAV Based System for Air Pollution Measurements

PubMed Central

Villa, Tommaso Francesco; Salimi, Farhad; Morton, Kye; Morawska, Lidia; Gonzalez, Felipe

2016-01-01

Air quality data collection near pollution sources is difficult, particularly when sites are complex, have physical barriers, or are themselves moving. Small Unmanned Aerial Vehicles (UAVs) offer new approaches to air pollution and atmospheric studies. However, there are a number of critical design decisions which need to be made to enable representative data collection, in particular the location of the air sampler or air sensor intake. The aim of this research was to establish the best mounting point for four gas sensors and a Particle Number Concentration (PNC) monitor, onboard a hexacopter, so to develop a UAV system capable of measuring point source emissions. The research included two different tests: (1) evaluate the air flow behavior of a hexacopter, its downwash and upwash effect, by measuring air speed along three axes to determine the location where the sensors should be mounted; (2) evaluate the use of gas sensors for CO2, CO, NO2 and NO, and the PNC monitor (DISCmini) to assess the efficiency and performance of the UAV based system by measuring emissions from a diesel engine. The air speed behavior map produced by test 1 shows the best mounting point for the sensors to be alongside the UAV. This position is less affected by the propeller downwash effect. Test 2 results demonstrated that the UAV propellers cause a dispersion effect shown by the decrease of gas and PN concentration measured in real time. A Linear Regression model was used to estimate how the sensor position, relative to the UAV center, affects pollutant concentration measurements when the propellers are turned on. This research establishes guidelines on how to develop a UAV system to measure point source emissions. Such research should be undertaken before any UAV system is developed for real world data collection. PMID:28009820

Development and Validation of a UAV Based System for Air Pollution Measurements.

PubMed

Villa, Tommaso Francesco; Salimi, Farhad; Morton, Kye; Morawska, Lidia; Gonzalez, Felipe

2016-12-21

Air quality data collection near pollution sources is difficult, particularly when sites are complex, have physical barriers, or are themselves moving. Small Unmanned Aerial Vehicles (UAVs) offer new approaches to air pollution and atmospheric studies. However, there are a number of critical design decisions which need to be made to enable representative data collection, in particular the location of the air sampler or air sensor intake. The aim of this research was to establish the best mounting point for four gas sensors and a Particle Number Concentration (PNC) monitor, onboard a hexacopter, so to develop a UAV system capable of measuring point source emissions. The research included two different tests: (1) evaluate the air flow behavior of a hexacopter, its downwash and upwash effect, by measuring air speed along three axes to determine the location where the sensors should be mounted; (2) evaluate the use of gas sensors for CO₂, CO, NO₂ and NO, and the PNC monitor (DISCmini) to assess the efficiency and performance of the UAV based system by measuring emissions from a diesel engine. The air speed behavior map produced by test 1 shows the best mounting point for the sensors to be alongside the UAV. This position is less affected by the propeller downwash effect. Test 2 results demonstrated that the UAV propellers cause a dispersion effect shown by the decrease of gas and PN concentration measured in real time. A Linear Regression model was used to estimate how the sensor position, relative to the UAV center, affects pollutant concentration measurements when the propellers are turned on. This research establishes guidelines on how to develop a UAV system to measure point source emissions. Such research should be undertaken before any UAV system is developed for real world data collection.

Graph theoretic framework based cooperative control and estimation of multiple UAVs for target tracking

NASA Astrophysics Data System (ADS)

Ahmed, Mousumi

Designing the control technique for nonlinear dynamic systems is a significant challenge. Approaches to designing a nonlinear controller are studied and an extensive study on backstepping based technique is performed in this research with the purpose of tracking a moving target autonomously. Our main motivation is to explore the controller for cooperative and coordinating unmanned vehicles in a target tracking application. To start with, a general theoretical framework for target tracking is studied and a controller in three dimensional environment for a single UAV is designed. This research is primarily focused on finding a generalized method which can be applied to track almost any reference trajectory. The backstepping technique is employed to derive the controller for a simplified UAV kinematic model. This controller can compute three autopilot modes i.e. velocity, ground heading (or course angle), and flight path angle for tracking the unmanned vehicle. Numerical implementation is performed in MATLAB with the assumption of having perfect and full state information of the target to investigate the accuracy of the proposed controller. This controller is then frozen for the multi-vehicle problem. Distributed or decentralized cooperative control is discussed in the context of multi-agent systems. A consensus based cooperative control is studied; such consensus based control problem can be viewed from the algebraic graph theory concepts. The communication structure between the UAVs is represented by the dynamic graph where UAVs are represented by the nodes and the communication links are represented by the edges. The previously designed controller is augmented to account for the group to obtain consensus based on their communication. A theoretical development of the controller for the cooperative group of UAVs is presented and the simulation results for different communication topologies are shown. This research also investigates the cases where the communication topology switches to a different topology over particular time instants. Lyapunov analysis is performed to show stability in all cases. Another important aspect of this dissertation research is to implement the controller for the case, where perfect or full state information is not available. This necessitates the design of an estimator to estimate the system state. A nonlinear estimator, Extended Kalman Filter (EKF) is first developed for target tracking with a single UAV. The uncertainties involved with the measurement model and dynamics model are considered as zero mean Gaussian noises with some known covariances. The measurements of the full state of the target are not available and only the range, elevation, and azimuth angle are available from an onboard seeker sensor. A separate EKF is designed to estimate the UAV's own state where the state measurement is available through on-board sensors. The controller computes the three control commands based on the estimated states of target and its own states. Estimation based control laws is also implemented for colored noise measurement uncertainties, and the controller performance is shown with the simulation results. The estimation based control approach is then extended for the cooperative target tracking case. The target information is available to the network and a separate estimator is used to estimate target states. All of the UAVs in the network apply the same control law and the only difference is that each UAV updates the commands according to their connection. The simulation is performed for both cases of fixed and time varying communication topology. Monte Carlo simulation is also performed with different sample noises to investigate the performance of the estimator. The proposed technique is shown to be simple and robust to noisy environments.

Configuration and Specifications of AN Unmanned Aerial Vehicle for Precision Agriculture

NASA Astrophysics Data System (ADS)

Erena, M.; Montesinos, S.; Portillo, D.; Alvarez, J.; Marin, C.; Fernandez, L.; Henarejos, J. M.; Ruiz, L. A.

2016-06-01

Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated, generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for farmers.

Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.

Monitoring of bolted joints using piezoelectric active-sensing for aerospace applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, Gyuhae; Farrar, Charles R; Park, Chan - Yik

2010-01-01

This paper is a report of an initial investigation into tracking and monitoring the integrity of bolted joints using piezoelectric active-sensors. The target application of this study is a fitting lug assembly of unmanned aerial vehicles (UAVs), where a composite wing is mounted to a UAV fuselage. The SHM methods deployed in this study are impedance-based SHM techniques, time-series analysis, and high-frequency response functions measured by piezoelectric active-sensors. Different types of simulated damage are introduced into the structure, and the capability of each technique is examined and compared. Additional considerations encountered in this initial investigation are made to guide furthermore » thorough research required for the successful field deployment of this technology.« less

Fusing Unmanned Aerial Vehicle Imagery with High Resolution Hydrologic Modeling (Invited)

NASA Astrophysics Data System (ADS)

Vivoni, E. R.; Pierini, N.; Schreiner-McGraw, A.; Anderson, C.; Saripalli, S.; Rango, A.

2013-12-01

After decades of development and applications, high resolution hydrologic models are now common tools in research and increasingly used in practice. More recently, high resolution imagery from unmanned aerial vehicles (UAVs) that provide information on land surface properties have become available for civilian applications. Fusing the two approaches promises to significantly advance the state-of-the-art in terms of hydrologic modeling capabilities. This combination will also challenge assumptions on model processes, parameterizations and scale as land surface characteristics (~0.1 to 1 m) may now surpass traditional model resolutions (~10 to 100 m). Ultimately, predictions from high resolution hydrologic models need to be consistent with the observational data that can be collected from UAVs. This talk will describe our efforts to develop, utilize and test the impact of UAV-derived topographic and vegetation fields on the simulation of two small watersheds in the Sonoran and Chihuahuan Deserts at the Santa Rita Experimental Range (Green Valley, AZ) and the Jornada Experimental Range (Las Cruces, NM). High resolution digital terrain models, image orthomosaics and vegetation species classification were obtained from a fixed wing airplane and a rotary wing helicopter, and compared to coarser analyses and products, including Light Detection and Ranging (LiDAR). We focus the discussion on the relative improvements achieved with UAV-derived fields in terms of terrain-hydrologic-vegetation analyses and summer season simulations using the TIN-based Real-time Integrated Basin Simulator (tRIBS) model. Model simulations are evaluated at each site with respect to a high-resolution sensor network consisting of six rain gauges, forty soil moisture and temperature profiles, four channel runoff flumes, a cosmic-ray soil moisture sensor and an eddy covariance tower over multiple summer periods. We also discuss prospects for the fusion of high resolution models with novel observations from UAVs, including synthetic aperture radar and multispectral imagery.

Autonomous UAV-based mapping of large-scale urban firefights

NASA Astrophysics Data System (ADS)

Snarski, Stephen; Scheibner, Karl; Shaw, Scott; Roberts, Randy; LaRow, Andy; Breitfeller, Eric; Lupo, Jasper; Nielson, Darron; Judge, Bill; Forren, Jim

2006-05-01

This paper describes experimental results from a live-fire data collect designed to demonstrate the ability of IR and acoustic sensing systems to detect and map high-volume gunfire events from tactical UAVs. The data collect supports an exploratory study of the FightSight concept in which an autonomous UAV-based sensor exploitation and decision support capability is being proposed to provide dynamic situational awareness for large-scale battalion-level firefights in cluttered urban environments. FightSight integrates IR imagery, acoustic data, and 3D scene context data with prior time information in a multi-level, multi-step probabilistic-based fusion process to reliably locate and map the array of urban firing events and firepower movements and trends associated with the evolving urban battlefield situation. Described here are sensor results from live-fire experiments involving simultaneous firing of multiple sub/super-sonic weapons (2-AK47, 2-M16, 1 Beretta, 1 Mortar, 1 rocket) with high optical and acoustic clutter at ranges up to 400m. Sensor-shooter-target configurations and clutter were designed to simulate UAV sensing conditions for a high-intensity firefight in an urban environment. Sensor systems evaluated were an IR bullet tracking system by Lawrence Livermore National Laboratory (LLNL) and an acoustic gunshot detection system by Planning Systems, Inc. (PSI). The results demonstrate convincingly the ability for the LLNL and PSI sensor systems to accurately detect, separate, and localize multiple shooters and the associated shot directions during a high-intensity firefight (77 rounds in 5 sec) in a high acoustic and optical clutter environment with very low false alarms. Preliminary fusion processing was also examined that demonstrated an ability to distinguish co-located shooters (shooter density), range to <0.5 m accuracy at 400m, and weapon type. The combined results of the high-intensity firefight data collect and a detailed systems study demonstrate the readiness of the FightSight concept for full system development and integration.

Performance Characteristic Mems-Based IMUs for UAVs Navigation

NASA Astrophysics Data System (ADS)

Mohamed, H. A.; Hansen, J. M.; Elhabiby, M. M.; El-Sheimy, N.; Sesay, A. B.

2015-08-01

Accurate 3D reconstruction has become essential for non-traditional mapping applications such as urban planning, mining industry, environmental monitoring, navigation, surveillance, pipeline inspection, infrastructure monitoring, landslide hazard analysis, indoor localization, and military simulation. The needs of these applications cannot be satisfied by traditional mapping, which is based on dedicated data acquisition systems designed for mapping purposes. Recent advances in hardware and software development have made it possible to conduct accurate 3D mapping without using costly and high-end data acquisition systems. Low-cost digital cameras, laser scanners, and navigation systems can provide accurate mapping if they are properly integrated at the hardware and software levels. Unmanned Aerial Vehicles (UAVs) are emerging as a mobile mapping platform that can provide additional economical and practical advantages. However, such economical and practical requirements need navigation systems that can provide uninterrupted navigation solution. Hence, testing the performance characteristics of Micro-Electro-Mechanical Systems (MEMS) or low cost navigation sensors for various UAV applications is important research. This work focuses on studying the performance characteristics under different manoeuvres using inertial measurements integrated with single point positioning, Real-Time-Kinematic (RTK), and additional navigational aiding sensors. Furthermore, the performance of the inertial sensors is tested during Global Positioning System (GPS) signal outage.

Analyzing the Effects of UAV Mobility Patterns on Data Collection in Wireless Sensor Networks.

PubMed

Rashed, Sarmad; Soyturk, Mujdat

2017-02-20

Sensor nodes in a Wireless Sensor Network (WSN) can be dispersed over a remote sensing area (e.g., the regions that are hardly accessed by human beings). In such kinds of networks, datacollectionbecomesoneofthemajorissues. Getting connected to each sensor node and retrieving the information in time introduces new challenges. Mobile sink usage-especially Unmanned Aerial Vehicles (UAVs)-is the most convenient approach to covering the area and accessing each sensor node in such a large-scale WSN. However, the operation of the UAV depends on some parameters, such as endurance time, altitude, speed, radio type in use, and the path. In this paper, we explore various UAV mobility patterns that follow different paths to sweep the operation area in order to seek the best area coverage with the maximum number of covered nodes in the least amount of time needed by the mobile sink. We also introduce a new metric to formulate the tradeoff between maximizing the covered nodes and minimizing the operation time when choosing the appropriate mobility pattern. A realistic simulation environment is used in order to compare and evaluate the performance of the system. We present the performance results for the explored UAV mobility patterns. The results are very useful to present the tradeoff between maximizing the covered nodes and minimizing the operation time to choose the appropriate mobility pattern.

Analyzing the Effects of UAV Mobility Patterns on Data Collection in Wireless Sensor Networks

PubMed Central

Rashed, Sarmad; Soyturk, Mujdat

2017-01-01

Sensor nodes in a Wireless Sensor Network (WSN) can be dispersed over a remote sensing area (e.g., the regions that are hardly accessed by human beings). In such kinds of networks, data collection becomes one of the major issues. Getting connected to each sensor node and retrieving the information in time introduces new challenges. Mobile sink usage—especially Unmanned Aerial Vehicles (UAVs)—is the most convenient approach to covering the area and accessing each sensor node in such a large-scale WSN. However, the operation of the UAV depends on some parameters, such as endurance time, altitude, speed, radio type in use, and the path. In this paper, we explore various UAV mobility patterns that follow different paths to sweep the operation area in order to seek the best area coverage with the maximum number of covered nodes in the least amount of time needed by the mobile sink. We also introduce a new metric to formulate the tradeoff between maximizing the covered nodes and minimizing the operation time when choosing the appropriate mobility pattern. A realistic simulation environment is used in order to compare and evaluate the performance of the system. We present the performance results for the explored UAV mobility patterns. The results are very useful to present the tradeoff between maximizing the covered nodes and minimizing the operation time to choose the appropriate mobility pattern. PMID:28230727

Analysis of the Assignment Scheduling Capability for Unmanned Aerial Vehicles (ASC-U) Simulation Tool

DTIC Science & Technology

2006-06-01

dynamic programming approach known as a “rolling horizon” approach. This method accounts for state transitions within the simulation rather than modeling ... model is based on the framework developed for Dynamic Allocation of Fires and Sensors used to evaluate factors associated with networking assets in the...of UAVs required by all types of maneuver and support brigades. (Witsken, 2004) The Modeling , Virtual Environments, and Simulations Institute

Autonomous Navigation of Small Uavs Based on Vehicle Dynamic Model

NASA Astrophysics Data System (ADS)

Khaghani, M.; Skaloud, J.

2016-03-01

This paper presents a novel approach to autonomous navigation for small UAVs, in which the vehicle dynamic model (VDM) serves as the main process model within the navigation filter. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs on-board. This is achieved with no extra sensor added to the conventional INS/GNSS setup. This improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The VDM is also fed with the control input to the UAV, which is available within the control/autopilot system. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS navigation system during the autonomous phase, when satellite signals are not available due to physical obstruction or electromagnetic interference for example. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to inertial coasting. It means that during such scenario, the position-velocity-attitude (PVA) determination is sufficiently accurate to navigate the UAV to a home position without any signal that depends on vehicle environment.

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring

PubMed Central

Ammad Uddin, Mohammad; Mansour, Ali; Le Jeune, Denis; Ayaz, Mohammad; Aggoune, el-Hadi M.

2018-01-01

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use. PMID:29439496

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring.

PubMed

Uddin, Mohammad Ammad; Mansour, Ali; Jeune, Denis Le; Ayaz, Mohammad; Aggoune, El-Hadi M

2018-02-11

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.

Scalable autonomous operations of unmanned assets

NASA Astrophysics Data System (ADS)

Jung, Sunghun

Although there have been great theoretical advances in the region of Unmanned Aerial Vehicle (UAV) autonomy, applications of those theories into real world are still hesitated due to unexpected disturbances. Most of UAVs which are currently used are mainly, strictly speaking, Remotely Piloted Vehicles (RPA) since most works related with the flight control, sensor data analysis, and decision makings are done by human operators. To increase the degree of autonomy, many researches are focused on developing Unmanned Autonomous Aerial Vehicle (UAAV) which can takeoff, fly to the interested area by avoiding unexpected obstacles, perform various missions with decision makings, come back to the base station, and land on by itself without any human operators. To improve the performance of UAVs, the accuracies of position and orientation sensors are enhanced by integrating a Unmanned Ground Vehicle (UGV) or a solar compass to a UAV; Position sensor accuracy of a GPS sensor on a UAV is improved by referencing the position of a UGV which is calculated by using three GPS sensors and Weighted Centroid Localization (WCL) method; Orientation sensor accuracy is improved as well by using Three Pixel Theorem (TPT) and integrating a solar compass which composed of nine light sensors to a magnetic compass. Also, improved health management of a UAV is fulfilled by developing a wireless autonomous charging station which uses four pairs of transmitter and receiver magnetic loops with four robotic arms. For the software aspect, I also analyze the error propagation of the proposed mission planning hierarchy to achieve the safest size of the buffer zone. In addition, among seven future research areas regarding UAV, this paper mainly focuses on developing algorithms of path planning, trajectory generation, and cooperative tactics for the operations of multiple UAVs using GA based multiple Traveling Salesman Problem (mTSP) which is solved by dividing into m number of Traveling Salesman Problems (TSP) using two region division methods such as Uniform Region Division (URD) and K-means Voronoi Region Division (KVRD). The topic of the maximum fuel efficiency is also dealt to ensure the minimum amount fuel consumption to perform surveillance on a given region using multiple UAVs. Last but not least, I present an application example of cattle roundup with two UAVs and two animals using the feedback linearization controller.

Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

NASA Astrophysics Data System (ADS)

Tlhabano, Lorato

2018-05-01

Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques' and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryan Hruska

Currently, small Unmanned Aerial Vehicles (UAVs) are primarily used for capturing and down-linking real-time video. To date, their role as a low-cost airborne platform for capturing high-resolution, georeferenced still imagery has not been fully utilized. On-going work within the Unmanned Vehicle Systems Program at the Idaho National Laboratory (INL) is attempting to exploit this small UAV-acquired, still imagery potential. Initially, a UAV-based still imagery work flow model was developed that includes initial UAV mission planning, sensor selection, UAV/sensor integration, and imagery collection, processing, and analysis. Components to support each stage of the work flow are also being developed. Critical tomore » use of acquired still imagery is the ability to detect changes between images of the same area over time. To enhance the analysts’ change detection ability, a UAV-specific, GIS-based change detection system called SADI or System for Analyzing Differences in Imagery is under development. This paper will discuss the associated challenges and approaches to collecting still imagery with small UAVs. Additionally, specific components of the developed work flow system will be described and graphically illustrated using varied examples of small UAV-acquired still imagery.« less

A new stratospheric sounding platform based on unmanned aerial vehicle (UAV) droppable from meteorological balloon

NASA Astrophysics Data System (ADS)

Efremov, Denis; Khaykin, Sergey; Lykov, Alexey; Berezhko, Yaroslav; Lunin, Aleksey

High-resolution measurements of climate-relevant trace gases and aerosols in the upper troposphere and stratosphere (UTS) have been and remain technically challenging. The high cost of measurements onboard airborne platforms or heavy stratospheric balloons results in a lack of accurate information on vertical distribution of atmospheric constituents. Whereas light-weight instruments carried by meteorological balloons are becoming progressively available, their usage is constrained by the cost of the equipment or the recovery operations. The evolving need in cost-efficient observations for UTS process studies has led to development of small airborne platforms - unmanned aerial vehicles (UAV), capable of carrying small sensors for in-situ measurements. We present a new UAV-based stratospheric sounding platform capable of carrying scientific payload of up to 2 kg. The airborne platform comprises of a latex meteorological balloon and detachable flying wing type UAV with internal measurement controller. The UAV is launched on a balloon to stratospheric altitudes up to 20 km, where it can be automatically released by autopilot or by a remote command sent from the ground control. Having been released from the balloon the UAV glides down and returns to the launch position. Autopilot using 3-axis gyro, accelerometer, barometer, compas and GPS navigation provides flight stabilization and optimal way back trajectory. Backup manual control is provided for emergencies. During the flight the onboard measurement controller stores the data into internal memory and transmits current flight parameters to the ground station via telemetry. Precise operation of the flight control systems ensures safe landing at the launch point. A series of field tests of the detachable stratospheric UAV has been conducted. The scientific payload included the following instruments involved in different flights: a) stratospheric Lyman-alpha hygrometer (FLASH); b) backscatter sonde; c) electrochemical ozone sonde; d) optical CO2 sensor; e) radioactivity sensor; f) solar radiation sensor. In addition, each payload included temperature sensor, barometric sensor and a GPS receiver. Design features of measurement systems onboard UAV and flight results are presented. Possible applications for atmospheric studies and validation of remote ground-based and space-borne observations is discussed.

Bio-inspired UAV routing, source localization, and acoustic signature classification for persistent surveillance

NASA Astrophysics Data System (ADS)

Burman, Jerry; Hespanha, Joao; Madhow, Upamanyu; Pham, Tien

2011-06-01

A team consisting of Teledyne Scientific Company, the University of California at Santa Barbara and the Army Research Laboratory* is developing technologies in support of automated data exfiltration from heterogeneous battlefield sensor networks to enhance situational awareness for dismounts and command echelons. Unmanned aerial vehicles (UAV) provide an effective means to autonomously collect data from a sparse network of unattended ground sensors (UGSs) that cannot communicate with each other. UAVs are used to reduce the system reaction time by generating autonomous collection routes that are data-driven. Bio-inspired techniques for search provide a novel strategy to detect, capture and fuse data. A fast and accurate method has been developed to localize an event by fusing data from a sparse number of UGSs. This technique uses a bio-inspired algorithm based on chemotaxis or the motion of bacteria seeking nutrients in their environment. A unique acoustic event classification algorithm was also developed based on using swarm optimization. Additional studies addressed the problem of routing multiple UAVs, optimally placing sensors in the field and locating the source of gunfire at helicopters. A field test was conducted in November of 2009 at Camp Roberts, CA. The field test results showed that a system controlled by bio-inspired software algorithms can autonomously detect and locate the source of an acoustic event with very high accuracy and visually verify the event. In nine independent test runs of a UAV, the system autonomously located the position of an explosion nine times with an average accuracy of 3 meters. The time required to perform source localization using the UAV was on the order of a few minutes based on UAV flight times. In June 2011, additional field tests of the system will be performed and will include multiple acoustic events, optimal sensor placement based on acoustic phenomenology and the use of the International Technology Alliance (ITA) Sensor Network Fabric (IBM).

Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System.

PubMed

Hinas, Ajmal; Roberts, Jonathan M; Gonzalez, Felipe

2017-12-17

In this paper, a system that uses an algorithm for target detection and navigation and a multirotor Unmanned Aerial Vehicle (UAV) for finding a ground target and inspecting it closely is presented. The system can also be used for accurate and safe delivery of payloads or spot spraying applications in site-specific crop management. A downward-looking camera attached to a multirotor is used to find the target on the ground. The UAV descends to the target and hovers above the target for a few seconds to inspect the target. A high-level decision algorithm based on an OODA (observe, orient, decide, and act) loop was developed as a solution to address the problem. Navigation of the UAV was achieved by continuously sending local position messages to the autopilot via Mavros. The proposed system performed hovering above the target in three different stages: locate, descend, and hover. The system was tested in multiple trials, in simulations and outdoor tests, from heights of 10 m to 40 m. Results show that the system is highly reliable and robust to sensor errors, drift, and external disturbance.

Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors.

PubMed

Heredia, Guillermo; Caballero, Fernando; Maza, Iván; Merino, Luis; Viguria, Antidio; Ollero, Aníbal

2009-01-01

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

A mathematical analysis of the Janus combat simulation weather effects models and sensitivity analysis of sky-to-ground brightness ratio on target detection

NASA Astrophysics Data System (ADS)

Shorts, Vincient F.

1994-09-01

The Janus combat simulation offers the user a wide variety of weather effects options to employ during the execution of any simulation run, which can directly influence detection of opposing forces. Realistic weather effects are required if the simulation is to accurately reproduce 'real world' results. This thesis examines the mathematics of the Janus weather effects models. A weather effect option in Janus is the sky-to-ground brightness ratio (SGR). SGR affects an optical sensor's ability to detect targets. It is a measure of the sun angle in relation to the horizon. A review of the derivation of SGR is performed and an analysis of SGR's affect on the number of optical detections and detection ranges is performed using an unmanned aerial vehicle (UAV) search scenario. For comparison, the UAV's are equipped with a combination of optical and thermal sensors.

Health monitoring of unmanned aerial vehicle based on optical fiber sensor array

NASA Astrophysics Data System (ADS)

Luo, Yuxiang; Shen, Jingshi; Shao, Fei; Guo, Chunhui; Yang, Ning; Zhang, Jiande

2017-10-01

The unmanned aerial vehicle (UAV) in flight needs to face the complicated environment, especially to withstand harsh weather conditions, such as the temperature and pressure. Compared with conventional sensors, fiber Bragg grating (FBG) sensor has the advantages of small size, light weight, high reliability, high precision, anti-electromagnetic interference, long lift-span, moistureproof and good resistance to causticity. It's easy to be embedded in composite structural components of UAVs. In the paper, over 1000 FBG sensors distribute regularly on a wide range of UAVs body, combining wavelength division multiplexing (WDM), time division multiplexing (TDM) and multichannel parallel architecture. WDM has the advantage of high spatial resolution. TDM has the advantage of large capacity and wide range. It is worthful to constitute a sensor network by different technologies. For the signal demodulation of FBG sensor array, WDM works by means of wavelength scanning light sources and F-P etalon. TDM adopts the technology of optical time-domain reflectometry. In order to demodulate efficiently, the most proper sensor multiplex number with some reflectivity is given by the curves fitting. Due to the regular array arrangement of FBG sensors on the UAVs, we can acquire the health state of UAVs in the form of 3D visualization. It is helpful to master the information of health status rapidly and give a real-time health evaluation.

Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller.

PubMed

Lopez-Franco, Carlos; Gomez-Avila, Javier; Alanis, Alma Y; Arana-Daniel, Nancy; Villaseñor, Carlos

2017-08-12

In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results.

Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller

PubMed Central

Lopez-Franco, Carlos; Alanis, Alma Y.; Arana-Daniel, Nancy; Villaseñor, Carlos

2017-01-01

In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results. PMID:28805689

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

PubMed Central

Elzoghby, Mostafa; Li, Fu; Arafa, Ibrahim. I.; Arif, Usman

2017-01-01

Information fusion from multiple sensors ensures the accuracy and robustness of a navigation system, especially in the absence of global positioning system (GPS) data which gets degraded in many cases. A way to deal with multi-mode estimation for a small fixed wing unmanned aerial vehicle (UAV) localization framework is proposed, which depends on utilizing a Luenberger observer-based linear matrix inequality (LMI) approach. The proposed estimation technique relies on the interaction between multiple measurement modes and a continuous observer. The state estimation is performed in a switching environment between multiple active sensors to exploit the available information as much as possible, especially in GPS-denied environments. Luenberger observer-based projection is implemented as a continuous observer to optimize the estimation performance. The observer gain might be chosen by solving a Lyapunov equation by means of a LMI algorithm. Convergence is achieved by utilizing the linear matrix inequality (LMI), based on Lyapunov stability which keeps the dynamic estimation error bounded by selecting the observer gain matrix (L). Simulation results are presented for a small UAV fixed wing localization problem. The results obtained using the proposed approach are compared with a single mode Extended Kalman Filter (EKF). Simulation results are presented to demonstrate the viability of the proposed strategy. PMID:28420214

Development of Hardware-in-the-Loop Simulation Based on Gazebo and Pixhawk for Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Nguyen, Khoa Dang; Ha, Cheolkeun

2018-04-01

Hardware-in-the-loop simulation (HILS) is well known as an effective approach in the design of unmanned aerial vehicles (UAV) systems, enabling engineers to test the control algorithm on a hardware board with a UAV model on the software. Performance of HILS is determined by performances of the control algorithm, the developed model, and the signal transfer between the hardware and software. The result of HILS is degraded if any signal could not be transferred to the correct destination. Therefore, this paper aims to develop a middleware software to secure communications in HILS system for testing the operation of a quad-rotor UAV. In our HILS, the Gazebo software is used to generate a nonlinear six-degrees-of-freedom (6DOF) model, sensor model, and 3D visualization for the quad-rotor UAV. Meanwhile, the flight control algorithm is designed and implemented on the Pixhawk hardware. New middleware software, referred to as the control application software (CAS), is proposed to ensure the connection and data transfer between Gazebo and Pixhawk using the multithread structure in Qt Creator. The CAS provides a graphical user interface (GUI), allowing the user to monitor the status of packet transfer, and perform the flight control commands and the real-time tuning parameters for the quad-rotor UAV. Numerical implementations have been performed to prove the effectiveness of the middleware software CAS suggested in this paper.

Improving geolocation and spatial accuracies with the modular integrated avionics group (MIAG)

NASA Astrophysics Data System (ADS)

Johnson, Einar; Souter, Keith

1996-05-01

The modular integrated avionics group (MIAG) is a single unit approach to combining position, inertial and baro-altitude/air data sensors to provide optimized navigation, guidance and control performance. Lear Astronics Corporation is currently working within the navigation community to upgrade existing MIAG performance with precise GPS positioning mechanization tightly integrated with inertial, baro and other sensors. Among the immediate benefits are the following: (1) accurate target location in dynamic conditions; (2) autonomous launch and recovery using airborne avionics only; (3) precise flight path guidance; and (4) improved aircraft and payload stability information. This paper will focus on the impact of using the MIAG with its multimode navigation accuracies on the UAV targeting mission. Gimbaled electro-optical sensors mounted on a UAV can be used to determine ground coordinates of a target at the center of the field of view by a series of vector rotation and scaling computations. The accuracy of the computed target coordinates is dependent on knowing the UAV position and the UAV-to-target offset computation. Astronics performed a series of simulations to evaluate the effects that the improved angular and position data available from the MIAG have on target coordinate accuracy.

An Optical Fiber Sensor and Its Application in UAVs for Current Measurements

PubMed Central

Delgado, Felipe S.; Carvalho, João P.; Coelho, Thiago V. N.; Dos Santos, Alexandre B.

2016-01-01

In this paper, we propose and experimentally investigate an optical sensor based on a novel combination of a long-period fiber grating (LPFG) with a permanent magnet to measure electrical current in unmanned aerial vehicles (UAVs). The proposed device uses a neodymium magnet attached to the grating structure, which suffers from an electromagnetic force produced when the current flows in the wire of the UAV engine. Therefore, it causes deformation on the sensor and thus, different shifts occur in the resonant bands of the transmission spectrum of the LPFG. Finally, the results show that it is possible to monitor electrical current throughout the entire operating range of the UAV engine from 0 A to 10 A in an effective and practical way with good linearity, reliability and response time, which are desirable characteristics in electrical current sensing. PMID:27801798

Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors - NASA Report on Dominion Virginia Power Flights of November 2016

NASA Technical Reports Server (NTRS)

Moore, Andrew J.; Schubert, Matthew; Nicholas Rymer

2017-01-01

The report details test and measurement flights to demonstrate autonomous UAV inspection of high voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensor logging software, measured ultraviolet emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace.

Dynamic Data-Driven UAV Network for Plume Characterization

DTIC Science & Technology

2016-05-23

data collection where simulations and measurements become a symbiotic feedback control system where simulations inform measurement locations and the...and measurements become a symbiotic feedback control system where simulations inform measurement locations and the measured data augments simulations...data analysis techniques with mobile sensor data collection where simulations and measurements become a symbiotic feedback control system where

Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning.

PubMed

Lin, Lanny; Goodrich, Michael A

2014-12-01

During unmanned aerial vehicle (UAV) search missions, efficient use of UAV flight time requires flight paths that maximize the probability of finding the desired subject. The probability of detecting the desired subject based on UAV sensor information can vary in different search areas due to environment elements like varying vegetation density or lighting conditions, making it likely that the UAV can only partially detect the subject. This adds another dimension of complexity to the already difficult (NP-Hard) problem of finding an optimal search path. We present a new class of algorithms that account for partial detection in the form of a task difficulty map and produce paths that approximate the payoff of optimal solutions. The algorithms use the mode goodness ratio heuristic that uses a Gaussian mixture model to prioritize search subregions. The algorithms search for effective paths through the parameter space at different levels of resolution. We compare the performance of the new algorithms against two published algorithms (Bourgault's algorithm and LHC-GW-CONV algorithm) in simulated searches with three real search and rescue scenarios, and show that the new algorithms outperform existing algorithms significantly and can yield efficient paths that yield payoffs near the optimal.

Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle.

PubMed

Kim, In-Ho; Jeon, Haemin; Baek, Seung-Chan; Hong, Won-Hwa; Jung, Hyung-Jo

2018-06-08

Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer learning is applied. As a result, a new network for the 384 collected crack images of 256 × 256 pixel resolution is generated from the pre-trained network. A field test is conducted to verify the proposed approach, and the experimental results proved that the UAV-based bridge inspection is effective at identifying and quantifying the cracks on the structures.

Multi-UAV Collaborative Sensor Management for UAV Team Survivability

DTIC Science & Technology

2006-08-01

Multi-UAV Collaborative Sensor Management for UAV Team Survivability Craig Stoneking, Phil DiBona , and Adria Hughes Lockheed Martin Advanced...Command, Aviation Applied Technology Directorate. REFERENCES [1] DiBona , P., Belov, N., Pawlowski, A. (2006). “Plan-Driven Fusion: Shaping the

Applications of UAVs in row-crop agriculture: advantages and limitations

NASA Astrophysics Data System (ADS)

Basso, B.; Putnam, G.; Price, R.; Zhang, J.

2016-12-01

The application of Unmanned Aerial Vehicles (UAV) to monitor agricultural fields has increased over the last few years due to advances in the technology, sensors, post-processing software for image analysis, along with more favorable regulations that allowed UAVs to be flown for commercial use. UAV have several capabilities depending on the type of sensors that are mounted onboard. The most widely used application remains crop scouting to identify areas within fields where the crops underperform for various reasons (nutritional status and water stress, presence of weeds, poor stands etc). In this talk, we present the preliminary results of UAVs field based research to better understand spatial and temporal variability of crop yield. Their advantage in providing timely information is critical, but adaptive management requires a system approach to account for the interactions occurring between genetics, environment and management.

Efficiency calibration and minimum detectable activity concentration of a real-time UAV airborne sensor system with two gamma spectrometers.

PubMed

Tang, Xiao-Bin; Meng, Jia; Wang, Peng; Cao, Ye; Huang, Xi; Wen, Liang-Sheng; Chen, Da

2016-04-01

A small-sized UAV (NH-UAV) airborne system with two gamma spectrometers (LaBr3 detector and HPGe detector) was developed to monitor activity concentration in serious nuclear accidents, such as the Fukushima nuclear accident. The efficiency calibration and determination of minimum detectable activity concentration (MDAC) of the specific system were studied by MC simulations at different flight altitudes, different horizontal distances from the detection position to the source term center and different source term sizes. Both air and ground radiation were considered in the models. The results obtained may provide instructive suggestions for in-situ radioactivity measurements of NH-UAV. Copyright © 2016 Elsevier Ltd. All rights reserved.

Path planning and Ground Control Station simulator for UAV

NASA Astrophysics Data System (ADS)

Ajami, A.; Balmat, J.; Gauthier, J.-P.; Maillot, T.

In this paper we present a Universal and Interoperable Ground Control Station (UIGCS) simulator for fixed and rotary wing Unmanned Aerial Vehicles (UAVs), and all types of payloads. One of the major constraints is to operate and manage multiple legacy and future UAVs, taking into account the compliance with NATO Combined/Joint Services Operational Environment (STANAG 4586). Another purpose of the station is to assign the UAV a certain degree of autonomy, via autonomous planification/replanification strategies. The paper is organized as follows. In Section 2, we describe the non-linear models of the fixed and rotary wing UAVs that we use in the simulator. In Section 3, we describe the simulator architecture, which is based upon interacting modules programmed independently. This simulator is linked with an open source flight simulator, to simulate the video flow and the moving target in 3D. To conclude this part, we tackle briefly the problem of the Matlab/Simulink software connection (used to model the UAV's dynamic) with the simulation of the virtual environment. Section 5 deals with the control module of a flight path of the UAV. The control system is divided into four distinct hierarchical layers: flight path, navigation controller, autopilot and flight control surfaces controller. In the Section 6, we focus on the trajectory planification/replanification question for fixed wing UAV. Indeed, one of the goals of this work is to increase the autonomy of the UAV. We propose two types of algorithms, based upon 1) the methods of the tangent and 2) an original Lyapunov-type method. These algorithms allow either to join a fixed pattern or to track a moving target. Finally, Section 7 presents simulation results obtained on our simulator, concerning a rather complicated scenario of mission.

Distributed control systems with incomplete and uncertain information

NASA Astrophysics Data System (ADS)

Tang, Jingpeng

Scientific and engineering advances in wireless communication, sensors, propulsion, and other areas are rapidly making it possible to develop unmanned air vehicles (UAVs) with sophisticated capabilities. UAVs have come to the forefront as tools for airborne reconnaissance to search for, detect, and destroy enemy targets in relatively complex environments. They potentially reduce risk to human life, are cost effective, and are superior to manned aircraft for certain types of missions. It is desirable for UAVs to have a high level of intelligent autonomy to carry out mission tasks with little external supervision and control. This raises important issues involving tradeoffs between centralized control and the associated potential to optimize mission plans, and decentralized control with great robustness and the potential to adapt to changing conditions. UAV capabilities have been extended several ways through armament (e.g., Hellfire missiles on Predator UAVs), increased endurance and altitude (e.g., Global Hawk), and greater autonomy. Some known barriers to full-scale implementation of UAVs are increased communication and control requirements as well as increased platform and system complexity. One of the key problems is how UAV systems can handle incomplete and uncertain information in dynamic environments. Especially when the system is composed of heterogeneous and distributed UAVs, the overall system complexity is increased under such conditions. Presented through the use of published papers, this dissertation lays the groundwork for the study of methodologies for handling incomplete and uncertain information for distributed control systems. An agent-based simulation framework is built to investigate mathematical approaches (optimization) and emergent intelligence approaches. The first paper provides a mathematical approach for systems of UAVs to handle incomplete and uncertain information. The second paper describes an emergent intelligence approach for UAVs, again in handling incomplete and uncertain information. The third paper combines mathematical and emergent intelligence approaches.

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System.

PubMed

Ni, Jun; Yao, Lili; Zhang, Jingchao; Cao, Weixing; Zhu, Yan; Tai, Xiuxiang

2017-03-03

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R²) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

PubMed Central

Ni, Jun; Yao, Lili; Zhang, Jingchao; Cao, Weixing; Zhu, Yan; Tai, Xiuxiang

2017-01-01

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively. PMID:28273815

Path Calculation and Packet Translation for UAV Surveillance in Support of Wireless Sensor Networks

DTIC Science & Technology

2006-09-01

AND PACKET TRANSLATION FOR UAV SURVEILLANCE IN SUPPORT OF WIRELESS SENSOR NETWORKS by Stephen Schall September 2006 Thesis Advisor...Calculation and Packet Translation for UAV Surveillance in Support of Wireless Sensor Networks 6. AUTHOR(S) Stephen Schall 5. FUNDING NUMBERS 7...200 words) Wireless Sensor Networks (WSNs) are a relatively new technology with many potential applications, including military and

Multiple Event Localization in a Sparse Acoustic Sensor Network Using UAVs as Data Mules

DTIC Science & Technology

2012-12-01

necessarily reflect the position or the policy of the Government , and no official endorsement should be inferred. Path Acoustic Sensor Communication Footprint...a Microhard radio to forward the ToAs to the mule-UAV. Two Procerus Unicorn UAVs were used with different payloads. The imaging- UAV was equipped

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping.

PubMed

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-08-12

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

PubMed Central

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-01-01

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights. PMID:26274960

Textured digital elevation model formation from low-cost UAV LADAR/digital image data

NASA Astrophysics Data System (ADS)

Bybee, Taylor C.; Budge, Scott E.

2015-05-01

Textured digital elevation models (TDEMs) have valuable use in precision agriculture, situational awareness, and disaster response. However, scientific-quality models are expensive to obtain using conventional aircraft-based methods. The cost of creating an accurate textured terrain model can be reduced by using a low-cost (<$20k) UAV system fitted with ladar and electro-optical (EO) sensors. A texel camera fuses calibrated ladar and EO data upon simultaneous capture, creating a texel image. This eliminates the problem of fusing the data in a post-processing step and enables both 2D- and 3D-image registration techniques to be used. This paper describes formation of TDEMs using simulated data from a small UAV gathering swaths of texel images of the terrain below. Being a low-cost UAV, only a coarse knowledge of position and attitude is known, and thus both 2D- and 3D-image registration techniques must be used to register adjacent swaths of texel imagery to create a TDEM. The process of creating an aggregate texel image (a TDEM) from many smaller texel image swaths is described. The algorithm is seeded with the rough estimate of position and attitude of each capture. Details such as the required amount of texel image overlap, registration models, simulated flight patterns (level and turbulent), and texture image formation are presented. In addition, examples of such TDEMs are shown and analyzed for accuracy.

Distributed Actuation and Sensing on an Uninhabited Aerial Vehicle

NASA Technical Reports Server (NTRS)

Barnwell, William Garrard

2003-01-01

An array of effectors and sensors has been designed, tested and implemented on a Blended Wing Body Uninhabited Aerial Vehicle (UAV). The UAV is modified to serve as a flying, controls research, testbed. This effector/sensor array provides for the dynamic vehicle testing of controller designs and the study of decentralized control techniques. Each wing of the UAV is equipped with 12 distributed effectors that comprise a segmented array of independently actuated, contoured control surfaces. A single pressure sensor is installed near the base of each effector to provide a measure of deflections of the effectors. The UAV wings were tested in the North Carolina State University Subsonic Wind Tunnel and the pressure distribution that result from the deflections of the effectors are characterized. The results of the experiments are used to develop a simple, but accurate, prediction method, such that for any arrangement of the effector array the corresponding pressure distribution can be determined. Numerical analysis using the panel code CMARC verifies this prediction method.

Lightweight UAV with on-board photogrammetry and single-frequency GPS positioning for metrology applications

NASA Astrophysics Data System (ADS)

Daakir, M.; Pierrot-Deseilligny, M.; Bosser, P.; Pichard, F.; Thom, C.; Rabot, Y.; Martin, O.

2017-05-01

This article presents a coupled system consisting of a single-frequency GPS receiver and a light photogrammetric quality camera embedded in an Unmanned Aerial Vehicle (UAV). The aim is to produce high quality data that can be used in metrology applications. The issue of Integrated Sensor Orientation (ISO) of camera poses using only GPS measurements is presented and discussed. The accuracy reached by our system based on sensors developed at the French Mapping Agency (IGN) Opto-Electronics, Instrumentation and Metrology Laboratory (LOEMI) is qualified. These sensors are specially designed for close-range aerial image acquisition with a UAV. Lever-arm calibration and time synchronization are explained and performed to reach maximum accuracy. All processing steps are detailed from data acquisition to quality control of final products. We show that an accuracy of a few centimeters can be reached with this system which uses low-cost UAV and GPS module coupled with the IGN-LOEMI home-made camera.

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives.

PubMed

Yang, Guijun; Liu, Jiangang; Zhao, Chunjiang; Li, Zhenhong; Huang, Yanbo; Yu, Haiyang; Xu, Bo; Yang, Xiaodong; Zhu, Dongmei; Zhang, Xiaoyan; Zhang, Ruyang; Feng, Haikuan; Zhao, Xiaoqing; Li, Zhenhai; Li, Heli; Yang, Hao

2017-01-01

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

PubMed Central

Yang, Guijun; Liu, Jiangang; Zhao, Chunjiang; Li, Zhenhong; Huang, Yanbo; Yu, Haiyang; Xu, Bo; Yang, Xiaodong; Zhu, Dongmei; Zhang, Xiaoyan; Zhang, Ruyang; Feng, Haikuan; Zhao, Xiaoqing; Li, Zhenhai; Li, Heli; Yang, Hao

2017-01-01

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping. PMID:28713402

An evaluation of a UAV guidance system with consumer grade GPS receivers

NASA Astrophysics Data System (ADS)

Rosenberg, Abigail Stella

Remote sensing has been demonstrated an important tool in agricultural and natural resource management and research applications, however there are limitations that exist with traditional platforms (i.e., hand held sensors, linear moves, vehicle mounted, airplanes, remotely piloted vehicles (RPVs), unmanned aerial vehicles (UAVs) and satellites). Rapid technological advances in electronics, computers, software applications, and the aerospace industry have dramatically reduced the cost and increased the availability of remote sensing technologies. Remote sensing imagery vary in spectral, spatial, and temporal resolutions and are available from numerous providers. Appendix A presented results of a test project that acquired high-resolution aerial photography with a RPV to map the boundary of a 0.42 km2 fire area. The project mapped the boundaries of the fire area from a mosaic of the aerial images collected and compared this with ground-based measurements. The project achieved a 92.4% correlation between the aerial assessment and the ground truth data. Appendix B used multi-objective analysis to quantitatively assess the tradeoffs between different sensor platform attributes to identify the best overall technology. Experts were surveyed to identify the best overall technology at three different pixel sizes. Appendix C evaluated the positional accuracy of a relatively low cost UAV designed for high resolution remote sensing of small areas in order to determine the positional accuracy of sensor readings. The study evaluated the accuracy and uncertainty of a UAV flight route with respect to the programmed waypoints and of the UAV's GPS position, respectively. In addition, the potential displacement of sensor data was evaluated based on (1) GPS measurements on board the aircraft and (2) the autopilot's circuit board with 3-axis gyros and accelerometers (i.e., roll, pitch, and yaw). The accuracies were estimated based on a 95% confidence interval or similar methods. The accuracy achieved in the second and third manuscripts demonstrates that reasonably priced, high resolution remote sensing via RPVs and UAVs is practical for agriculture and natural resource professionals.

Real-time implementations of acoustic signal enhancement techniques for aerial based surveillance and rescue applications

NASA Astrophysics Data System (ADS)

Ramos, Antonio L. L.; Shao, Zhili; Holthe, Aleksander; Sandli, Mathias F.

2017-05-01

The introduction of the System-on-Chip (SoC) technology has brought exciting new opportunities for the development of smart low cost embedded systems spanning a wide range of applications. Currently available SoC devices are capable of performing high speed digital signal processing tasks in software while featuring relatively low development costs and reduced time-to-market. Unmanned aerial vehicles (UAV) are an application example that has shown tremendous potential in an increasing number of scenarios, ranging from leisure to surveillance as well as in search and rescue missions. Video capturing from UAV platforms is a relatively straightforward task that requires almost no preprocessing. However, that does not apply to audio signals, especially in cases where the data is to be used to support real-time decision making. In fact, the enormous amount of acoustic interference from the surroundings, including the noise from the UAVs propellers, becomes a huge problem. This paper discusses a real-time implementation of the NLMS adaptive filtering algorithm applied to enhancing acoustic signals captured from UAV platforms. The model relies on a combination of acoustic sensors and a computational inexpensive algorithm running on a digital signal processor. Given its simplicity, this solution can be incorporated into the main processing system of an UAV using the SoC technology, and run concurrently with other required tasks, such as flight control and communications. Simulations and real-time DSP-based implementations have shown significant signal enhancement results by efficiently mitigating the interference from the noise generated by the UAVs propellers as well as from other external noise sources.

Integrating a High Resolution Optically Pumped Magnetometer with a Multi-Rotor UAV towards 3-D Magnetic Gradiometry

NASA Astrophysics Data System (ADS)

Braun, A.; Walter, C. A.; Parvar, K.

2016-12-01

The current platforms for collecting magnetic data include dense coverage, but low resolution traditional airborne surveys, and high resolution, but low coverage terrestrial surveys. Both platforms leave a critical observation gap between the ground surface and approximately 100m above ground elevation, which can be navigated efficiently by new technologies, such as Unmanned Aerial Vehicles (UAVs). Specifically, multi rotor UAV platforms provide the ability to sense the magnetic field in a full 3-D tensor, which increases the quality of data collected over other current platform types. Payload requirements and target requirements must be balanced to fully exploit the 3-D magnetic tensor. This study outlines the integration of a GEM Systems Cesium Vapour UAV Magnetometer, a Lightware SF-11 Laser Altimeter and a uBlox EVK-7P GPS module with a DJI s900 Multi Rotor UAV. The Cesium Magnetometer is suspended beneath the UAV platform by a cable of varying length. A set of surveys was carried out to optimize the sensor orientation, sensor cable length beneath the UAV and data collection methods of the GEM Systems Cesium Vapour UAV Magnetometer when mounted on the DJI s900. The target for these surveys is a 12 inch steam pipeline located approximately 2 feet below the ground surface. A systematic variation of cable length, sensor orientation and inclination was conducted. The data collected from the UAV magnetometer was compared to a terrestrial survey conducted with the GEM GST-19 Proton Procession Magnetometer at the same elevation, which also served a reference station. This allowed for a cross examination between the UAV system and a proven industry standard for magnetic field data collection. The surveys resulted in optimizing the above parameters based on minimizing instrument error and ensuring reliable data acquisition. The results demonstrate that optimizing the UAV magnetometer survey can yield to industry standard measurements.

Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses

PubMed Central

Corniglia, Matteo; Gaetani, Monica; Grossi, Nicola; Magni, Simone; Migliazzi, Mauro; Angelini, Luciana; Mazzoncini, Marco; Silvestri, Nicola; Fontanelli, Marco; Raffaelli, Michele; Peruzzi, Andrea; Volterrani, Marco

2016-01-01

Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits. The objectives of the trial were i) to compare the spectral reflectance of 3 turfgrasses acquired via UAV and by a ground-based instrument; ii) to test the sensitivity of the 2 data acquisition sources in detecting induced variation in N levels. N application gradients from 0 to 250 kg ha-1 were created on 3 different turfgrass species: Cynodon dactylon x transvaalensis (Cdxt) ‘Patriot’, Zoysia matrella (Zm) ‘Zeon’ and Paspalum vaginatum (Pv) ‘Salam’. Proximity and remote-sensed reflectance measurements were acquired using a GreenSeeker handheld crop sensor and a UAV with onboard a multispectral sensor, to determine Normalized Difference Vegetation Index (NDVI). Proximity-sensed NDVI is highly correlated with data acquired from UAV with r values ranging from 0.83 (Zm) to 0.97 (Cdxt). Relating NDVI-UAV with clippings N, the highest r is for Cdxt (0.95). The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%. UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management. For relatively small green areas, a hand-held crop sensor can be a less expensive and more practical option. PMID:27341674

Multi-sensor field trials for detection and tracking of multiple small unmanned aerial vehicles flying at low altitude

NASA Astrophysics Data System (ADS)

Laurenzis, Martin; Hengy, Sebastien; Hommes, Alexander; Kloeppel, Frank; Shoykhetbrod, Alex; Geibig, Thomas; Johannes, Winfried; Naz, Pierre; Christnacher, Frank

2017-05-01

Small unmanned aerial vehicles (UAV) flying at low altitude are becoming more and more a serious threat in civilian and military scenarios. In recent past, numerous incidents have been reported where small UAV were flying in security areas leading to serious danger to public safety or privacy. The detection and tracking of small UAV is a widely discussed topic. Especially, small UAV flying at low altitude in urban environment or near background structures and the detection of multiple UAV at the same time is challenging. Field trials were carried out to investigate the detection and tracking of multiple UAV flying at low altitude with state of the art detection technologies. Here, we present results which were achieved using a heterogeneous sensor network consisting of acoustic antennas, small frequency modulated continuous wave (FMCW) RADAR systems and optical sensors. While acoustics, RADAR and LiDAR were applied to monitor a wide azimuthal area (360°) and to simultaneously track multiple UAV, optical sensors were used for sequential identification with a very narrow field of view.

Pricise Target Geolocation Based on Integeration of Thermal Video Imagery and Rtk GPS in Uavs

NASA Astrophysics Data System (ADS)

Hosseinpoor, H. R.; Samadzadegan, F.; Dadras Javan, F.

2015-12-01

There are an increasingly large number of uses for Unmanned Aerial Vehicles (UAVs) from surveillance, mapping and target geolocation. However, most of commercial UAVs are equipped with low-cost navigation sensors such as C/A code GPS and a low-cost IMU on board, allowing a positioning accuracy of 5 to 10 meters. This low accuracy which implicates that it cannot be used in applications that require high precision data on cm-level. This paper presents a precise process for geolocation of ground targets based on thermal video imagery acquired by small UAV equipped with RTK GPS. The geolocation data is filtered using a linear Kalman filter, which provides a smoothed estimate of target location and target velocity. The accurate geo-locating of targets during image acquisition is conducted via traditional photogrammetric bundle adjustment equations using accurate exterior parameters achieved by on board IMU and RTK GPS sensors and Kalman filtering and interior orientation parameters of thermal camera from pre-flight laboratory calibration process.

Top-down and Bottom-up aerosol-cloud-closure: towards understanding sources of unvertainty in deriving cloud radiative flux

NASA Astrophysics Data System (ADS)

Sanchez, K.; Roberts, G.; Calmer, R.; Nicoll, K.; Hashimshoni, E.; Rosenfeld, D.; Ovadnevaite, J.; Preissler, J.; Ceburnis, D.; O'Dowd, C. D. D.; Russell, L. M.

2017-12-01

Top-down and bottom-up aerosol-cloud shortwave radiative flux closures were conducted at the Mace Head atmospheric research station in Galway, Ireland in August 2015. Instrument platforms include ground-based, unmanned aerial vehicles (UAV), and satellite measurements of aerosols, clouds and meteorological variables. The ground-based and airborne measurements of aerosol size distributions and cloud condensation nuclei (CCN) concentration were used to initiate a 1D microphysical aerosol-cloud parcel model (ACPM). UAVs were equipped for a specific science mission, with an optical particle counter for aerosol distribution profiles, a cloud sensor to measure cloud extinction, or a 5-hole probe for 3D wind vectors. These are the first UAV measurements at Mace Head. ACPM simulations are compared to in-situ cloud extinction measurements from UAVs to quantify closure in terms of cloud shortwave radiative flux. Two out of seven cases exhibit sub-adiabatic vertical temperature profiles within the cloud, which suggests that entrainment processes affect cloud microphysical properties and lead to an overestimate of simulated cloud shortwave radiative flux. Including an entrainment parameterization and explicitly calculating the entrainment fraction in the ACPM simulations both improved cloud-top radiative closure. Entrainment reduced the difference between simulated and observation-derived cloud-top shortwave radiative flux (δRF) by between 25 W m-2 and 60 W m-2. After accounting for entrainment, satellite-derived cloud droplet number concentrations (CDNC) were within 30% of simulated CDNC. In cases with a well-mixed boundary layer, δRF is no greater than 20 W m-2 after accounting for cloud-top entrainment, and up to 50 W m-2 when entrainment is not taken into account. In cases with a decoupled boundary layer, cloud microphysical properties are inconsistent with ground-based aerosol measurements, as expected, and δRF is as high as 88 W m-2, even high (> 30 W m-2) after accounting for cloud-top entrainment. This work demonstrates the need to take in-situ measurements of aerosol properties for cases where the boundary layer is decoupled as well as consider cloud-top entrainment to accurately model stratocumulus cloud radiative flux.

Top-down and Bottom-up aerosol-cloud-closure: towards understanding sources of unvertainty in deriving cloud radiative flux

NASA Astrophysics Data System (ADS)

Sanchez, K.; Roberts, G.; Calmer, R.; Nicoll, K.; Hashimshoni, E.; Rosenfeld, D.; Ovadnevaite, J.; Preissler, J.; Ceburnis, D.; O'Dowd, C. D. D.; Russell, L. M.

2016-12-01

Top-down and bottom-up aerosol-cloud shortwave radiative flux closures were conducted at the Mace Head atmospheric research station in Galway, Ireland in August 2015. Instrument platforms include ground-based, unmanned aerial vehicles (UAV), and satellite measurements of aerosols, clouds and meteorological variables. The ground-based and airborne measurements of aerosol size distributions and cloud condensation nuclei (CCN) concentration were used to initiate a 1D microphysical aerosol-cloud parcel model (ACPM). UAVs were equipped for a specific science mission, with an optical particle counter for aerosol distribution profiles, a cloud sensor to measure cloud extinction, or a 5-hole probe for 3D wind vectors. These are the first UAV measurements at Mace Head. ACPM simulations are compared to in-situ cloud extinction measurements from UAVs to quantify closure in terms of cloud shortwave radiative flux. Two out of seven cases exhibit sub-adiabatic vertical temperature profiles within the cloud, which suggests that entrainment processes affect cloud microphysical properties and lead to an overestimate of simulated cloud shortwave radiative flux. Including an entrainment parameterization and explicitly calculating the entrainment fraction in the ACPM simulations both improved cloud-top radiative closure. Entrainment reduced the difference between simulated and observation-derived cloud-top shortwave radiative flux (δRF) by between 25 W m-2 and 60 W m-2. After accounting for entrainment, satellite-derived cloud droplet number concentrations (CDNC) were within 30% of simulated CDNC. In cases with a well-mixed boundary layer, δRF is no greater than 20 W m-2 after accounting for cloud-top entrainment, and up to 50 W m-2 when entrainment is not taken into account. In cases with a decoupled boundary layer, cloud microphysical properties are inconsistent with ground-based aerosol measurements, as expected, and δRF is as high as 88 W m-2, even high (> 30 W m-2) after accounting for cloud-top entrainment. This work demonstrates the need to take in-situ measurements of aerosol properties for cases where the boundary layer is decoupled as well as consider cloud-top entrainment to accurately model stratocumulus cloud radiative flux.

UAV Flight Control Using Distributed Actuation and Sensing

NASA Technical Reports Server (NTRS)

Barnwell, William G.; Heinzen, Stearns N.; Hall, Charles E., Jr.; Chokani, Ndaona; Raney, David L. (Technical Monitor)

2003-01-01

An array of effectors and sensors has been designed, tested and implemented on a Blended Wing Body Uninhabited Aerial Vehicle (UAV). This UAV is modified to serve as a flying, controls research, testbed. This effectorhensor array provides for the dynamic vehicle testing of controller designs and the study of decentralized control techniques. Each wing of the UAV is equipped with 12 distributed effectors that comprise a segmented array of independently actuated, contoured control surfaces. A single pressure sensor is installed near the base of each effector to provide a measure of deflections of the effectors. The UAV wings were tested in the North Carolina State University Subsonic Wind Tunnel and the pressure distribution that result from the deflections of the effectors are characterized. The results of the experiments are used to develop a simple, but accurate, prediction method, such that for any arrangement of the effector array the corresponding pressure distribution can be determined. Numerical analysis using the panel code CMARC verifies this prediction method.

Application of Sensor Fusion to Improve Uav Image Classification

NASA Astrophysics Data System (ADS)

Jabari, S.; Fathollahi, F.; Zhang, Y.

2017-08-01

Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data.

PubMed

Vanegas, Fernando; Bratanov, Dmitry; Powell, Kevin; Weiss, John; Gonzalez, Felipe

2018-01-17

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used-the sensors, the UAV, and the flight operations-the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analising and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.

Sense and avoid technology for Global Hawk and Predator UAVs

NASA Astrophysics Data System (ADS)

McCalmont, John F.; Utt, James; Deschenes, Michael; Taylor, Michael J.

2005-05-01

The Sensors Directorate at the Air Force Research Laboratory (AFRL) along with Defense Research Associates, Inc. (DRA) conducted a flight demonstration of technology that could potentially satisfy the Federal Aviation Administration's (FAA) requirement for Unmanned Aerial Vehicles (UAVs) to sense and avoid local air traffic sufficient to provide an "...equivalent level of safety, comparable to see-and-avoid requirements for manned aircraft". This FAA requirement must be satisfied for autonomous UAV operation within the national airspace. The real-time on-board system passively detects approaching aircraft, both cooperative and non-cooperative, using imaging sensors operating in the visible/near infrared band and a passive moving target indicator algorithm. Detection range requirements for RQ-4 and MQ-9 UAVs were determined based on analysis of flight geometries, avoidance maneuver timelines, system latencies and human pilot performance. Flight data and UAV operating parameters were provided by the system program offices, prime contractors, and flight-test personnel. Flight demonstrations were conducted using a surrogate UAV (Aero Commander) and an intruder aircraft (Beech Bonanza). The system demonstrated target detection ranges out to 3 nautical miles in nose-to-nose scenarios and marginal visual meteorological conditions. (VMC) This paper will describe the sense and avoid requirements definition process and the system concept (sensors, algorithms, processor, and flight rest results) that has demonstrated the potential to satisfy the FAA sense and avoid requirements.

Pricise Target Geolocation and Tracking Based on Uav Video Imagery

NASA Astrophysics Data System (ADS)

Hosseinpoor, H. R.; Samadzadegan, F.; Dadrasjavan, F.

2016-06-01

There is an increasingly large number of applications for Unmanned Aerial Vehicles (UAVs) from monitoring, mapping and target geolocation. However, most of commercial UAVs are equipped with low-cost navigation sensors such as C/A code GPS and a low-cost IMU on board, allowing a positioning accuracy of 5 to 10 meters. This low accuracy cannot be used in applications that require high precision data on cm-level. This paper presents a precise process for geolocation of ground targets based on thermal video imagery acquired by small UAV equipped with RTK GPS. The geolocation data is filtered using an extended Kalman filter, which provides a smoothed estimate of target location and target velocity. The accurate geo-locating of targets during image acquisition is conducted via traditional photogrammetric bundle adjustment equations using accurate exterior parameters achieved by on board IMU and RTK GPS sensors, Kalman filtering and interior orientation parameters of thermal camera from pre-flight laboratory calibration process. The results of this study compared with code-based ordinary GPS, indicate that RTK observation with proposed method shows more than 10 times improvement of accuracy in target geolocation.

Agent-Based Simulation and Analysis of a Defensive UAV Swarm Against an Enemy UAV Swarm

DTIC Science & Technology

2011-06-01

de Investigacion, Programas y Desarrollo de la Armada Armada de Chile CHILE 10. CAPT Jeffrey Kline, USN(ret.) Naval Postgraduate School Monterey, California 91 ...this de - fensive swarm system, an agent-based simulation model is developed, and appropriate designs of experiments and statistical analyses are... de - velopment and implementation of counter UAV technology from readily-available commercial products. The organization leverages the “largest

Spurious RF signals emitted by mini-UAVs

NASA Astrophysics Data System (ADS)

Schleijpen, Ric (H. M. A.); Voogt, Vincent; Zwamborn, Peter; van den Oever, Jaap

2016-10-01

This paper presents experimental work on the detection of spurious RF emissions of mini Unmanned Aerial Vehicles (mini-UAV). Many recent events have shown that mini-UAVs can be considered as a potential threat for civil security. For this reason the detection of mini-UAVs has become of interest to the sensor community. The detection, classification and identification chain can take advantage of different sensor technologies. Apart from the signatures used by radar and electro-optical sensor systems, the UAV also emits RF signals. These RF signatures can be split in intentional signals for communication with the operator and un-intentional RF signals emitted by the UAV. These unintentional or spurious RF emissions are very weak but could be used to discriminate potential UAV detections from false alarms. The goal of this research was to assess the potential of exploiting spurious emissions in the classification and identification chain of mini-UAVs. It was already known that spurious signals are very weak, but the focus was on the question whether the emission pattern could be correlated to the behaviour of the UAV. In this paper experimental examples of spurious RF emission for different types of mini-UAVs and their correlation with the electronic circuits in the UAVs will be shown

Assignment Scheduling Capability for Unmanned Aerial Vehicles - A Discrete Event Simulation with Optimization in the Loop Approach to Solving a Scheduling Problem

DTIC Science & Technology

2006-12-01

APPROACH As mentioned previously, ASCU does not use simulation in the traditional manner. Instead, it uses simulation to transition and capture the state...0 otherwise (by a heuristic discussed below). • Let cja = The reward for a UAV with sensor pack- age j being assigned to mission area a from the

Multisensor Equipped Uav/ugv for Automated Exploration

NASA Astrophysics Data System (ADS)

Batzdorfer, S.; Bobbe, M.; Becker, M.; Harms, H.; Bestmann, U.

2017-08-01

The usage of unmanned systems for exploring disaster scenarios has become more and more important in recent times as a supporting system for action forces. These systems have to offer a well-balanced relationship between the quality of support and additional workload. Therefore within the joint research project ANKommEn - german acronym for Automated Navigation and Communication for Exploration - a system for exploration of disaster scenarios is build-up using multiple UAV und UGV controlled via a central ground station. The ground station serves as user interface for defining missions and tasks conducted by the unmanned systems, equipped with different environmental sensors like cameras - RGB as well as IR - or LiDAR. Depending on the exploration task results, in form of pictures, 2D stitched orthophoto or LiDAR point clouds will be transmitted via datalinks and displayed online at the ground station or will be processed in short-term after a mission, e.g. 3D photogrammetry. For mission planning and its execution, UAV/UGV monitoring and georeferencing of environmental sensor data, reliable positioning and attitude information is required. This is gathered using an integrated GNSS/IMU positioning system. In order to increase availability of positioning information in GNSS challenging scenarios, a GNSS-Multiconstellation based approach is used, amongst others. The present paper focuses on the overall system design including the ground station and sensor setups on the UAVs and UGVs, the underlying positioning techniques as well as 2D and 3D exploration based on a RGB camera mounted on board the UAV and its evaluation based on real world field tests.

UAV Inspection of Electrical Transmission Infrastructure with Path Conformance Autonomy and Lidar-Based Geofences NASA Report on UTM Reference Mission Flights at Southern Company Flights November 2016

NASA Technical Reports Server (NTRS)

Moore, Andrew J.; Schubert, Matthew; Rymer, Nicholas; Balachandran, Swee; Consiglio, Maria; Munoz, Cesar; Smith, Joshua; Lewis, Dexter; Schneider, Paul

2017-01-01

Flights at low altitudes in close proximity to electrical transmission infrastructure present serious navigational challenges: GPS and radio communication quality is variable and yet tight position control is needed to measure defects while avoiding collisions with ground structures. To advance unmanned aerial vehicle (UAV) navigation technology while accomplishing a task with economic and societal benefit, a high voltage electrical infrastructure inspection reference mission was designed. An integrated air-ground platform was developed for this mission and tested in two days of experimental flights to determine whether navigational augmentation was needed to successfully conduct a controlled inspection experiment. The airborne component of the platform was a multirotor UAV built from commercial off-the-shelf hardware and software, and the ground component was a commercial laptop running open source software. A compact ultraviolet sensor mounted on the UAV can locate 'hot spots' (potential failure points in the electric grid), so long as the UAV flight path adequately samples the airspace near the power grid structures. To improve navigation, the platform was supplemented with two navigation technologies: lidar-to-polyhedron preflight processing for obstacle demarcation and inspection distance planning, and trajectory management software to enforce inspection standoff distance. Both navigation technologies were essential to obtaining useful results from the hot spot sensor in this obstacle-rich, low-altitude airspace. Because the electrical grid extends into crowded airspaces, the UAV position was tracked with NASA unmanned aerial system traffic management (UTM) technology. The following results were obtained: (1) Inspection of high-voltage electrical transmission infrastructure to locate 'hot spots' of ultraviolet emission requires navigation methods that are not broadly available and are not needed at higher altitude flights above ground structures. (2) The sensing capability of a novel airborne UV detector was verified with a standard ground-based instrument. Flights with this sensor showed that UAV measurement operations and recording methods are viable. With improved sensor range, UAVs equipped with compact UV sensors could serve as the detection elements in a self-diagnosing power grid. (3) Simplification of rich lidar maps to polyhedral obstacle maps reduces data volume by orders of magnitude, so that computation with the resultant maps in real time is possible. This enables real-time obstacle avoidance autonomy. Stable navigation may be feasible in the GPS-deprived environment near transmission lines by a UAV that senses ground structures and compares them to these simplified maps. (4) A new, formally verified path conformance software system that runs onboard a UAV was demonstrated in flight for the first time. It successfully maneuvered the aircraft after a sudden lateral perturbation that models a gust of wind, and processed lidar-derived polyhedral obstacle maps in real time. (5) Tracking of the UAV in the national airspace using the NASA UTM technology was a key safety component of this reference mission, since the flights were conducted beneath the landing approach to a heavily used runway. Comparison to autopilot tracking showed that UTM tracking accurately records the UAV position throughout the flight path.

Repurposing Radiosonde Sensors for UAV Integration

NASA Astrophysics Data System (ADS)

Clowney, F. A.

2015-12-01

Radiosondes provide accurate, high-resolution meteorological data for a variety of purposes but are inefficient for studying the atmospheric boundary layer. Tethered balloons can provide greater temporal resolution but are difficult to acquire, hard to manage and limited in vertical resolution. UAVs appear to offer a more cost-effective method for gathering low-level meteorological data in situ, with a strong possibility of adding atmospheric chemistry. This potential is enhanced by the availability of new generations of small sensors along with dramatic advances in low-cost UAVs, especially rotary-wing. InterMet is using its experience in radiosonde design and manufacturing to develop sensor packages for fixed and rotary-wing UAVs, with the goal of delivering high-quality data at low cost. The challenge is to adapt affordable, high-accuracy sensors to the different UAV flight modes. Equally important is learning from the research community what is required for this data to have useful scientific value. Specific topics to be covered include data sampling and output rates, sensor response times, calibration, sensor placement, data storage and transfer, power consumption, integration with flight management systems and wind calculations. Beta test results for the iMet-XQ and iMet-XF sensor packages will be presented if available.

Top-down and bottom-up aerosol-cloud closure: towards understanding sources of uncertainty in deriving cloud shortwave radiative flux

NASA Astrophysics Data System (ADS)

Sanchez, Kevin J.; Roberts, Gregory C.; Calmer, Radiance; Nicoll, Keri; Hashimshoni, Eyal; Rosenfeld, Daniel; Ovadnevaite, Jurgita; Preissler, Jana; Ceburnis, Darius; O'Dowd, Colin; Russell, Lynn M.

2017-08-01

Top-down and bottom-up aerosol-cloud shortwave radiative flux closures were conducted at the Mace Head Atmospheric Research Station in Galway, Ireland, in August 2015. This study is part of the BACCHUS (Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding) European collaborative project, with the goal of understanding key processes affecting aerosol-cloud shortwave radiative flux closures to improve future climate predictions and develop sustainable policies for Europe. Instrument platforms include ground-based unmanned aerial vehicles (UAVs)1 and satellite measurements of aerosols, clouds and meteorological variables. The ground-based and airborne measurements of aerosol size distributions and cloud condensation nuclei (CCN) concentration were used to initiate a 1-D microphysical aerosol-cloud parcel model (ACPM). UAVs were equipped for a specific science mission, with an optical particle counter for aerosol distribution profiles, a cloud sensor to measure cloud extinction or a five-hole probe for 3-D wind vectors. UAV cloud measurements are rare and have only become possible in recent years through the miniaturization of instrumentation. These are the first UAV measurements at Mace Head. ACPM simulations are compared to in situ cloud extinction measurements from UAVs to quantify closure in terms of cloud shortwave radiative flux. Two out of seven cases exhibit sub-adiabatic vertical temperature profiles within the cloud, which suggests that entrainment processes affect cloud microphysical properties and lead to an overestimate of simulated cloud shortwave radiative flux. Including an entrainment parameterization and explicitly calculating the entrainment fraction in the ACPM simulations both improved cloud-top radiative closure. Entrainment reduced the difference between simulated and observation-derived cloud-top shortwave radiative flux (δRF) by between 25 and 60 W m-2. After accounting for entrainment, satellite-derived cloud droplet number concentrations (CDNCs) were within 30 % of simulated CDNC. In cases with a well-mixed boundary layer, δRF is no greater than 20 W m-2 after accounting for cloud-top entrainment and up to 50 W m-2 when entrainment is not taken into account. In cases with a decoupled boundary layer, cloud microphysical properties are inconsistent with ground-based aerosol measurements, as expected, and δRF is as high as 88 W m-2, even high (> 30 W m-2) after accounting for cloud-top entrainment. This work demonstrates the need to take in situ measurements of aerosol properties for cases where the boundary layer is decoupled as well as consider cloud-top entrainment to accurately model stratocumulus cloud radiative flux. 1The regulatory term for UAV is remotely piloted aircraft (RPA).

Searching Lost People with Uavs: the System and Results of the Close-Search Project

NASA Astrophysics Data System (ADS)

Molina, P.; Colomina, I.; Vitoria, T.; Silva, P. F.; Skaloud, J.; Kornus, W.; Prades, R.; Aguilera, C.

2012-07-01

This paper will introduce the goals, concept and results of the project named CLOSE-SEARCH, which stands for 'Accurate and safe EGNOS-SoL Navigation for UAV-based low-cost Search-And-Rescue (SAR) operations'. The main goal is to integrate a medium-size, helicopter-type Unmanned Aerial Vehicle (UAV), a thermal imaging sensor and an EGNOS-based multi-sensor navigation system, including an Autonomous Integrity Monitoring (AIM) capability, to support search operations in difficult-to-access areas and/or night operations. The focus of the paper is three-fold. Firstly, the operational and technical challenges of the proposed approach are discussed, such as ultra-safe multi-sensor navigation system, the use of combined thermal and optical vision (infrared plus visible) for person recognition and Beyond-Line-Of-Sight communications among others. Secondly, the implementation of the integrity concept for UAV platforms is discussed herein through the AIM approach. Based on the potential of the geodetic quality analysis and on the use of the European EGNOS system as a navigation performance starting point, AIM approaches integrity from the precision standpoint; that is, the derivation of Horizontal and Vertical Protection Levels (HPLs, VPLs) from a realistic precision estimation of the position parameters is performed and compared to predefined Alert Limits (ALs). Finally, some results from the project test campaigns are described to report on particular project achievements. Together with actual Search-and-Rescue teams, the system was operated in realistic, user-chosen test scenarios. In this context, and specially focusing on the EGNOS-based UAV navigation, the AIM capability and also the RGB/thermal imaging subsystem, a summary of the results is presented.

Development of a bio-inspired UAV perching system

NASA Astrophysics Data System (ADS)

Xie, Pu

Although technologies of unmanned aerial vehicles (UAVs) including micro air vehicles (MAVs) have been greatly advanced in the recent years, it is still very difficult for a UAV to perform some very challenging tasks such as perching to any desired spot reliably and agilely like a bird. Unlike the UAVs, the biological control mechanism of birds has been optimized through millions of year evolution and hence, they can perform many extremely maneuverability tasks, such as perching or grasping accurately and robustly. Therefore, we have good reason to learn from the nature in order to significantly improve the capabilities of UAVs. The development of a UAV perching system is becoming feasible, especially after a lot of research contributions in ornithology which involve the analysis of the bird's functionalities. Meanwhile, as technology advances in many engineering fields, such as airframes, propulsion, sensors, batteries, micro-electromechanical-system (MEMS), and UAV technology is also advancing rapidly. All of these research efforts in ornithology and the fast growing development technologies in UAV applications are motivating further interests and development in the area of UAV perching and grasping research. During the last decade, the research contributions about UAV perching and grasping were mainly based on fixed-wing, flapping-wing, and rotorcraft UAVs. However, most of the current researches in UAV systems with perching and grasping capability are focusing on either active (powered) grasping and perching or passive (unpowered) perching. Although birds do have both active and passive perching capabilities depending on their needs, there is no UAV perching system with both capabilities. In this project, we focused on filling this gap. Inspired by the anatomy analysis of bird legs and feet, a novel perching system has been developed to implement the bionics action for both active grasping and passive perching. In addition, for developing a robust and autonomous perching system, the following objectives were included for this project. The statics model was derived through both quasi-static and analytical method. The grasping stable condition and grasping target of the mechanical gripper were studied through the static analysis. Furthermore, the contact behavior between each foot and the perched object was modeled and evaluated on SimMechanics based on the contact force model derived through virtual principle. The kinematics modeling of UAV perching system was governed with Euler angles and quaternions. Also the propulsion model of the brushless motors was introduced and calibrated. In addition, the flight dynamics model of the UAV system was developed for simulation-based analysis prior to developing a hardware prototype and flight experiment. A special inertial measurement unit (IMU) was designed which has the capability of indirectly calculating the angular acceleration from the angular velocity and the linear acceleration readings. Moreover, a commercial-of-the-shelf (COTS) autopilot-APM 2.6 was selected for the autonomous flight control of the quadrotor. The APM 2.6 is a complete open source autopilot system, which allows the user to turn any fixed, rotary wing or multi-rotor vehicle into a fully autonomous vehicle and capable of performing programmed GPS missions with pre-programed waypoints. In addition, algorithms for inverted pendulum control and autonomous perching control was introduced. The proportion-integrate-differential (PID) controller was used for the simplified UAV perching with inverted pendulum model for horizontal balance. The performance of the controller was verified through both simulation and experiment. In addition, for the purpose of achieving the autonomous perching, guidance and control algorithms were developed the UAV perching system. For guidance, the desired flight trajectory was developed based on a bio-behavioral tau theory which was established from studying the natural motion patterns of animals and human arms approaching to a fixed or moving target for grasping or capturing. The autonomous flight control was also implemented through a PID controller. Autonomous flight performance was proved through simulation in SimMechanics. Finally, the prototyping of our designs were conducted in different generations of our bio-inspired UAV perching system, which include the leg prototype, gripper prototype, and system prototype. Both the machined prototype and 3D printed prototype were tried. The performance of these prototypes was tested through experiments.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data

PubMed Central

Vanegas, Fernando; Weiss, John; Gonzalez, Felipe

2018-01-01

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used—the sensors, the UAV, and the flight operations—the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analysing and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications. PMID:29342101

Online optimal obstacle avoidance for rotary-wing autonomous unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Kang, Keeryun

This thesis presents an integrated framework for online obstacle avoidance of rotary-wing unmanned aerial vehicles (UAVs), which can provide UAVs an obstacle field navigation capability in a partially or completely unknown obstacle-rich environment. The framework is composed of a LIDAR interface, a local obstacle grid generation, a receding horizon (RH) trajectory optimizer, a global shortest path search algorithm, and a climb rate limit detection logic. The key feature of the framework is the use of an optimization-based trajectory generation in which the obstacle avoidance problem is formulated as a nonlinear trajectory optimization problem with state and input constraints over the finite range of the sensor. This local trajectory optimization is combined with a global path search algorithm which provides a useful initial guess to the nonlinear optimization solver. Optimization is the natural process of finding the best trajectory that is dynamically feasible, safe within the vehicle's flight envelope, and collision-free at the same time. The optimal trajectory is continuously updated in real time by the numerical optimization solver, Nonlinear Trajectory Generation (NTG), which is a direct solver based on the spline approximation of trajectory for dynamically flat systems. In fact, the overall approach of this thesis to finding the optimal trajectory is similar to the model predictive control (MPC) or the receding horizon control (RHC), except that this thesis followed a two-layer design; thus, the optimal solution works as a guidance command to be followed by the controller of the vehicle. The framework is implemented in a real-time simulation environment, the Georgia Tech UAV Simulation Tool (GUST), and integrated in the onboard software of the rotary-wing UAV test-bed at Georgia Tech. Initially, the 2D vertical avoidance capability of real obstacles was tested in flight. The flight test evaluations were extended to the benchmark tests for 3D avoidance capability over the virtual obstacles, and finally it was demonstrated on real obstacles located at the McKenna MOUT site in Fort Benning, Georgia. Simulations and flight test evaluations demonstrate the feasibility of the developed framework for UAV applications involving low-altitude flight in an urban area.

Uav Borne Low Altitude Photogrammetry System

NASA Astrophysics Data System (ADS)

Lin, Z.; Su, G.; Xie, F.

2012-07-01

In this paper，the aforementioned three major aspects related to the Unmanned Aerial Vehicles (UAV) system for low altitude aerial photogrammetry, i.e., flying platform, imaging sensor system and data processing software, are discussed. First of all, according to the technical requirements about the least cruising speed, the shortest taxiing distance, the level of the flight control and the performance of turbulence flying, the performance and suitability of the available UAV platforms (e.g., fixed wing UAVs, the unmanned helicopters and the unmanned airships) are compared and analyzed. Secondly, considering the restrictions on the load weight of a platform and the resolution pertaining to a sensor, together with the exposure equation and the theory of optical information, the principles of designing self-calibration and self-stabilizing combined wide-angle digital cameras (e.g., double-combined camera and four-combined camera) are placed more emphasis on. Finally, a software named MAP-AT, considering the specialty of UAV platforms and sensors, is developed and introduced. Apart from the common functions of aerial image processing, MAP-AT puts more effort on automatic extraction, automatic checking and artificial aided adding of the tie points for images with big tilt angles. Based on the recommended process for low altitude photogrammetry with UAVs in this paper, more than ten aerial photogrammetry missions have been accomplished, the accuracies of Aerial Triangulation, Digital orthophotos(DOM)and Digital Line Graphs(DLG) of which meet the standard requirement of 1:2000, 1:1000 and 1:500 mapping.

Development of collision avoidance system for useful UAV applications using image sensors with laser transmitter

NASA Astrophysics Data System (ADS)

Cheong, M. K.; Bahiki, M. R.; Azrad, S.

2016-10-01

The main goal of this study is to demonstrate the approach of achieving collision avoidance on Quadrotor Unmanned Aerial Vehicle (QUAV) using image sensors with colour- based tracking method. A pair of high definition (HD) stereo cameras were chosen as the stereo vision sensor to obtain depth data from flat object surfaces. Laser transmitter was utilized to project high contrast tracking spot for depth calculation using common triangulation. Stereo vision algorithm was developed to acquire the distance from tracked point to QUAV and the control algorithm was designed to manipulate QUAV's response based on depth calculated. Attitude and position controller were designed using the non-linear model with the help of Optitrack motion tracking system. A number of collision avoidance flight tests were carried out to validate the performance of the stereo vision and control algorithm based on image sensors. In the results, the UAV was able to hover with fairly good accuracy in both static and dynamic collision avoidance for short range collision avoidance. Collision avoidance performance of the UAV was better with obstacle of dull surfaces in comparison to shiny surfaces. The minimum collision avoidance distance achievable was 0.4 m. The approach was suitable to be applied in short range collision avoidance.

Research on performance requirements of turbofan engine used on carrier-based UAV

NASA Astrophysics Data System (ADS)

Zhao, Shufan; Li, Benwei; Zhang, Wenlong; Wu, Heng; Feng, Tang

2017-05-01

According to the mission requirements of the carrier-based unmanned aerial vehicle (UAV), a mode level flight was established to calculate the thrust requirements from altitude 9 km to 13 km. Then, the estimation method of flight profile was used to calculate the weight of UAV in each stage to get the specific fuel consumption requirements of the UAV in standby stage. The turbofan engine of carrier-based UAV should meet the thrust and specific fuel consumption requirements. Finally, the GSP software was used to verify the simulation of a small high-bypass turbofan engine. The conclusion is useful for the turbofan engine selection of carrier-based UAV.

A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification

USDA-ARS?s Scientific Manuscript database

With the rapid development of small imaging sensors and unmanned aerial vehicles (UAVs), remote sensing is undergoing a revolution with greatly increased spatial and temporal resolutions. While more relevant detail becomes available, it is a challenge to analyze the large number of images to extract...

Assessment of an Onboard EO Sensor to Enable Detect-and-Sense Capability for UAVs Operating in a Cluttered Environment

DTIC Science & Technology

2017-09-01

via visual sensors onboard the UAV. Both the hardware and software architecture design are discussed at length. Then, a series of tests that were...visual sensors onboard the UAV. Both the hardware and software architecture design are discussed at length. Then, a series of tests that were conducted...and representing the change in time . (1) Horn and Schunck (1981) further simplified this equation by taking the Taylor series

Development and evaluation of unmanned aerial vehicle (UAV) magnetometry systems

NASA Astrophysics Data System (ADS)

Parvar, Kiyavash

In this thesis, the procedure of conducting magnetic surveys from a UAV platform is investigated. In the process of evaluating UAVs for such surveys, magnetic sensors capable of operating on a UAV platform were tested using a terrestrial survey, as well as on a UAV-platform. Results were then compared to a model of the area generated using a proton precession magnetometer. Magnetic signature of the UAVs are discussed and impact values are calculated. For a better understanding of the magnetic fields around UAVs some micro-surveys were conducted with the help of a fluxgate magnetometer around two UAVs. Results of such surveys were used to determine a location to mount the magnetometer during the survey. A test survey over a known anomaly (a visible chromite outcrop in Oman) is conducted in order to determine the feasibility of using UAV-based magnetometry for chromite exploration. Observations were taken at two different elevations in order to generate a 3-D model of the magnetic field. Later, after applying upward continuation filters and comparing the calculated results to the real values, the reliability and uncertainty levels of such filters were investigated. Results show that magnetometery on UAV platforms is feasible. Unwanted signals can be noticeable and produce fake anomalies by the end of each line because of the swinging effect of the suspended magnetometer below the UAV. This should be reduced by hardware and software modifications i.e. applying non-linear filters and mounting the sensor on a rigid rod. Also, it was derived that the error level associated with upward continuation filters exceeds 45% and thus, using such filters instead of actual observations is not suggested in gradiometry. Moreover, 3-D magnetic gradient surveys can be beneficial for future inversion problems.

Feature-Based Approach for the Registration of Pushbroom Imagery with Existing Orthophotos

NASA Astrophysics Data System (ADS)

Xiong, Weifeng

Low-cost Unmanned Airborne Vehicles (UAVs) are rapidly becoming suitable platforms for acquiring remote sensing data for a wide range of applications. For example, a UAV-based mobile mapping system (MMS) is emerging as a novel phenotyping tool that delivers several advantages to alleviate the drawbacks of conventional manual plant trait measurements. Moreover, UAVs equipped with direct geo-referenced frame cameras and pushbroom scanners can acquire geospatial data for comprehensive high-throughput phenotyping. UAVs for mobile mapping platforms are low-cost and easy to use, can fly closer to the objects, and are filling an important gap between ground wheel-based and traditional manned-airborne platforms. However, consumer-grade UAVs are capable of carrying only equipment with a relatively light payload and their flying time is determined by a limited battery life. These restrictions of UAVs unfortunately force potential users to adopt lower-quality direct geo-referencing and imaging systems that may negatively impact the quality of the deliverables. Recent advances in sensor calibration and automated triangulation have made it feasible to obtain accurate mapping using low-cost camera systems equipped with consumer-grade GNSS/INS units. However, ortho-rectification of the data from a linear-array scanner is challenging for low-cost UAV systems, because the derived geo-location information from pushbroom sensors is quite sensitive to the performance of the implemented direct geo-referencing unit. This thesis presents a novel approach for improving the ortho-rectification of hyperspectral pushbroom scanner imagery with the aid of orthophotos generated from frame cameras through the identification of conjugate features while modeling the impact of residual artifacts in the direct geo-referencing information. The experimental results qualitatively and quantitatively proved the feasibility of the proposed methodology in improving the geo-referencing accuracy of real datasets collected over an agricultural field.

Introducing a Low-Cost Mini-Uav for - and Multispectral-Imaging

NASA Astrophysics Data System (ADS)

Bendig, J.; Bolten, A.; Bareth, G.

2012-07-01

The trend to minimize electronic devices also accounts for Unmanned Airborne Vehicles (UAVs) as well as for sensor technologies and imaging devices. Consequently, it is not surprising that UAVs are already part of our daily life and the current pace of development will increase civil applications. A well known and already wide spread example is the so called flying video game based on Parrot's AR.Drone which is remotely controlled by an iPod, iPhone, or iPad (http://ardrone.parrot.com). The latter can be considered as a low-weight and low-cost Mini-UAV. In this contribution a Mini-UAV is considered to weigh less than 5 kg and is being able to carry 0.2 kg to 1.5 kg of sensor payload. While up to now Mini-UAVs like Parrot's AR.Drone are mainly equipped with RGB cameras for videotaping or imaging, the development of such carriage systems clearly also goes to multi-sensor platforms like the ones introduced for larger UAVs (5 to 20 kg) by Jaakkolla et al. (2010) for forestry applications or by Berni et al. (2009) for agricultural applications. The problem when designing a Mini-UAV for multi-sensor imaging is the limitation of payload of up to 1.5 kg and a total weight of the whole system below 5 kg. Consequently, the Mini-UAV without sensors but including navigation system and GPS sensors must weigh less than 3.5 kg. A Mini-UAV system with these characteristics is HiSystems' MK-Okto (www.mikrokopter.de). Total weight including battery without sensors is less than 2.5 kg. Payload of a MK-Okto is approx. 1 kg and maximum speed is around 30 km/h. The MK-Okto can be operated up to a wind speed of less than 19 km/h which corresponds to Beaufort scale number 3 for wind speed. In our study, the MK-Okto is equipped with a handheld low-weight NEC F30IS thermal imaging system. The F30IS which was developed for veterinary applications, covers 8 to 13 μm, weighs only 300 g, and is capturing the temperature range between -20 °C and 100 °C. Flying at a height of 100 m, the camera's image covers an area of approx. 50 by 40 m. The sensor's resolution is 160 x 120 pixel and the field of view is 28° (H) x 21° (V). According to the producer, absolute accuracy for temperature is ±1 °C and the thermal sensitivity is >0.1 K. Additionally, the MK-Okto is equipped with Tetracam's Mini MCA. The Mini MCA in our study is a four band multispectral imaging system. Total weight is 700 g and spectral characteristics can be modified by filters between 400 and 1000 nm. In this study, three bands with a width of 10 nm (green: 550 nm, red: 671 nm, NIR1: 800 nm) and one band of 20 nm width (NIR2: 950 nm) have been used. Even so the MK-Okto is able to carry both sensors at the same time, the imaging systems were used separately for this contribution. First results of a combined thermal- and multispectral MK-Okto campaign in 2011 are presented and evaluated for a sugarbeet field experiment examining pathogens and drought stress.

The development of a UGV-mounted automated refueling system for VTOL UAVs

NASA Astrophysics Data System (ADS)

Wills, Mike; Burmeister, Aaron; Nelson, Travis; Denewiler, Thomas; Mullens, Kathy

2006-05-01

This paper describes the latest efforts to develop an Automated UAV Mission System (AUMS) for small vertical takeoff and landing (VTOL) unmanned air vehicles (UAVs). In certain applications such as force protection, perimeter security, and urban surveillance a VTOL UAV can provide far greater utility than fixed-wing UAVs or ground-based sensors. The VTOL UAV can operate much closer to an object of interest and can provide a hover-and-stare capability to keep its sensors trained on an object, while the fixed wing UAV would be forced into a higher altitude loitering pattern where its sensors would be subject to intermittent blockage by obstacles and terrain. The most significant disadvantage of a VTOL UAV when compared to a fixed-wing UAV is its reduced flight endurance. AUMS addresses this disadvantage by providing forward staging, refueling, and recovery capabilities for the VTOL UAV through a host unmanned ground vehicle (UGV), which serves as a launch/recovery platform and service station. The UGV has sufficient payload capacity to carry UAV fuel for multiple launch, recovery, and refuel iterations. The UGV also provides a highly mobile means of forward deploying a small UAV into hazardous areas unsafe for personnel, such as chemically or biologically contaminated areas. Teaming small UAVs with large UGVs can decrease risk to personnel and expand mission capabilities and effectiveness. There are numerous technical challenges being addressed by these development efforts. Among the challenges is the development and integration of a precision landing system compact and light enough to allow it to be mounted on a small VTOL UAV while providing repeatable landing accuracy to safely land on the AUMS. Another challenge is the design of a UGV-transportable, expandable, self-centering landing pad that contains hardware and safety devices for automatically refueling the UAV. A third challenge is making the design flexible enough to accommodate different types of VTOL UAVs, such as the AAI iSTAR ducted-fan vehicle and small helicopter UAVs. Finally, a common command-and-control architecture which supports the UAV, UGV, and AUMS must be developed and interfaced with these systems to allow fully autonomous collaborative behaviors. Funded by the Joint Robotics Program, AUMS is part of a joint effort with the Air Force Research Laboratory and the Army Missile Research Development and Engineering Command. The objective is to develop and demonstrate UGVUAV teaming concepts and work with the warfighter to ensure that future upgrades are focused on operational requirements. This paper describes the latest achievements in AUMS development and some of the military program and first responder situations that could benefit from this system.

Mission-Oriented Sensor Arrays and UAVs - a Case Study on Environmental Monitoring

NASA Astrophysics Data System (ADS)

Figueira, N. M.; Freire, I. L.; Trindade, O.; Simões, E.

2015-08-01

This paper presents a new concept of UAV mission design in geomatics, applied to the generation of thematic maps for a multitude of civilian and military applications. We discuss the architecture of Mission-Oriented Sensors Arrays (MOSA), proposed in Figueira et Al. (2013), aimed at splitting and decoupling the mission-oriented part of the system (non safety-critical hardware and software) from the aircraft control systems (safety-critical). As a case study, we present an environmental monitoring application for the automatic generation of thematic maps to track gunshot activity in conservation areas. The MOSA modeled for this application integrates information from a thermal camera and an on-the-ground microphone array. The use of microphone arrays technology is of particular interest in this paper. These arrays allow estimation of the direction-of-arrival (DOA) of the incoming sound waves. Information about events of interest is obtained by the fusion of the data provided by the microphone array, captured by the UAV, fused with information from the termal image processing. Preliminary results show the feasibility of the on-the-ground sound processing array and the simulation of the main processing module, to be embedded into an UAV in a future work. The main contributions of this paper are the proposed MOSA system, including concepts, models and architecture.

Design and implementation of atmospheric multi-parameter sensor for UAVs

NASA Astrophysics Data System (ADS)

Yu, F.; Zhao, Y.; Chen, G.; Liu, Y.; Han, Y.

2017-12-01

With the rapid development of industry and the increase of cars in developing countries, air pollutants have caused a series of environmental issues such as haze and smog. However, air pollution is a process of surface-to-air mass exchange, and various kinds of atmospheric factors have close association with aerosol concentration, such as temperature, humidity, etc. Vertical distributions of aerosol in the region provide an important clue to reveal the exchange mechanism in the atmosphere between atmospheric boundary layer and troposphere. Among the various kinds of flying platforms, unmanned aerial vehicles (UAVs) shows more advantages in vertical measurement of aerosol owned to its flexibility and low cost. However, only few sensors could be mounted on the UAVs because of the limited size and power requirement. Here, a light-weight, low-power atmospheric multi-parameter sensor (AMPS) is proposed and could be mounted on several kinds of UAV platforms. The AMPS integrates multi-sensors, which are the laser aerosol particle sensor, the temperature probe, the humidity probe and the pressure probe, in order to simultaneously sample the vertical distribution characters of aerosol particle concentration, temperature, relative humidity and atmospheric pressure. The data from the sensors are synchronized by a proposed communication mechanism based on GPS. Several kinds of housing are designed to accommodate the different payload requirements of UAVs in size and weight. The experiments were carried out with AMPS mounted on three kinds of flying platforms. The results shows that the power consumption is less than 1.3 W, with relatively high accuracy in temperature (±0.1°C), relative humidity (±0.8%RH), PM2.5 (<20%) and PM10 (<20%). Vertical profiles of PM2.5 and PM10 concentrations were observed simultaneously by the AMPS three times every day in five days. The results revealed the significant correlation between the aerosol particle concentration and atmospheric parameters. With low cost and flexibility, AMPS for UAVs provides an effective way to explore the properties of aerosol vertical distribution, and to monitor air pollutants flexibly.

Laser range profiling for small target recognition

NASA Astrophysics Data System (ADS)

Steinvall, Ove; Tulldahl, Michael

2016-05-01

The detection and classification of small surface and airborne targets at long ranges is a growing need for naval security. Long range ID or ID at closer range of small targets has its limitations in imaging due to the demand on very high transverse sensor resolution. It is therefore motivated to look for 1D laser techniques for target ID. These include vibrometry, and laser range profiling. Vibrometry can give good results but is also sensitive to certain vibrating parts on the target being in the field of view. Laser range profiling is attractive because the maximum range can be substantial, especially for a small laser beam width. A range profiler can also be used in a scanning mode to detect targets within a certain sector. The same laser can also be used for active imaging when the target comes closer and is angular resolved. The present paper will show both experimental and simulated results for laser range profiling of small boats out to 6-7 km range and a UAV mockup at close range (1.3 km). We obtained good results with the profiling system both for target detection and recognition. Comparison of experimental and simulated range waveforms based on CAD models of the target support the idea of having a profiling system as a first recognition sensor and thus narrowing the search space for the automatic target recognition based on imaging at close ranges. The naval experiments took place in the Baltic Sea with many other active and passive EO sensors beside the profiling system. Discussion of data fusion between laser profiling and imaging systems will be given. The UAV experiments were made from the rooftop laboratory at FOI.

Study of Lever-Arm Effect Using Embedded Photogrammetry and On-Board GPS Receiver on Uav for Metrological Mapping Purpose and Proposal of a Free Ground Measurements Calibration Procedure

NASA Astrophysics Data System (ADS)

Daakir, M.; Pierrot-Deseilligny, M.; Bosser, P.; Pichard, F.; Thom, C.; Rabot, Y.

2016-03-01

Nowadays, Unmanned Aerial Vehicle (UAV) on-board photogrammetry knows a significant growth due to the democratization of using drones in the civilian sector. Also, due to changes in regulations laws governing the rules of inclusion of a UAV in the airspace which become suitable for the development of professional activities. Fields of application of photogrammetry are diverse, for instance: architecture, geology, archaeology, mapping, industrial metrology, etc. Our research concerns the latter area. Vinci-Construction- Terrassement is a private company specialized in public earthworks that uses UAVs for metrology applications. This article deals with maximum accuracy one can achieve with a coupled camera and GPS receiver system for direct-georeferencing of Digital Surface Models (DSMs) without relying on Ground Control Points (GCPs) measurements. This article focuses specially on the lever-arm calibration part. This proposed calibration method is based on two steps: a first step involves the proper calibration for each sensor, i.e. to determine the position of the optical center of the camera and the GPS antenna phase center in a local coordinate system relative to the sensor. A second step concerns a 3d modeling of the UAV with embedded sensors through a photogrammetric acquisition. Processing this acquisition allows to determine the value of the lever-arm offset without using GCPs.

Pheromone-based coordination strategy to static sensors on the ground and unmanned aerial vehicles carried sensors

NASA Astrophysics Data System (ADS)

Pignaton de Freitas, Edison; Heimfarth, Tales; Pereira, Carlos Eduardo; Morado Ferreira, Armando; Rech Wagner, Flávio; Larsson, Tony

2010-04-01

A current trend that is gaining strength in the wireless sensor network area is the use of heterogeneous sensor nodes in one coordinated overall network, needed to fulfill the requirements of sophisticated emerging applications, such as area surveillance systems. One of the main concerns when developing such sensor networks is how to provide coordination among the heterogeneous nodes, in order to enable them to efficiently respond the user needs. This study presents an investigation of strategies to coordinate a set of static sensor nodes on the ground cooperating with wirelessly connected Unmanned Aerial Vehicles (UAVs) carrying a variety of sensors, in order to provide efficient surveillance over an area of interest. The sensor nodes on the ground are set to issue alarms on the occurrence of a given event of interest, e.g. entrance of a non-authorized vehicle in the area, while the UAVs receive the issued alarms and have to decide which of them is the most suitable to handle the issued alarm. A bio-inspired coordination strategy based on the concept of pheromones is presented. As a complement of this strategy, a utility-based decision making approach is proposed.

Dual-Stack Single-Radio Communication Architecture for UAV Acting As a Mobile Node to Collect Data in WSNs

PubMed Central

Sayyed, Ali; Medeiros de Araújo, Gustavo; Bodanese, João Paulo; Buss Becker, Leandro

2015-01-01

The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node. PMID:26389911

Dual-Stack Single-Radio Communication Architecture for UAV Acting As a Mobile Node to Collect Data in WSNs.

PubMed

Sayyed, Ali; de Araújo, Gustavo Medeiros; Bodanese, João Paulo; Becker, Leandro Buss

2015-09-16

The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.

Coupled sensor/platform control design for low-level chemical detection with position-adaptive micro-UAVs

NASA Astrophysics Data System (ADS)

Goodwin, Thomas; Carr, Ryan; Mitra, Atindra K.; Selmic, Rastko R.

2009-05-01

We discuss the development of Position-Adaptive Sensors [1] for purposes for detecting embedded chemical substances in challenging environments. This concept is a generalization of patented Position-Adaptive Radar Concepts developed at AFRL for challenging conditions such as urban environments. For purposes of investigating the detection of chemical substances using multiple MAV (Micro-UAV) platforms, we have designed and implemented an experimental testbed with sample structures such as wooden carts that contain controlled leakage points. Under this general concept, some of the members of a MAV swarm can serve as external position-adaptive "transmitters" by blowing air over the cart and some of the members of a MAV swarm can serve as external position-adaptive "receivers" that are equipped with chemical or biological (chem/bio) sensors that function as "electronic noses". The objective can be defined as improving the particle count of chem/bio concentrations that impinge on a MAV-based position-adaptive sensor that surrounds a chemical repository, such as a cart, via the development of intelligent position-adaptive control algorithms. The overall effect is to improve the detection and false-alarm statistics of the overall system. Within the major sections of this paper, we discuss a number of different aspects of developing our initial MAV-Based Sensor Testbed. This testbed includes blowers to simulate position-adaptive excitations and a MAV from Draganfly Innovations Inc. with stable design modifications to accommodate our chem/bio sensor boom design. We include details with respect to several critical phases of the development effort including development of the wireless sensor network and experimental apparatus, development of the stable sensor boom for the MAV, integration of chem/bio sensors and sensor node onto the MAV and boom, development of position-adaptive control algorithms and initial tests at IDCAST (Institute for the Development and Commercialization of Advanced Sensor Technologies), and autonomous positionadaptive chem/bio tests and demos in the MAV Lab at AFRL Air Vehicles Directorate. For this particular MAV implementation of chem/bio sensors, we selected miniature Methane, Nitrogen Dioxide, and Carbon Monoxide sensors. To safely simulate the behavior of chem/bio substances in our laboratory environment, we used either cigarette smoke or incense. We present a set of concise parametric results along with visual demonstration of our new position-adaptive sensor capability. Two types of experiments were conducted: with sensor nodes screening the chemical contaminant (cigarette smoke or incense) without MAVs, and with a sensor node integrated with the MAV. It was shown that the MOS-based chemical sensors could be used for chemical leakage detection, as well as for position-adaptive sensors on air/ground vehicles as sniffers for chemical contaminants.

Semiautonomous Avionics-and-Sensors System for a UAV

NASA Technical Reports Server (NTRS)

Shams, Qamar

2006-01-01

Unmanned Aerial Vehicles (UAVs) autonomous or remotely controlled pilotless aircraft have been recently thrust into the spotlight for military applications, for homeland security, and as test beds for research. In addition to these functions, there are many space applications in which lightweight, inexpensive, small UAVS can be used e.g., to determine the chemical composition and other qualities of the atmospheres of remote planets. Moreover, on Earth, such UAVs can be used to obtain information about weather in various regions; in particular, they can be used to analyze wide-band acoustic signals to aid in determining the complex dynamics of movement of hurricanes. The Advanced Sensors and Electronics group at Langley Research Center has developed an inexpensive, small, integrated avionics-and-sensors system to be installed in a UAV that serves two purposes. The first purpose is to provide flight data to an AI (Artificial Intelligence) controller as part of an autonomous flight-control system. The second purpose is to store data from a subsystem of distributed MEMS (microelectromechanical systems) sensors. Examples of these MEMS sensors include humidity, temperature, and acoustic sensors, plus chemical sensors for detecting various vapors and other gases in the environment. The critical sensors used for flight control are a differential- pressure sensor that is part of an apparatus for determining airspeed, an absolute-pressure sensor for determining altitude, three orthogonal accelerometers for determining tilt and acceleration, and three orthogonal angular-rate detectors (gyroscopes). By using these eight sensors, it is possible to determine the orientation, height, speed, and rates of roll, pitch, and yaw of the UAV. This avionics-and-sensors system is shown in the figure. During the last few years, there has been rapid growth and advancement in the technological disciplines of MEMS, of onboard artificial-intelligence systems, and of smaller, faster, and smarter wireless telemetry systems. The major attraction of MEMS lies in orders-of-magnitude reductions of power requirements relative to traditional electronic components that perform equivalent functions. In addition, the compactness of MEMS, relative to functionally equivalent traditional electronics systems, makes MEMS attractive for UAV applications. Recent advances in MEMS have made it possible to produce pressure, acceleration, humidity, and temperature sensors having masses in subgram range and possessing sensitivities and accuracies comparable to those of larger devices.

Formation Flying for Satellites and Unmanned Aerial Vehicles

NASA Technical Reports Server (NTRS)

Merrill, Garrick

2015-01-01

The shrinking size of satellites and unmanned aerial vehicles (UAVs) is enabling lower cost missions. As sensors and electronics continue to downsize, the next step is multiple vehicles providing different perspectives or variations for more precise measurements. While flying a single satellite or UAV autonomously is a challenge, flying multiple vehicles in a precise formation is even more challenging. The goal of this project is to develop a scalable mesh network between vehicles (satellites or UAVs) to share real-time position data and maintain formations autonomously. Newly available low-cost, commercial off-the-shelf credit card size computers will be used as the basis for this network. Mesh networking techniques will be used to provide redundant links and a flexible network. The Small Projects Rapid Integration and Test Environment Lab will be used to simulate formation flying of satellites. UAVs built by the Aero-M team will be used to demonstrate the formation flying in the West Test Area. The ability to test in flight on NASA-owned UAVs allows this technology to achieve a high Technology Readiness Level (TRL) (TRL-4 for satellites and TRL-7 for UAVs). The low cost of small UAVs and the availability of a large test range (West Test Area) dramatically reduces the expense of testing. The end goal is for this technology to be ready to use on any multiple satellite or UAV mission.

A UAV-based gas sensing system for detecting fugitive methane emissions

NASA Astrophysics Data System (ADS)

Hugenholtz, C.; Barchyn, T.; Myshak, S.; Bauer, J.

2016-12-01

Methane is one of the most prevalent greenhouse gases emitted by human activities and is a major component of government-led initiatives to reduce GHG emissions in Canada, the USA, and elsewhere. In light of growing demand for measurements and verification of atmospheric methane concentration across the oil and gas supply chain, an autonomous airborne gas sensing system was developed that combines a small UAV and a lightweight gas monitor. This paper outlines the technology, analytics, and presents data from a case study to demonstrate the proof of concept. The UAV is a fixed-wing (2.2 m wingspan), battery-operated platform, with a flight endurance of 80-120 minutes. The gas sensor onboard the UAV is a tunable diode laser absorption spectrometer that uses an integrated transmitter/receiver unit and a remote, passive retro-reflector. The transmitter is attached to one of the winglets, while the other is coated with reflective material. The total weight of the UAV and gas sensor is 4.3 kg. During flight, the system operates autonomously, acquiring averages of raw measurements at 1 Hz, with a recorded resolution of 0.0455 ppm. The onboard measurement and control unit (MCU) for the gas sensor is integrated with the UAV autopilot in order to provide time-stamped and geotagged concentration measurements, and to provide real-time flight adjustments when concentration exceeds a pre-determined threshold. The data are retrieved from the MCU when the mission is complete. In order to demonstrate the proof of concept, we present results from a case study and outline opportunities for translating the measurements into decision making.

Field determination of multipollutant, open area combustion source emission factors with a hexacopter unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Aurell, J.; Mitchell, W.; Chirayath, V.; Jonsson, J.; Tabor, D.; Gullett, B.

2017-10-01

An emission sensor/sampler system was coupled to a National Aeronautics and Space Administration (NASA) hexacopter unmanned aerial vehicle (UAV) to characterize gases and particles in the plumes emitted from open burning of military ordnance. The UAV/sampler was tested at two field sites with test and sampling flights spanning over 16 h of flight time. The battery-operated UAV was remotely maneuvered into the plumes at distances from the pilot of over 600 m and at altitudes of up to 122 m above ground level. While the flight duration could be affected by sampler payload (3.2-4.6 kg) and meteorological conditions, the 57 sampling flights, ranging from 4 to 12 min, were typically terminated when the plume concentrations of CO2 were diluted to near ambient levels. Two sensor/sampler systems, termed ;Kolibri,; were variously configured to measure particulate matter, metals, chloride, perchlorate, volatile organic compounds, chlorinated dioxins/furans, and nitrogen-based organics for determination of emission factors. Gas sensors were selected based on their applicable concentration range, light weight, freedom from interferents, and response/recovery times. Samplers were designed, constructed, and operated based on U.S. Environmental Protection Agency (EPA) methods and quality control criteria. Results show agreement with published emission factors and good reproducibility (e.g., 26% relative standard deviation for PM2.5). The UAV/Kolibri represents a significant advance in multipollutant emission characterization capabilities for open area sources, safely and effectively making measurements heretofore deemed too hazardous for personnel or beyond the reach of land-based samplers.

Characteristic analysis on UAV-MIMO channel based on normalized correlation matrix.

PubMed

Gao, Xi jun; Chen, Zi li; Hu, Yong Jiang

2014-01-01

Based on the three-dimensional GBSBCM (geometrically based double bounce cylinder model) channel model of MIMO for unmanned aerial vehicle (UAV), the simple form of UAV space-time-frequency channel correlation function which includes the LOS, SPE, and DIF components is presented. By the methods of channel matrix decomposition and coefficient normalization, the analytic formula of UAV-MIMO normalized correlation matrix is deduced. This formula can be used directly to analyze the condition number of UAV-MIMO channel matrix, the channel capacity, and other characteristic parameters. The simulation results show that this channel correlation matrix can be applied to describe the changes of UAV-MIMO channel characteristics under different parameter settings comprehensively. This analysis method provides a theoretical basis for improving the transmission performance of UAV-MIMO channel. The development of MIMO technology shows practical application value in the field of UAV communication.

Characteristic Analysis on UAV-MIMO Channel Based on Normalized Correlation Matrix

PubMed Central

Xi jun, Gao; Zi li, Chen; Yong Jiang, Hu

2014-01-01

Based on the three-dimensional GBSBCM (geometrically based double bounce cylinder model) channel model of MIMO for unmanned aerial vehicle (UAV), the simple form of UAV space-time-frequency channel correlation function which includes the LOS, SPE, and DIF components is presented. By the methods of channel matrix decomposition and coefficient normalization, the analytic formula of UAV-MIMO normalized correlation matrix is deduced. This formula can be used directly to analyze the condition number of UAV-MIMO channel matrix, the channel capacity, and other characteristic parameters. The simulation results show that this channel correlation matrix can be applied to describe the changes of UAV-MIMO channel characteristics under different parameter settings comprehensively. This analysis method provides a theoretical basis for improving the transmission performance of UAV-MIMO channel. The development of MIMO technology shows practical application value in the field of UAV communication. PMID:24977185

Current development of UAV sense and avoid system

NASA Astrophysics Data System (ADS)

Zhahir, A.; Razali, A.; Mohd Ajir, M. R.

2016-10-01

As unmanned aerial vehicles (UAVs) are now gaining high interests from civil and commercialised market, the automatic sense and avoid (SAA) system is currently one of the essential features in research spotlight of UAV. Several sensor types employed in current SAA research and technology of sensor fusion that offers a great opportunity in improving detection and tracking system are presented here. The purpose of this paper is to provide an overview of SAA system development in general, as well as the current challenges facing UAV researchers and designers.

Security Engineering Project

DTIC Science & Technology

2015-01-31

from a wireless joystick console broadcasting at 2.4 GHz. Figure 6. GTRI Airborne Unmanned Sensor System As shown in Figure 7 the autopilot has a...generating wind turbines , and video reconnaissance systems on unmanned aerial vehicles (UAVs). The most basic decision problem in designing a...chosen test UAV case was the GTRI Aerial Unmanned Sensor System (GAUSS) aircraft. The GAUSS platform is a small research UAV with a widely used

Lidar on small UAV for 3D mapping

NASA Astrophysics Data System (ADS)

Tulldahl, H. Michael; Larsson, Hâkan

2014-10-01

Small UAV:s (Unmanned Aerial Vehicles) are currently in an explosive technical development phase. The performance of UAV-system components such as inertial navigation sensors, propulsion, control processors and algorithms are gradually improving. Simultaneously, lidar technologies are continuously developing in terms of reliability, accuracy, as well as speed of data collection, storage and processing. The lidar development towards miniature systems with high data rates has, together with recent UAV development, a great potential for new three dimensional (3D) mapping capabilities. Compared to lidar mapping from manned full-size aircraft a small unmanned aircraft can be cost efficient over small areas and more flexible for deployment. An advantage with high resolution lidar compared to 3D mapping from passive (multi angle) photogrammetry is the ability to penetrate through vegetation and detect partially obscured targets. Another advantage is the ability to obtain 3D data over the whole survey area, without the limited performance of passive photogrammetry in low contrast areas. The purpose of our work is to demonstrate 3D lidar mapping capability from a small multirotor UAV. We present the first experimental results and the mechanical and electrical integration of the Velodyne HDL-32E lidar on a six-rotor aircraft with a total weight of 7 kg. The rotating lidar is mounted at an angle of 20 degrees from the horizontal plane giving a vertical field-of-view of 10-50 degrees below the horizon in the aircraft forward directions. For absolute positioning of the 3D data, accurate positioning and orientation of the lidar sensor is of high importance. We evaluate the lidar data position accuracy both based on inertial navigation system (INS) data, and on INS data combined with lidar data. The INS sensors consist of accelerometers, gyroscopes, GPS, magnetometers, and a pressure sensor for altimetry. The lidar range resolution and accuracy is documented as well as the capability for target surface reflectivity estimation based on measurements on calibration standards. Initial results of the general mapping capability including the detection through partly obscured environments is demonstrated through field data collection and analysis.

Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard

PubMed Central

Seo, Seung-Hyun; Choi, Jung-In; Song, Jinseok

2017-01-01

An intelligent emergency system for hazard monitoring and building evacuation is a very important application area in Internet of Things (IoT) technology. Through the use of smart sensors, such a system can provide more vital and reliable information to first-responders and also reduce the incidents of false alarms. Several smart monitoring and warning systems do already exist, though they exhibit key weaknesses such as a limited monitoring coverage and security, which have not yet been sufficiently addressed. In this paper, we propose a monitoring and emergency response method for buildings by utilizing beacons and Unmanned Aerial Vehicles (UAVs) on an IoT security platform. In order to demonstrate the practicability of our method, we also implement a proof of concept prototype, which we call the UAV-EMOR (UAV-assisted Emergency Monitoring and Response) system. Our UAV-EMOR system provides the following novel features: (1) secure communications between UAVs, smart sensors, the control server and a smartphone app for security managers; (2) enhanced coordination between smart sensors and indoor/outdoor UAVs to expand real-time monitoring coverage; and (3) beacon-aided rescue and building evacuation. PMID:28946659

Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard.

PubMed

Seo, Seung-Hyun; Choi, Jung-In; Song, Jinseok

2017-09-25

An intelligent emergency system for hazard monitoring and building evacuation is a very important application area in Internet of Things (IoT) technology. Through the use of smart sensors, such a system can provide more vital and reliable information to first-responders and also reduce the incidents of false alarms. Several smart monitoring and warning systems do already exist, though they exhibit key weaknesses such as a limited monitoring coverage and security, which have not yet been sufficiently addressed. In this paper, we propose a monitoring and emergency response method for buildings by utilizing beacons and Unmanned Aerial Vehicles (UAVs) on an IoT security platform. In order to demonstrate the practicability of our method, we also implement a proof of concept prototype, which we call the UAV-EMOR (UAV-assisted Emergency Monitoring and Response) system. Our UAV-EMOR system provides the following novel features: (1) secure communications between UAVs, smart sensors, the control server and a smartphone app for security managers; (2) enhanced coordination between smart sensors and indoor/outdoor UAVs to expand real-time monitoring coverage; and (3) beacon-aided rescue and building evacuation.

[Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology.

PubMed

Sun, Zhong Yu; Chen, Yan Qiao; Yang, Long; Tang, Guang Liang; Yuan, Shao Xiong; Lin, Zhi Wen

2017-02-01

Low-altitude unmanned aerial vehicles (UAV) remote sensing system overcomes the deficiencies of space and aerial remote sensing system in resolution, revisit period, cloud cover and cost, which provides a novel method for ecological research on mesoscale. This study introduced the composition of UAV remote sensing system, reviewed its applications in species, population, community and ecosystem ecology research. Challenges and opportunities of UAV ecology were identified to direct future research. The promising research area of UAV ecology includes the establishment of species morphology and spectral characteristic data base, species automatic identification, the revelation of relationship between spectral index and plant physiological processes, three-dimension monitoring of ecosystem, and the integration of remote sensing data from multi resources and multi scales. With the development of UAV platform, data transformation and sensors, UAV remote sensing technology will have wide application in ecology research.

Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs.

NASA Astrophysics Data System (ADS)

Hawary, A. F.; Razak, N. A.

2018-05-01

Whilst UAV offers a potentially cheaper and more localized observation platform than current satellite or land-based approaches, it requires an advance path planner to reveal its true potential, particularly in real-time missions. Manual control by human will have limited line-of-sights and prone to errors due to careless and fatigue. A good alternative solution is to equip the UAV with semi-autonomous capabilities that able to navigate via a pre-planned route in real-time fashion. In this paper, we propose an easy-and-practical path optimizer based on the classical Travelling Salesman Problem and adopts a brute force search method to re-optimize the route in the event of collisions using range finder sensor. The former utilizes a Simple Genetic Algorithm and the latter uses Nearest Neighbour algorithm. Both algorithms are combined to optimize the route and avoid collision at once. Although many researchers proposed various path planning algorithms, we find that it is difficult to integrate on a basic UAV model and often lacks of real-time collision detection optimizer. Therefore, we explore a practical benefit from this approach using on-board Arduino and Ardupilot controllers by manually emulating the motion of an actual UAV model prior to test on the flying site. The result showed that the range finder sensor provides a real-time data to the algorithm to find a collision-free path and eventually optimized the route successfully.

Generic Helicopter-Based Testbed for Surface Terrain Imaging Sensors

NASA Technical Reports Server (NTRS)

Alexander, James; Goldberg, Hannah; Montgomery, James; Spiers, Gary; Liebe, Carl; Johnson, Andrew; Gromov, Konstantin; Konefat, Edward; Lam, Raymond; Meras, Patrick

2008-01-01

To be certain that a candidate sensor system will perform as expected during missions, we have developed a field test system and have executed test flights with a helicopter-mounted sensor platform over desert terrains, which simulate Lunar features. A key advantage to this approach is that different sensors can be tested and characterized in an environment relevant to the flight needs prior to flight. Testing the various sensors required the development of a field test system, including an instrument to validate the truth of the sensor system under test. The field test system was designed to be flexible enough to cover the test needs of many sensors (lidar, radar, cameras) that require an aerial test platform, including helicopters, airplanes, unmanned aerial vehicles (UAV), or balloons. To validate the performance of the sensor under test, the dynamics of the test platform must be known with sufficient accuracy to provide accurate models for input into algorithm development. The test system provides support equipment to measure the dynamics of the field test sensor platform, and allow computation of the truth position, velocity, attitude, and time.

Accuracy evaluation of 3D lidar data from small UAV

NASA Astrophysics Data System (ADS)

Tulldahl, H. M.; Bissmarck, Fredrik; Larsson, Hâkan; Grönwall, Christina; Tolt, Gustav

2015-10-01

A UAV (Unmanned Aerial Vehicle) with an integrated lidar can be an efficient system for collection of high-resolution and accurate three-dimensional (3D) data. In this paper we evaluate the accuracy of a system consisting of a lidar sensor on a small UAV. High geometric accuracy in the produced point cloud is a fundamental qualification for detection and recognition of objects in a single-flight dataset as well as for change detection using two or several data collections over the same scene. Our work presented here has two purposes: first to relate the point cloud accuracy to data processing parameters and second, to examine the influence on accuracy from the UAV platform parameters. In our work, the accuracy is numerically quantified as local surface smoothness on planar surfaces, and as distance and relative height accuracy using data from a terrestrial laser scanner as reference. The UAV lidar system used is the Velodyne HDL-32E lidar on a multirotor UAV with a total weight of 7 kg. For processing of data into a geographically referenced point cloud, positioning and orientation of the lidar sensor is based on inertial navigation system (INS) data combined with lidar data. The combination of INS and lidar data is achieved in a dynamic calibration process that minimizes the navigation errors in six degrees of freedom, namely the errors of the absolute position (x, y, z) and the orientation (pitch, roll, yaw) measured by GPS/INS. Our results show that low-cost and light-weight MEMS based (microelectromechanical systems) INS equipment with a dynamic calibration process can obtain significantly improved accuracy compared to processing based solely on INS data.

The Altus Cumulus Electrification Study (ACES): A UAV-Based Science Demonstration

NASA Technical Reports Server (NTRS)

Blakeslee, R. J.; Croskey, C. L.; Desch, M. D.; Farrell, W. M.; Goldberg, R. A.; Houser, J. G.; Kim, H. S.; Mach, D. M.; Mitchell, J. D.; Stoneburner, J. C.

2003-01-01

The Altus Cumulus Electrification Study (ACES) is an unmanned aerial vehicle (UAV)- based project that investigated thunderstorms in the vicinity of the Florida Everglades in August 2002. ACES was conducted to investigate storm electrical activity and its relationship to storm morphology, and to validate satellite-based lightning measurements. In addition, as part of the NASA sponsored UAV-based science demonstration program, this project provided a scientifically useful demonstration of the utility and promise of UAV platforms for Earth science and applications observations. ACES employed the Altus II aircraft, built by General Atomics - Aeronautical Systems, Inc. Key science objectives simultaneously addressed by ACES are to: (1) investigate lightning-storm relationships, (2) study storm electrical budgets, and provide Lightning Imaging Sensor validation. The ACES payload included electrical, magnetic, and optical sensors to remotely characterize the lightning activity and the electrical environment within and around thunderstorms. ACES contributed important electrical and optical measurements not available from other sources. Also, the high altitude vantage point of the UAV observing platform (up to 55,000 feet) provided cloud-top perspective. By taking advantage of its slow flight speed (70 to 100 knots), long endurance, and high altitude flight, the Altus was flown near, and when possible, over (but never into) thunderstorms for long periods of time that allowed investigations to be conducted over entire storm life cycles. An innovative real time weather system was used to identify and vector the aircraft to selected thunderstorms and safely fly around these storms, while, at the same time monitor the weather near our base of operations. In addition, concurrent ground-based observations that included radar (Miami and Key West WSRBD, NASA NPOL), satellite imagery, and lightning (NALDN and Los Alamos EDOT) enable the UAV measurements to be more completely interpreted and evaluated in the context of the thunderstorm structure, evolution, and environment.

Stochastic global identification of a bio-inspired self-sensing composite UAV wing via wind tunnel experiments

NASA Astrophysics Data System (ADS)

Kopsaftopoulos, Fotios; Nardari, Raphael; Li, Yu-Hung; Wang, Pengchuan; Chang, Fu-Kuo

2016-04-01

In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bioinspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled air flow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow-structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system-level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly-by-fee" UAVs.

Cloud Water Content Sensor for Sounding Balloons and Small UAVs

NASA Technical Reports Server (NTRS)

Bognar, John A.

2009-01-01

A lightweight, battery-powered sensor was developed for measuring cloud water content, which is the amount of liquid or solid water present in a cloud, generally expressed as grams of water per cubic meter. This sensor has near-zero power consumption and can be flown on standard sounding balloons and small, unmanned aerial vehicles (UAVs). The amount of solid or liquid water is important to the study of atmospheric processes and behavior. Previous sensing techniques relied on strongly heating the incoming air, which requires a major energy input that cannot be achieved on sounding balloons or small UAVs.

Tracking with a Cooperatively Controlled Swarm of GMTI Equipped UAVS

DTIC Science & Technology

2008-12-02

with Ground Optimal Placement of GMTI UAVs for ground target tracking Abhijit Sinha°, Thia. Kirubarajana and Yaakov Bar-Shalom6 " Electrical and...Storrs, CT 06269, USA Abstract—With the recent advent of moderate-cost unmanned (or uninhabited) aerial vehicles (UAV) and their success in...the sensor platforms are mobile one has to decide the optimal placement of sensors. With the recent advent of af- fordable unmanned aerial vehicles

Vision Based Obstacle Detection in Uav Imaging

NASA Astrophysics Data System (ADS)

Badrloo, S.; Varshosaz, M.

2017-08-01

Detecting and preventing incidence with obstacles is crucial in UAV navigation and control. Most of the common obstacle detection techniques are currently sensor-based. Small UAVs are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are considered, which can be divided into stereo-based and mono-based techniques. Mono-based methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection; hence, this research aims to detect obstacles using brain-inspired techniques, which try to enlarge the obstacle by approaching it. A recent research in this field, has concentrated on matching the SIFT points along with, SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. This method is not able to distinguish between near and far obstacles or the obstacles in complex environment, and is sensitive to wrong matched points. In order to solve the above mentioned problems, this research calculates the dist-ratio of matched points. Then, each and every point is investigated for Distinguishing between far and close obstacles. The results demonstrated the high efficiency of the proposed method in complex environments.

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments.

PubMed

Ramon Soria, Pablo; Arrue, Begoña C; Ollero, Anibal

2017-01-07

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors.

Development of Open source-based automatic shooting and processing UAV imagery for Orthoimage Using Smart Camera UAV

NASA Astrophysics Data System (ADS)

Park, J. W.; Jeong, H. H.; Kim, J. S.; Choi, C. U.

2016-06-01

Recently, aerial photography with unmanned aerial vehicle (UAV) system uses UAV and remote controls through connections of ground control system using bandwidth of about 430 MHz radio Frequency (RF) modem. However, as mentioned earlier, existing method of using RF modem has limitations in long distance communication. The Smart Camera equipments's LTE (long-term evolution), Bluetooth, and Wi-Fi to implement UAV that uses developed UAV communication module system carried out the close aerial photogrammetry with the automatic shooting. Automatic shooting system is an image capturing device for the drones in the area's that needs image capturing and software for loading a smart camera and managing it. This system is composed of automatic shooting using the sensor of smart camera and shooting catalog management which manages filmed images and information. Processing UAV imagery module used Open Drone Map. This study examined the feasibility of using the Smart Camera as the payload for a photogrammetric UAV system. The open soure tools used for generating Android, OpenCV (Open Computer Vision), RTKLIB, Open Drone Map.

GaN-based THz advanced quantum cascade lasers for manned and unmanned systems

NASA Astrophysics Data System (ADS)

Anwar, A. F. M.; Manzur, Tariq; Lefebvre, Kevin R.; Carapezza, Edward M.

2009-09-01

In recent years the use of Unmanned Autonomous Vehicles (UAV) has seen a wider range of applications. However, their applications are restricted due to (a) advanced integrated sensing and processing electronics and (b) limited energy storage or on-board energy generation to name a few. The availability of a wide variety of sensing elements, operating at room temperatures, provides a great degree of flexibility with an extended application domain. Though sensors responding to a variable spectrum of input excitations ranging from (a) chemical, (b) biological, (c) atmospheric, (d) magnetic and (e) visual/IR imaging have been implemented in UAVs, the use of THz as a technology has not been implemented due to the absence of systems operating at room temperature. The integration of multi-phenomenological onboard sensors on small and miniature unmanned air vehicles will dramatically impact the detection and processing of challenging targets, such as humans carrying weapons or wearing suicide bomb vests. Unmanned air vehicles have the potential of flying over crowds of people and quickly discriminating non-threat humans from treat humans. The state of the art in small and miniature UAV's has progressed to vehicles of less than 1 pound in weight but with payloads of only a fraction of a pound. Uncooled IR sensors, such as amorphous silicon and vanadium oxide microbolometers with MRT's of less than 70mK and requiring power of less than 250mW, are available for integration into small UAV's. These sensors are responsive only up to approximately 14 microns and do not favorably compare with THz imaging systems for remotely detecting and classifying concealed weapons and bombs. In the following we propose the use of THz GaN-based QCL operating at room temperature as a possible alternative.

Concept for Classifying Facade Elements Based on Material, Geometry and Thermal Radiation Using Multimodal Uav Remote Sensing

NASA Astrophysics Data System (ADS)

Ilehag, R.; Schenk, A.; Hinz, S.

2017-08-01

This paper presents a concept for classification of facade elements, based on the material and the geometry of the elements in addition to the thermal radiation of the facade with the usage of a multimodal Unmanned Aerial Vehicle (UAV) system. Once the concept is finalized and functional, the workflow can be used for energy demand estimations for buildings by exploiting existing methods for estimation of heat transfer coefficient and the transmitted heat loss. The multimodal system consists of a thermal, a hyperspectral and an optical sensor, which can be operational with a UAV. While dealing with sensors that operate in different spectra and have different technical specifications, such as the radiometric and the geometric resolution, the challenges that are faced are presented. Addressed are the different approaches of data fusion, such as image registration, generation of 3D models by performing image matching and the means for classification based on either the geometry of the object or the pixel values. As a first step towards realizing the concept, the result from a geometric calibration with a designed multimodal calibration pattern is presented.

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor.

PubMed

Nguyen, Phong Ha; Kim, Ki Wan; Lee, Young Won; Park, Kang Ryoung

2017-08-30

Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments.

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production

PubMed Central

Domingues Franceschini, Marston Héracles; Bartholomeus, Harm; van Apeldoorn, Dirk; Suomalainen, Juha; Kooistra, Lammert

2017-01-01

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm−2), leaf area index (RMSE = 0.67 m2·m−2), canopy chlorophyll (RMSE = 0.24 g·m−2) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm−2, 0.85 m2·m−2, 0.28 g·m−2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system. PMID:28629159

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production.

PubMed

Domingues Franceschini, Marston Héracles; Bartholomeus, Harm; van Apeldoorn, Dirk; Suomalainen, Juha; Kooistra, Lammert

2017-06-18

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm -2 ), leaf area index (RMSE = 0.67 m²·m -2 ), canopy chlorophyll (RMSE = 0.24 g·m -2 ) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm -2 , 0.85 m²·m -2 , 0.28 g·m -2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CI g provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system.

Laser range profiling for small target recognition

NASA Astrophysics Data System (ADS)

Steinvall, Ove; Tulldahl, Michael

2017-03-01

Long range identification (ID) or ID at closer range of small targets has its limitations in imaging due to the demand for very high-transverse sensor resolution. This is, therefore, a motivation to look for one-dimensional laser techniques for target ID. These include laser vibrometry and laser range profiling. Laser vibrometry can give good results, but is not always robust as it is sensitive to certain vibrating parts on the target being in the field of view. Laser range profiling is attractive because the maximum range can be substantial, especially for a small laser beam width. A range profiler can also be used in a scanning mode to detect targets within a certain sector. The same laser can also be used for active imaging when the target comes closer and is angularly resolved. Our laser range profiler is based on a laser with a pulse width of 6 ns (full width half maximum). This paper will show both experimental and simulated results for laser range profiling of small boats out to a 6 to 7-km range and a unmanned arrial vehicle (UAV) mockup at close range (1.3 km). The naval experiments took place in the Baltic Sea using many other active and passive electro-optical sensors in addition to the profiling system. The UAV experiments showed the need for a high-range resolution, thus we used a photon counting system in addition to the more conventional profiler used in the naval experiments. This paper shows the influence of target pose and range resolution on the capability of classification. The typical resolution (in our case 0.7 m) obtainable with a conventional range finder type of sensor can be used for large target classification with a depth structure over 5 to 10 m or more, but for smaller targets such as a UAV a high resolution (in our case 7.5 mm) is needed to reveal depth structures and surface shapes. This paper also shows the need for 3-D target information to build libraries for comparison of measured and simulated range profiles. At closer ranges, full 3-D images should be preferable.

Two-agent cooperative search using game models with endurance-time constraints

NASA Astrophysics Data System (ADS)

Sujit, P. B.; Ghose, Debasish

2010-07-01

In this article, the problem of two Unmanned Aerial Vehicles (UAVs) cooperatively searching an unknown region is addressed. The search region is discretized into hexagonal cells and each cell is assumed to possess an uncertainty value. The UAVs have to cooperatively search these cells taking limited endurance, sensor and communication range constraints into account. Due to limited endurance, the UAVs need to return to the base station for refuelling and also need to select a base station when multiple base stations are present. This article proposes a route planning algorithm that takes endurance time constraints into account and uses game theoretical strategies to reduce the uncertainty. The route planning algorithm selects only those cells that ensure the agent will return to any one of the available bases. A set of paths are formed using these cells which the game theoretical strategies use to select a path that yields maximum uncertainty reduction. We explore non-cooperative Nash, cooperative and security strategies from game theory to enhance the search effectiveness. Monte-Carlo simulations are carried out which show the superiority of the game theoretical strategies over greedy strategy for different look ahead step length paths. Within the game theoretical strategies, non-cooperative Nash and cooperative strategy perform similarly in an ideal case, but Nash strategy performs better than the cooperative strategy when the perceived information is different. We also propose a heuristic based on partitioning of the search space into sectors to reduce computational overhead without performance degradation.

Force Protection via UGV-UAV Collaboration: Development of Control Law for Vision Based Target Tracking on SUAV

DTIC Science & Technology

2007-12-01

Hardware - In - Loop , Piccolo, UAV, Unmanned Aerial Vehicle 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT...Maneuvering Target.......................... 35 C. HARDWARE - IN - LOOP SIMULATION............................................... 37 1. Hardware - In - Loop Setup...law as proposed in equation (23) is capable of tracking a maneuvering target. C. HARDWARE - IN - LOOP SIMULATION The intention of HIL simulation

Unmanned air vehicle: autonomous takeoff and landing

NASA Astrophysics Data System (ADS)

Lim, K. L.; Gitano-Briggs, Horizon Walker

2010-03-01

UAVs are increasing in popularity and sophistication due to the demonstrated performance which cannot be attained by manned aircraft1. These developments have been made possible by development of sensors, instrumentation, telemetry and controls during the last few decades. UAVs are now common in areas such as aerial observation and as communication relays3. Most UAVs, however, are still flown by a human pilot via remote control from a ground station. Even the existing autonomous UAVs often require a human pilot to handle the most difficult tasks of take off and landing2 (TOL). This is mainly because the navigation of the airplane requires observation, constant situational assessment and hours of experience from the pilot himself4. Therefore, an autonomous takeoff and landing system (TLS) for UAVs using a few practical design rules with various sensors, instrumentation, etc has been developed. This paper details the design and modeling of the UAV TLS. The model indicates that the UAV's TLS shows promising stability.

Unmanned air vehicle: autonomous takeoff and landing

NASA Astrophysics Data System (ADS)

Lim, K. L.; Gitano-Briggs, Horizon Walker

2009-12-01

UAVs are increasing in popularity and sophistication due to the demonstrated performance which cannot be attained by manned aircraft1. These developments have been made possible by development of sensors, instrumentation, telemetry and controls during the last few decades. UAVs are now common in areas such as aerial observation and as communication relays3. Most UAVs, however, are still flown by a human pilot via remote control from a ground station. Even the existing autonomous UAVs often require a human pilot to handle the most difficult tasks of take off and landing2 (TOL). This is mainly because the navigation of the airplane requires observation, constant situational assessment and hours of experience from the pilot himself4. Therefore, an autonomous takeoff and landing system (TLS) for UAVs using a few practical design rules with various sensors, instrumentation, etc has been developed. This paper details the design and modeling of the UAV TLS. The model indicates that the UAV's TLS shows promising stability.

Low cost infrared and near infrared sensors for UAVs

NASA Astrophysics Data System (ADS)

Aden, S. T.; Bialas, J. P.; Champion, Z.; Levin, E.; McCarty, J. L.

2014-11-01

Thermal remote sensing has a wide range of applications, though the extent of its use is inhibited by cost. Robotic and computer components are now widely available to consumers on a scale that makes thermal data a readily accessible resource. In this project, thermal imagery collected via a lightweight remote sensing Unmanned Aerial Vehicle (UAV) was used to create a surface temperature map for the purpose of providing wildland firefighting crews with a cost-effective and time-saving resource. The UAV system proved to be flexible, allowing for customized sensor packages to be designed that could include visible or infrared cameras, GPS, temperature sensors, and rangefinders, in addition to many data management options. Altogether, such a UAV system could be used to rapidly collect thermal and aerial data, with a geographic accuracy of less than one meter.

Experimental application of simulation tools for evaluating UAV video change detection

NASA Astrophysics Data System (ADS)

Saur, Günter; Bartelsen, Jan

2015-10-01

Change detection is one of the most important tasks when unmanned aerial vehicles (UAV) are used for video reconnaissance and surveillance. In this paper, we address changes on short time scale, i.e. the observations are taken within time distances of a few hours. Each observation is a short video sequence corresponding to the near-nadir overflight of the UAV above the interesting area and the relevant changes are e.g. recently added or removed objects. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are versatile objects like trees and compression or transmission artifacts. To enable the usage of an automatic change detection within an interactive workflow of an UAV video exploitation system, an evaluation and assessment procedure has to be performed. Large video data sets which contain many relevant objects with varying scene background and altering influence parameters (e.g. image quality, sensor and flight parameters) including image metadata and ground truth data are necessary for a comprehensive evaluation. Since the acquisition of real video data is limited by cost and time constraints, from our point of view, the generation of synthetic data by simulation tools has to be considered. In this paper the processing chain of Saur et al. (2014) [1] and the interactive workflow for video change detection is described. We have selected the commercial simulation environment Virtual Battle Space 3 (VBS3) to generate synthetic data. For an experimental setup, an example scenario "road monitoring" has been defined and several video clips have been produced with varying flight and sensor parameters and varying objects in the scene. Image registration and change mask extraction, both components of the processing chain, are applied to corresponding frames of different video clips. For the selected examples, the images could be registered, the modelled changes could be extracted and the artifacts of the image rendering considered as noise (slight differences of heading angles, disparity of vegetation, 3D parallax) could be suppressed. We conclude that these image data could be considered to be realistic enough to serve as evaluation data for the selected processing components. Future work will extend the evaluation to other influence parameters and may include the human operator for mission planning and sensor control.

Vision-Based SLAM System for Unmanned Aerial Vehicles

PubMed Central

Munguía, Rodrigo; Urzua, Sarquis; Bolea, Yolanda; Grau, Antoni

2016-01-01

The present paper describes a vision-based simultaneous localization and mapping system to be applied to Unmanned Aerial Vehicles (UAVs). The main contribution of this work is to propose a novel estimator relying on an Extended Kalman Filter. The estimator is designed in order to fuse the measurements obtained from: (i) an orientation sensor (AHRS); (ii) a position sensor (GPS); and (iii) a monocular camera. The estimated state consists of the full state of the vehicle: position and orientation and their first derivatives, as well as the location of the landmarks observed by the camera. The position sensor will be used only during the initialization period in order to recover the metric scale of the world. Afterwards, the estimated map of landmarks will be used to perform a fully vision-based navigation when the position sensor is not available. Experimental results obtained with simulations and real data show the benefits of the inclusion of camera measurements into the system. In this sense the estimation of the trajectory of the vehicle is considerably improved, compared with the estimates obtained using only the measurements from the position sensor, which are commonly low-rated and highly noisy. PMID:26999131

Research for new UAV capabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Canavan, G.H.; Leadabrand, R.

1996-07-01

This paper discusses research for new Unmanned Aerial Vehicles (UAV) capabilities. Findings indicate that UAV performance could be greatly enhanced by modest research. Improved sensors and communications enhance near term cost effectiveness. Improved engines, platforms, and stealth improve long term effectiveness.

System Considerations and Challendes in 3d Mapping and Modeling Using Low-Cost Uav Systems

NASA Astrophysics Data System (ADS)

Lari, Z.; El-Sheimy, N.

2015-08-01

In the last few years, low-cost UAV systems have been acknowledged as an affordable technology for geospatial data acquisition that can meet the needs of a variety of traditional and non-traditional mapping applications. In spite of its proven potential, UAV-based mapping is still lacking in terms of what is needed for it to become an acceptable mapping tool. In other words, a well-designed system architecture that considers payload restrictions as well as the specifications of the utilized direct geo-referencing component and the imaging systems in light of the required mapping accuracy and intended application is still required. Moreover, efficient data processing workflows, which are capable of delivering the mapping products with the specified quality while considering the synergistic characteristics of the sensors onboard, the wide range of potential users who might lack deep knowledge in mapping activities, and time constraints of emerging applications, are still needed to be adopted. Therefore, the introduced challenges by having low-cost imaging and georeferencing sensors onboard UAVs with limited payload capability, the necessity of efficient data processing techniques for delivering required products for intended applications, and the diversity of potential users with insufficient mapping-related expertise needs to be fully investigated and addressed by UAV-based mapping research efforts. This paper addresses these challenges and reviews system considerations, adaptive processing techniques, and quality assurance/quality control procedures for achievement of accurate mapping products from these systems.

Dynamic Distributed Cooperative Control of Multiple Heterogeneous Resources

DTIC Science & Technology

2012-10-01

of the UAVs to maximize the total sensor footprint over the region of interest. The algorithm utilized to solve this problem was based on sampling a...and moving obstacles. Obstacle positions were assumed known a priori. Kingston and Beard [22] presented an algorithm to keep moving UAVs equally spaced...Planning Algorithms , Cambridge University Press, 2006. 11. Ma, C. S. and Miller, R. H., “Mixed integer linear programming trajectory generation for

UAV path planning using artificial potential field method updated by optimal control theory

NASA Astrophysics Data System (ADS)

Chen, Yong-bo; Luo, Guan-chen; Mei, Yue-song; Yu, Jian-qiao; Su, Xiao-long

2016-04-01

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.

Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

PubMed Central

Yuan, Xuebing; Yu, Shuai; Zhang, Shengzhi; Wang, Guoping; Liu, Sheng

2015-01-01

Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path. PMID:25961384

Survey of computer vision technology for UVA navigation

NASA Astrophysics Data System (ADS)

Xie, Bo; Fan, Xiang; Li, Sijian

2017-11-01

Navigation based on computer version technology, which has the characteristics of strong independence, high precision and is not susceptible to electrical interference, has attracted more and more attention in the filed of UAV navigation research. Early navigation project based on computer version technology mainly applied to autonomous ground robot. In recent years, the visual navigation system is widely applied to unmanned machine, deep space detector and underwater robot. That further stimulate the research of integrated navigation algorithm based on computer version technology. In China, with many types of UAV development and two lunar exploration, the three phase of the project started, there has been significant progress in the study of visual navigation. The paper expounds the development of navigation based on computer version technology in the filed of UAV navigation research and draw a conclusion that visual navigation is mainly applied to three aspects as follows.(1) Acquisition of UAV navigation parameters. The parameters, including UAV attitude, position and velocity information could be got according to the relationship between the images from sensors and carrier's attitude, the relationship between instant matching images and the reference images and the relationship between carrier's velocity and characteristics of sequential images.(2) Autonomous obstacle avoidance. There are many ways to achieve obstacle avoidance in UAV navigation. The methods based on computer version technology ,including feature matching, template matching, image frames and so on, are mainly introduced. (3) The target tracking, positioning. Using the obtained images, UAV position is calculated by using optical flow method, MeanShift algorithm, CamShift algorithm, Kalman filtering and particle filter algotithm. The paper expounds three kinds of mainstream visual system. (1) High speed visual system. It uses parallel structure, with which image detection and processing are carried out at high speed. The system is applied to rapid response system. (2) The visual system of distributed network. There are several discrete image data acquisition sensor in different locations, which transmit image data to the node processor to increase the sampling rate. (3) The visual system combined with observer. The system combines image sensors with the external observers to make up for lack of visual equipment. To some degree, these systems overcome lacks of the early visual system, including low frequency, low processing efficiency and strong noise. In the end, the difficulties of navigation based on computer version technology in practical application are briefly discussed. (1) Due to the huge workload of image operation , the real-time performance of the system is poor. (2) Due to the large environmental impact , the anti-interference ability of the system is poor.(3) Due to the ability to work in a particular environment, the system has poor adaptability.

A distributed automatic target recognition system using multiple low resolution sensors

NASA Astrophysics Data System (ADS)

Yue, Zhanfeng; Lakshmi Narasimha, Pramod; Topiwala, Pankaj

2008-04-01

In this paper, we propose a multi-agent system which uses swarming techniques to perform high accuracy Automatic Target Recognition (ATR) in a distributed manner. The proposed system can co-operatively share the information from low-resolution images of different looks and use this information to perform high accuracy ATR. An advanced, multiple-agent Unmanned Aerial Vehicle (UAV) systems-based approach is proposed which integrates the processing capabilities, combines detection reporting with live video exchange, and swarm behavior modalities that dramatically surpass individual sensor system performance levels. We employ real-time block-based motion analysis and compensation scheme for efficient estimation and correction of camera jitter, global motion of the camera/scene and the effects of atmospheric turbulence. Our optimized Partition Weighted Sum (PWS) approach requires only bitshifts and additions, yet achieves a stunning 16X pixel resolution enhancement, which is moreover parallizable. We develop advanced, adaptive particle-filtering based algorithms to robustly track multiple mobile targets by adaptively changing the appearance model of the selected targets. The collaborative ATR system utilizes the homographies between the sensors induced by the ground plane to overlap the local observation with the received images from other UAVs. The motion of the UAVs distorts estimated homography frame to frame. A robust dynamic homography estimation algorithm is proposed to address this, by using the homography decomposition and the ground plane surface estimation.

Towards distributed ATR using subjective logic combination rules with a swarm of UAVs

NASA Astrophysics Data System (ADS)

O'Hara, Stephen; Simon, Michael; Zhu, Qiuming

2007-04-01

In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR) employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR). Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged in deriving a high probability of detection (P d) while maintaining a low probability of false alarms (P fa). Our hypothesis is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same scene can raise the P d and lower the P fa sufficiently in order to provide a cost-effective ATR capability. This capability can lead to practical implementations of autonomous, coordinated, multi-UAV operations. In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars. The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief statement. Our system considers how many frames in the observation interval support each potential classification. A definable function transforms the observational data into a belief value. The belief value, or opinion, represents the UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will have similar index values in the evidence cache. The evidential reasoning system combines observations of similar spatial indexes, discounting older observations based upon a parameterized information aging function. We employ Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all observations that an object of a given class exists in a given region.

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments

PubMed Central

Ramon Soria, Pablo; Arrue, Begoña C.; Ollero, Anibal

2017-01-01

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors. PMID:28067851

Feasibility of employing a smartphone as the payload in a photogrammetric UAV system

NASA Astrophysics Data System (ADS)

Kim, Jinsoo; Lee, Seongkyu; Ahn, Hoyong; Seo, Dongju; Park, Soyoung; Choi, Chuluong

2013-05-01

Smartphones can be operated in a 3G network environment at any time or location, and they also cost less than existing photogrammetric UAV systems, providing high-resolution images and 3D location and attitude data from a variety of built-in sensors. This study aims to assess the feasibility of using a smartphone as the payload for a photogrammetric UAV system. To carry out the assessment, a smartphone-based photogrammetric UAV system was developed and utilized to obtain image, location, and attitude data under both static and dynamic conditions. The accuracy of the location and attitude data obtained and sent by this system was then evaluated. The smartphone images were converted into ortho-images via image triangulation, which was carried out both with and without consideration of the interior orientation (IO) parameters determined by camera calibration. In the static experiment, when the IO parameters were taken into account, the triangulation results were less than 1.28 pixels (RMSE) for all smartphone types, an improvement of at least 47% compared with the case when IO parameters were not taken into account. In the dynamic experiment, on the other hand, the accuracy of smartphone image triangulation was not significantly improved by considering IO parameters. This was because the electronic rolling shutter within the complementary metal-oxide semiconductor (CMOS) sensor built into the smartphone and the actuator for the voice coil motor (VCM)-type auto-focusing affected by the vibration and the speed of the UAV, which is likely to have a negative effect on image-based digital elevation model (DEM) generation. However, considering that these results were obtained using a single smartphone, this suggests that a smartphone is not only feasible as the payload for a photogrammetric UAV system but it may also play a useful role when installed in existing UAV systems.

Robust all-source positioning of UAVs based on belief propagation

NASA Astrophysics Data System (ADS)

Chen, Xi; Gao, Wenyun; Wang, Jiabo

2013-12-01

For unmanned air vehicles (UAVs) to survive hostile operational environments, it is always preferable to utilize all wireless positioning sources available to fuse a robust position. While belief propagation is a well-established method for all source data fusion, it is not an easy job to handle all the mathematics therein. In this work, a comprehensive mathematical framework for belief propagation-based all-source positioning of UAVs is developed, taking wireless sources including Global Navigation Satellite Systems (GNSS) space vehicles, peer UAVs, ground control stations, and signal of opportunities. Based on the mathematical framework, a positioning algorithm named Belief propagation-based Opportunistic Positioning of UAVs (BOPU) is proposed, with an unscented particle filter for Bayesian approximation. The robustness of the proposed BOPU is evaluated by a fictitious scenario that a group of formation flying UAVs encounter GNSS countermeasures en route. Four different configurations of measurements availability are simulated. The results show that the performance of BOPU varies only slightly with different measurements availability.

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor

PubMed Central

Nguyen, Phong Ha; Kim, Ki Wan; Lee, Young Won; Park, Kang Ryoung

2017-01-01

Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments. PMID:28867775

UAV-Based Hyperspectral Remote Sensing for Precision Agriculture: Challenges and Opportunities

NASA Astrophysics Data System (ADS)

Angel, Y.; Parkes, S. D.; Turner, D.; Houborg, R.; Lucieer, A.; McCabe, M.

2017-12-01

Modern agricultural production relies on monitoring crop status by observing and measuring variables such as soil condition, plant health, fertilizer and pesticide effect, irrigation and crop yield. Managing all of these factors is a considerable challenge for crop producers. As such, providing integrated technological solutions that enable improved diagnostics of field condition to maximize profits, while minimizing environmental impacts, would be of much interest. Such challenges can be addressed by implementing remote sensing systems such as hyperspectral imaging to produce precise biophysical indicator maps across the various cycles of crop development. Recent progress in unmanned aerial vehicles (UAVs) have advanced traditional satellite-based capabilities, providing a capacity for high-spatial, spectral and temporal response. However, while some hyperspectral sensors have been developed for use onboard UAVs, significant investment is required to develop a system and data processing workflow that retrieves accurately georeferenced mosaics. Here we explore the use of a pushbroom hyperspectral camera that is integrated on-board a multi-rotor UAV system to measure the surface reflectance in 272 distinct spectral bands across a wavelengths range spanning 400-1000 nm, and outline the requirement for sensor calibration, integration onto a stable UAV platform enabling accurate positional data, flight planning, and development of data post-processing workflows for georeferenced mosaics. The provision of high-quality and geo-corrected imagery facilitates the development of metrics of vegetation health that can be used to identify potential problems such as production inefficiencies, diseases and nutrient deficiencies and other data-streams to enable improved crop management. Immense opportunities remain to be exploited in the implementation of UAV-based hyperspectral sensing (and its combination with other imaging systems) to provide a transferable and scalable integrated framework for crop growth monitoring and yield prediction. Here we explore some of the challenges and issues in translating the available technological capacity into a useful and useable image collection and processing flow-path that enables these potential applications to be better realized.

ALTUS Cumulus Electrification Study (ACES)

NASA Technical Reports Server (NTRS)

Kim, Tony; Blakeslee, Richard; Russell, Larry W. (Technical Monitor)

2002-01-01

The ALTUS Cumulus Electrification Study (ACES) is an uninhabited aerial vehicle (UAV)-based project that will investigate thunderstorms in the vicinity of the Florida Everglades in August 2002. ACES is being conducted to both investigate storm electrical activity and its relationship to storm morphology, and validate Tropical Rainfall Measurement Mission (TRMM) satellite measurements. In addition, as part of NASA's UAV-based science demonstration program, this project will provide a scientifically useful demonstration of the utility and promise of UAV platforms for Earth science and applications observations. Part of the demonstration involves getting approvals from the Federal Aviation Administration and the NASA airworthiness flight safety review board. ACES will employ the ALTUS II aircraft, built by General Atomics - Aeronautical Systems, Inc. Key science objectives simultaneously addressed by ACES are to: (1) investigate lightning-storm relationships, (2) study storm electrical budgets, and (3) provide Lightning Imaging Sensor validation. The ACES payload, already developed and flown on ALTUS, includes electrical, magnetic, and optical sensors to remotely characterize the lightning activity and the electrical environment within and around thunderstorms. ACES will contribute important electrical and optical measurements not available from other sources. Also, the high altitude vantage point of the UAV observing platform (up to 55,000 feet) offers a useful 'cloud-top' perspective. By taking advantage of its slow flight speed (70 to 100 knots), long endurance, and high altitude flight, the ALTUS will be flown near, and when possible, above (but never into) thunderstorms for long periods of time, allowing investigations to be conducted over entire storm life cycles. In addition, concurrent ground-based observations will enable the UAV measurements to be more completely interpreted and evaluated in the context of the thunderstorm structure, evolution, and environment.

Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks

PubMed Central

Kupssinskü, Lucas S.; T. Guimarães, Tainá; Koste, Emilie C.; da Silva, Juarez M.; de Souza, Laís V.; Oliverio, William F. M.; Jardim, Rogélio S.; Koch, Ismael É.; de Souza, Jonas G.; Mauad, Frederico F.

2018-01-01

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R2 values of greater than 0.60, consistent with literature values. PMID:29315219

Development and Integration of a Solar Powered Unmanned Aerial Vehicle and a Wireless Sensor Network to Monitor Greenhouse Gases

PubMed Central

Malaver, Alexander; Motta, Nunzio; Corke, Peter; Gonzalez, Felipe

2015-01-01

Measuring gases for environmental monitoring is a demanding task that requires long periods of observation and large numbers of sensors. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) currently represent the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialized gas sensing systems. This paper presents the development and integration of a WSN and an UAV powered by solar energy in order to enhance their functionality and broader their applications. A gas sensing system implementing nanostructured metal oxide (MOX) and non-dispersive infrared sensors was developed to measure concentrations of CH4 and CO2. Laboratory, bench and field testing results demonstrate the capability of UAV to capture, analyze and geo-locate a gas sample during flight operations. The field testing integrated ground sensor nodes and the UAV to measure CO2 concentration at ground and low aerial altitudes, simultaneously. Data collected during the mission was transmitted in real time to a central node for analysis and 3D mapping of the target gas. The results highlights the accomplishment of the first flight mission of a solar powered UAV equipped with a CO2 sensing system integrated with a WSN. The system provides an effective 3D monitoring and can be used in a wide range of environmental applications such as agriculture, bushfires, mining studies, zoology and botanical studies using a ubiquitous low cost technology. PMID:25679312

Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases.

PubMed

Malaver, Alexander; Motta, Nunzio; Corke, Peter; Gonzalez, Felipe

2015-02-11

Measuring gases for environmental monitoring is a demanding task that requires long periods of observation and large numbers of sensors. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) currently represent the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialized gas sensing systems. This paper presents the development and integration of a WSN and an UAV powered by solar energy in order to enhance their functionality and broader their applications. A gas sensing system implementing nanostructured metal oxide (MOX) and non-dispersive infrared sensors was developed to measure concentrations of CH4 and CO2. Laboratory, bench and field testing results demonstrate the capability of UAV to capture, analyze and geo-locate a gas sample during flight operations. The field testing integrated ground sensor nodes and the UAV to measure CO2 concentration at ground and low aerial altitudes, simultaneously. Data collected during the mission was transmitted in real time to a central node for analysis and 3D mapping of the target gas. The results highlights the accomplishment of the first flight mission of a solar powered UAV equipped with a CO2 sensing system integrated with a WSN. The system provides an effective 3D monitoring and can be used in a wide range of environmental applications such as agriculture, bushfires, mining studies, zoology and botanical studies using a ubiquitous low cost technology.

Derivation of high spatial resolution albedo from UAV digital imagery: application over the Greenland Ice Sheet

NASA Astrophysics Data System (ADS)

Ryan, Jonathan C.; Hubbard, Alun; Box, Jason E.; Brough, Stephen; Cameron, Karen; Cook, Joseph M.; Cooper, Matthew; Doyle, Samuel H.; Edwards, Arwyn; Holt, Tom; Irvine-Fynn, Tristram; Jones, Christine; Pitcher, Lincoln H.; Rennermalm, Asa K.; Smith, Laurence C.; Stibal, Marek; Snooke, Neal

2017-05-01

Measurements of albedo are a prerequisite for modelling surface melt across the Earth's cryosphere, yet available satellite products are limited in spatial and/or temporal resolution. Here, we present a practical methodology to obtain centimetre resolution albedo products with accuracies of 5% using consumer-grade digital camera and unmanned aerial vehicle (UAV) technologies. Our method comprises a workflow for processing, correcting and calibrating raw digital images using a white reference target, and upward and downward shortwave radiation measurements from broadband silicon pyranometers. We demonstrate the method with a set of UAV sorties over the western, K-sector of the Greenland Ice Sheet. The resulting albedo product, UAV10A1, covers 280 km2, at a resolution of 20 cm per pixel and has a root-mean-square difference of 3.7% compared to MOD10A1 and 4.9% compared to ground-based broadband pyranometer measurements. By continuously measuring downward solar irradiance, the technique overcomes previous limitations due to variable illumination conditions during and between surveys over glaciated terrain. The current miniaturization of multispectral sensors and incorporation of upward facing radiation sensors on UAV packages means that this technique will likely become increasingly attractive in field studies and used in a wide range of applications for high temporal and spatial resolution surface mapping of debris, dust, cryoconite and bioalbedo and for directly constraining surface energy balance models.

The Altus Cumulus Electrification Study (ACES): A UAV-based Investigation of Thunderstorms

NASA Technical Reports Server (NTRS)

Blakeslee, Richard; Arnold, James E. (Technical Monitor)

2001-01-01

The Altus Cumulus Electrification Study (ACES) is a NASA-sponsored and -led science investigation that utilizes an uninhabited aerial vehicle (UAV) to investigate thunderstorms in the vicinity of the NASA Kennedy Space Center, Florida. As part of NASA's UAV-based science demonstration program, ACES will provide a scientifically useful demonstration of the utility and promise of UAV platforms for Earth science and applications observations. ACES will employ the Altus 11 aircraft, built by General Atomics-Aeronautical Systems, Inc. By taking advantage of its slow flight speed (70 to 100 knots), long endurance, and high-altitude flight (up to 55,000 feet), the Altus will be flown near, and when possible, above (but never into) thunderstorms for long periods of time, allowing investigations to be conducted over entire storm life cycles. Key science objectives simultaneously addressed by ACES are to: (1) investigate lightning-storm relationships, (2) study storm electrical budgets, and (3) provide Lightning Imaging Sensor validation. The ACES payload, already developed and flown on Altus, includes electrical, magnetic, and optical sensors to remotely characterize the lightning activity and the electrical environment within and around thunderstorms. The ACES field campaign will be conducted during July 2002 with a goal of performing 8 to 10 UAV flights. Each flight will require about 4 to 5 hours on station at altitudes from 40,000 ft to 55,000 ft. The ACES team is comprised of scientists from the NASA Marshall Space Flight Center and NASA Goddard Space Flight Centers partnered with General Atomics and IDEA, LLC.

Ground Control Point - Wireless System Network for UAV-based environmental monitoring applications

NASA Astrophysics Data System (ADS)

Mejia-Aguilar, Abraham

2016-04-01

In recent years, Unmanned Aerial Vehicles (UAV) have seen widespread civil applications including usage for survey and monitoring services in areas such as agriculture, construction and civil engineering, private surveillance and reconnaissance services and cultural heritage management. Most aerial monitoring services require the integration of information acquired during the flight (such as imagery) with ground-based information (such as GPS information or others) for improved ground truth validation. For example, to obtain an accurate 3D and Digital Elevation Model based on aerial imagery, it is necessary to include ground-based information of coordinate points, which are normally acquired with surveying methods based on Global Position Systems (GPS). However, GPS surveys are very time consuming and especially for longer time series of monitoring data repeated GPS surveys are necessary. In order to improve speed of data collection and integration, this work presents an autonomous system based on Waspmote technologies build on single nodes interlinked in a Wireless Sensor Network (WSN) star-topology for ground based information collection and later integration with surveying data obtained by UAV. Nodes are designed to be visible from the air, to resist extreme weather conditions with low-power consumption. Besides, nodes are equipped with GPS as well as Inertial Measurement Unit (IMU), accelerometer, temperature and soil moisture sensors and thus provide significant advantages in a broad range of applications for environmental monitoring. For our purpose, the WSN transmits the environmental data with 3G/GPRS to a database on a regular time basis. This project provides a detailed case study and implementation of a Ground Control Point System Network for UAV-based vegetation monitoring of dry mountain grassland in the Matsch valley, Italy.

Meteorological and Remote Sensing Applications of High Altitude Unmanned Aerial Vehicles

NASA Technical Reports Server (NTRS)

Schoenung, S. M.; Wegener, S. S.

1999-01-01

Unmanned aerial vehicles (UAVs) are maturing in performance and becoming available for routine use in environmental applications including weather reconnaissance and remote sensing. This paper presents a discussion of UAV characteristics and unique features compared with other measurement platforms. A summary of potential remote sensing applications is provided, along with details for four types of tropical cyclone missions. Capabilities of platforms developed under NASA's Environmental Research Aircraft and Sensor Technology (ERAST) program are reviewed, including the Altus, Perseus, and solar- powered Pathfinder, all of which have flown to over 57,000 ft (17 km). In many scientific missions, the science objectives drive the experimental design, thus defining the sensor payload, aircraft performance, and operational requirements. Some examples of science missions and the requisite UAV / payload system are given. A discussion of technology developments needed to fully mature UAV systems for routine operational use is included, along with remarks on future science and commercial UAV business opportunities.

Common Approach to Geoprocessing of Uav Data across Application Domains

NASA Astrophysics Data System (ADS)

Percivall, G. S.; Reichardt, M.; Taylor, T.

2015-08-01

UAVs are a disruptive technology bringing new geographic data and information to many application domains. UASs are similar to other geographic imagery systems so existing frameworks are applicable. But the diversity of UAVs as platforms along with the diversity of available sensors are presenting challenges in the processing and creation of geospatial products. Efficient processing and dissemination of the data is achieved using software and systems that implement open standards. The challenges identified point to the need for use of existing standards and extending standards. Results from the use of the OGC Sensor Web Enablement set of standards are presented. Next steps in the progress of UAVs and UASs may follow the path of open data, open source and open standards.

Modeling and Optimization of Multiple Unmanned Aerial Vehicles System Architecture Alternatives

PubMed Central

Wang, Weiping; He, Lei

2014-01-01

Unmanned aerial vehicle (UAV) systems have already been used in civilian activities, although very limitedly. Confronted different types of tasks, multi UAVs usually need to be coordinated. This can be extracted as a multi UAVs system architecture problem. Based on the general system architecture problem, a specific description of the multi UAVs system architecture problem is presented. Then the corresponding optimization problem and an efficient genetic algorithm with a refined crossover operator (GA-RX) is proposed to accomplish the architecting process iteratively in the rest of this paper. The availability and effectiveness of overall method is validated using 2 simulations based on 2 different scenarios. PMID:25140328

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images

PubMed Central

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-01-01

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures. PMID:26007744

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

PubMed

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-05-22

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Calibration Assessment of Uncooled Thermal Cameras for Deployment on UAV platforms

NASA Astrophysics Data System (ADS)

Aragon, B.; Parkes, S. D.; Lucieer, A.; Turner, D.; McCabe, M.

2017-12-01

In recent years an array of miniaturized sensors have been developed and deployed on Unmanned Aerial Vehicles (UAVs). Prior to gaining useful data from these integrations, it is vitally important to quantify sensor accuracy, precision and cross-sensitivity of retrieved measurements on environmental variables. Small uncooled thermal frame cameras provide a novel solution to monitoring surface temperatures from UAVs with very high spatial resolution, with retrievals being used to investigate heat stress or evapotranspiration. For these studies, accuracies of a few degrees are generally required. Although radiometrically calibrated thermal cameras have recently become commercially available, confirmation of the accuracy of these sensors is required. Here we detail a system for investigating the accuracy and precision, start up stabilisation time, dependence of retrieved temperatures on ambient temperatures and image vignetting. The calibration system uses a relatively inexpensive blackbody source deployed with the sensor inside an environmental chamber to maintain and control the ambient temperature. Calibration of a number of different thermal sensors commonly used for UAV deployment was investigated. Vignetting was shown to be a major limitation on sensor accuracy, requiring characterization through measuring a spatially uniform temperature target such as the blackbody. Our results also showed that a stabilization period is required after powering on the sensors and before conducting an aerial survey. Through use of the environmental chamber it was shown the ambient temperature influenced the temperatures retrieved by the different sensors. This study illustrates the importance of determining the calibration and cross-sensitivities of thermal sensors to obtain accurate thermal maps that can be used to study crop ecosystems.

Numerical simulation of unmanned aerial vehicle under centrifugal load and optimization of milling and planing

NASA Astrophysics Data System (ADS)

Chen, Yunsheng; Lu, Xinghua

2018-05-01

The mechanical parts of the fuselage surface of the UAV are easily fractured by the action of the centrifugal load. In order to improve the compressive strength of UAV and guide the milling and planing of mechanical parts, a numerical simulation method of UAV fuselage compression under centrifugal load based on discrete element analysis method is proposed. The three-dimensional discrete element method is used to establish the splitting tensile force analysis model of the UAV fuselage under centrifugal loading. The micro-contact connection parameters of the UAV fuselage are calculated, and the yield tensile model of the mechanical components is established. The dynamic and static mechanical model of the aircraft fuselage milling is analyzed by the axial amplitude vibration frequency combined method. The correlation parameters of the cutting depth on the tool wear are obtained. The centrifugal load stress spectrum of the surface of the UAV is calculated. The meshing and finite element simulation of the rotor blade of the unmanned aerial vehicle is carried out to optimize the milling process. The test results show that the accuracy of the anti - compression numerical test of the UAV is higher by adopting the method, and the anti - fatigue damage capability of the unmanned aerial vehicle body is improved through the milling and processing optimization, and the mechanical strength of the unmanned aerial vehicle can be effectively improved.

Portable Imagery Quality Assessment Test Field for Uav Sensors

NASA Astrophysics Data System (ADS)

Dąbrowski, R.; Jenerowicz, A.

2015-08-01

Nowadays the imagery data acquired from UAV sensors are the main source of all data used in various remote sensing applications, photogrammetry projects and in imagery intelligence (IMINT) as well as in other tasks as decision support. Therefore quality assessment of such imagery is an important task. The research team from Military University of Technology, Faculty of Civil Engineering and Geodesy, Geodesy Institute, Department of Remote Sensing and Photogrammetry has designed and prepared special test field- The Portable Imagery Quality Assessment Test Field (PIQuAT) that provides quality assessment in field conditions of images obtained with sensors mounted on UAVs. The PIQuAT consists of 6 individual segments, when combined allow for determine radiometric, spectral and spatial resolution of images acquired from UAVs. All segments of the PIQuAT can be used together in various configurations or independently. All elements of The Portable Imagery Quality Assessment Test Field were tested in laboratory conditions in terms of their radiometry and spectral reflectance characteristics.

Geometric processing workflow for vertical and oblique hyperspectral frame images collected using UAV

NASA Astrophysics Data System (ADS)

Markelin, L.; Honkavaara, E.; Näsi, R.; Nurminen, K.; Hakala, T.

2014-08-01

Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology. UAVs enable accurate, flexible, low-cost and multiangular measurements of 3D geometric, radiometric, and temporal properties of land and vegetation using various sensors. In this paper we present a geometric processing chain for multiangular measurement system that is designed for measuring object directional reflectance characteristics in a wavelength range of 400-900 nm. The technique is based on a novel, lightweight spectral camera designed for UAV use. The multiangular measurement is conducted by collecting vertical and oblique area-format spectral images. End products of the geometric processing are image exterior orientations, 3D point clouds and digital surface models (DSM). This data is needed for the radiometric processing chain that produces reflectance image mosaics and multiangular bidirectional reflectance factor (BRF) observations. The geometric processing workflow consists of the following three steps: (1) determining approximate image orientations using Visual Structure from Motion (VisualSFM) software, (2) calculating improved orientations and sensor calibration using a method based on self-calibrating bundle block adjustment (standard photogrammetric software) (this step is optional), and finally (3) creating dense 3D point clouds and DSMs using Photogrammetric Surface Reconstruction from Imagery (SURE) software that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the image. We have tested the geometric processing workflow over various targets, including test fields, agricultural fields, lakes and complex 3D structures like forests.

Vision-Based Precision Landings of a Tailsitter UAV

DTIC Science & Technology

2010-04-01

2.2: Schematic of the controller used in simulation. The block diagram shown in Figure 2.2 shows the simulation structure used to simulate the vision...the structure of the flight facility walls, any vibration applied to the structure would potentially change the pose of the cameras. Each camera’s pose...relative to the target in Chap- ter 4, a flat earth assumption was made. In several situations the approximation that the ground over which the UAV is

Multi-UAV Supervisory Control Interface Technology (MUSCIT)

DTIC Science & Technology

2012-09-01

similar capability into the Vigilant Spirit Control Station ( VSCS ). During operations each vehicle is placed in its loiter mode. While in loiter mode...the vehicle will maintain a loiter over its designated loiter position. During pervious spirals, VSCS included a Loiter Slave mode where the sensor...available control station features. Prior to Spiral 3 simulation, VSCS developers had been working with Real Time Video Systems (RTVS) and had

Hardware Implementation of COTS Avionics System on Unmanned Aerial Vehicle Platforms

NASA Technical Reports Server (NTRS)

Yeh, Yoo-Hsiu; Kumar, Parth; Ishihara, Abraham; Ippolito, Corey

2010-01-01

Unmanned Aerial Vehicles (UAVs) can serve as low cost and low risk platforms for flight testing in Aeronautics research. The NASA Exploration Aerial Vehicle (EAV) and Experimental Sensor-Controlled Aerial Vehicle (X-SCAV) UAVs were developed in support of control systems research at NASA Ames Research Center. The avionics hardware for both systems has been redesigned and updated, and the structure of the EAV has been further strengthened. Preliminary tests show the avionics operate properly in the new configuration. A linear model for the EAV also was estimated from flight data, and was verified in simulation. These modifications and results prepare the EAV and X-SCAV to be used in a wide variety of flight research projects.

Integration of Multiple UAVs for Collaborative ISR Missions in an Urban Environment

DTIC Science & Technology

2012-09-01

URBAN ENVIRONMENT UAVS .............................3  1.  Qube UAS ......................................4  2.  SQ-4...93  INITIAL DISTRIBUTION LIST ...................................97  ix LIST OF FIGURES   Qube UAS by Aerovironment. From [7...laboratory setup. From [31].....................47    Sonar sensor....................................49 Figure 25.   Reflectors on the UAV

UAV State Estimation Modeling Techniques in AHRS

NASA Astrophysics Data System (ADS)

Razali, Shikin; Zhahir, Amzari

2017-11-01

Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.

Assisted Perception, Planning and Control for Remote Mobility and Dexterous Manipulation

DTIC Science & Technology

2017-04-01

on unmanned aerial vehicles (UAVs). The underlying algorithm is based on an Extended Kalman Filter (EKF) that simultaneously estimates robot state...and sensor biases. The filter developed provided a probabilistic fusion of sensor data from many modalities to produce a single consistent position...estimation for a walking humanoid. Given a prior map using a Gaussian particle filter , the LIDAR based system is able to provide a drift-free

UAV formation control design with obstacle avoidance in dynamic three-dimensional environment.

PubMed

Chang, Kai; Xia, Yuanqing; Huang, Kaoli

2016-01-01

This paper considers the artificial potential field method combined with rotational vectors for a general problem of multi-unmanned aerial vehicle (UAV) systems tracking a moving target in dynamic three-dimensional environment. An attractive potential field is generated between the leader and the target. It drives the leader to track the target based on the relative position of them. The other UAVs in the formation are controlled to follow the leader by the attractive control force. The repulsive force affects among the UAVs to avoid collisions and distribute the UAVs evenly on the spherical surface whose center is the leader-UAV. Specific orders or positions of the UAVs are not required. The trajectories of avoidance obstacle can be obtained through two kinds of potential field with rotation vectors. Every UAV can choose the optimal trajectory to avoid the obstacle and reconfigure the formation after passing the obstacle. Simulations study on UAV are presented to demonstrate the effectiveness of proposed method.

Power Management for Fuel Cell and Battery Hybrid Unmanned Aerial Vehicle Applications

NASA Astrophysics Data System (ADS)

Stein, Jared Robert

As electric powered unmanned aerial vehicles enter a new age of commercial viability, market opportunities in the small UAV sector are expanding. Extending UAV flight time through a combination of fuel cell and battery technologies enhance the scope of potential applications. A brief survey of UAV history provides context and examples of modern day UAVs powered by fuel cells are given. Conventional hybrid power system management employs DC-to-DC converters to control the power split between battery and fuel cell. In this study, a transistor replaces the DC-to-DC converter which lowers weight and cost. Simulation models of a lithium ion battery and a proton exchange membrane fuel cell are developed and integrated into a UAV power system model. Flight simulations demonstrate the operation of the transistor-based power management scheme and quantify the amount of hydrogen consumed by a 5.5 kg fixed wing UAV during a six hour flight. Battery power assists the fuel cell during high throttle periods but may also augment fuel cell power during cruise flight. Simulations demonstrate a 60 liter reduction in hydrogen consumption when battery power assists the fuel cell during cruise flight. Over the full duration of the flight, averaged efficiency of the power system exceeds 98%. For scenarios where inflight battery recharge is desirable, a constant current battery charger is integrated into the UAV power system. Simulation of inflight battery recharge is performed. Design of UAV hybrid power systems must consider power system weight against potential flight time. Data from the flight simulations are used to identify a simple formula that predicts flight time as a function of energy stored onboard the modeled UAV. A small selection of commercially available batteries, fuel cells, and compressed air storage tanks are listed to characterize the weight of possible systems. The formula is then used in conjunction with the weight data to generate a graph of power system weight versus potential flight times. Combinations of the listed batteries, fuel cells, and storage tanks are plotted on the graph to evaluate various hybrid power system configurations.

Autonomous agricultural remote sensing systems with high spatial and temporal resolutions

NASA Astrophysics Data System (ADS)

Xiang, Haitao

In this research, two novel agricultural remote sensing (RS) systems, a Stand-alone Infield Crop Monitor RS System (SICMRS) and an autonomous Unmanned Aerial Vehicles (UAV) based RS system have been studied. A high-resolution digital color and multi-spectral camera was used as the image sensor for the SICMRS system. An artificially intelligent (AI) controller based on artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) was developed. Morrow Plots corn field RS images in the 2004 and 2006 growing seasons were collected by the SICMRS system. The field site contained 8 subplots (9.14 m x 9.14 m) that were planted with corn and three different fertilizer treatments were used among those subplots. The raw RS images were geometrically corrected, resampled to 10cm resolution, removed soil background and calibrated to real reflectance. The RS images from two growing seasons were studied and 10 different vegetation indices were derived from each day's image. The result from the image processing demonstrated that the vegetation indices have temporal effects. To achieve high quality RS data, one has to utilize the right indices and capture the images at the right time in the growing season. Maximum variations among the image data set are within the V6-V10 stages, which indicated that these stages are the best period to identify the spatial variability caused by the nutrient stress in the corn field. The derived vegetation indices were also used to build yield prediction models via the linear regression method. At that point, all of the yield prediction models were evaluated by comparing the R2-value and the best index model from each day's image was picked based on the highest R 2-value. It was shown that the green normalized difference vegetation (GNDVI) based model is more sensitive to yield prediction than other indices-based models. During the VT-R4 stages, the GNDVI based models were able to explain more than 95% potential corn yield consistently for both seasons. The VT-R4 stages are the best period of time to estimate the corn yield. The SICMS system is only suitable for the RS research at a fixed location. In order to provide more flexibility of the RS image collection, a novel UAV based system has been studied. The UAV based agricultural RS system used a light helicopter platform equipped with a multi-spectral camera. The UAV control system consisted of an on-board and a ground station subsystem. For the on-board subsystem, an Extended Kalman Filter (EKF) based UAV navigation system was designed and implemented. The navigation system, using low cost inertial sensors, magnetometer, GPS and a single board computer, was capable of providing continuous estimates of UAV position and attitude at 50 Hz using sensor fusion techniques. The ground station subsystem was designed to be an interface between a human operator and the UAV to implement mission planning, flight command activation, and real-time flight monitoring. The navigation system is controlled by the ground station, and able to navigate the UAV in the air to reach the predefined waypoints and trigger the multi-spectral camera. By so doing, the aerial images at each point could be captured automatically. The developed UAV RS system can provide a maximum flexibility in crop field RS image collection. It is essential to perform the geometric correction and the geocoding before an aerial image can be used for precision farming. An automatic (no Ground Control Point (GCP) needed) UAV image georeferencing algorithm was developed. This algorithm can do the automatic image correction and georeferencing based on the real-time navigation data and a camera lens distortion model. The accuracy of the georeferencing algorithm was better than 90 cm according to a series test. The accuracy that has been achieved indicates that, not only is the position solution good, but the attitude error is extremely small. The waypoints planning for UAV flight was investigated. It suggested that a 16.5% forward overlap and a 15% lateral overlap were required to avoiding missing desired mapping area when the UAV flies above 45 m high with 4.5 mm lens. A whole field mosaic image can be generated according to the individual image georeferencing information. A 0.569 m mosaic error has been achieved and this accuracy is sufficient for many of the intended precision agricultural applications. With careful interpretation, the UAV images are an excellent source of high spatial and temporal resolution data for precision agricultural applications. (Abstract shortened by UMI.)

Experimental Measurement of Small Scale Multirotor Flows

NASA Astrophysics Data System (ADS)

Connors, Jacob; Weiner, Joseph; Velarde, John-Michael; Glauser, Mark

2017-11-01

Work is being done to create a multirotor Unmanned Air Vehicle (UAV) based anemometer system that would allow for measurement of velocity and spectra in the atmospheric boundary layer. The flow from the UAV's rotors will impact such measurements and hence must be filtered. This study focuses on measuring the fluctuations of the velocity field in the flow both above and below various UAVs to determine first, the feasibility of the creation of the filter, and second, the optimal placement of the system on the body of the UAV. These measurements are taking place in both Syracuse University's subsonic wind tunnel and Skytop Turbulence Lab's Indoor Flow Lab. Constant Temperature Anemometry is being used to measure these velocity field fluctuations across a variety of UAVs with differing characteristics such as size, number of propellers, and rotor blade type. The data from these experiments is being used to define a method to estimate the filter band required to isolate noise from wake effects, and determine ideal sensor placement based on characteristics of the vehicle's design alone. The authors would like to thank The Center for Advanced Systems and Engineering (CASE) at Syracuse University for funding and supporting this work.

Feasibility of Using Synthetic Aperture Radar to Aid UAV Navigation

PubMed Central

Nitti, Davide O.; Bovenga, Fabio; Chiaradia, Maria T.; Greco, Mario; Pinelli, Gianpaolo

2015-01-01

This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimate UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system. PMID:26225977

Feasibility of Using Synthetic Aperture Radar to Aid UAV Navigation.

PubMed

Nitti, Davide O; Bovenga, Fabio; Chiaradia, Maria T; Greco, Mario; Pinelli, Gianpaolo

2015-07-28

This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimated UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system.

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

PubMed Central

Al-Kaff, Abdulla; García, Fernando; Martín, David; De La Escalera, Arturo; Armingol, José María

2017-01-01

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. PMID:28481277

Integrated long-range UAV/UGV collaborative target tracking

NASA Astrophysics Data System (ADS)

Moseley, Mark B.; Grocholsky, Benjamin P.; Cheung, Carol; Singh, Sanjiv

2009-05-01

Coordinated operations between unmanned air and ground assets allow leveraging of multi-domain sensing and increase opportunities for improving line of sight communications. While numerous military missions would benefit from coordinated UAV-UGV operations, foundational capabilities that integrate stove-piped tactical systems and share available sensor data are required and not yet available. iRobot, AeroVironment, and Carnegie Mellon University are working together, partially SBIR-funded through ARDEC's small unit network lethality initiative, to develop collaborative capabilities for surveillance, targeting, and improved communications based on PackBot UGV and Raven UAV platforms. We integrate newly available technologies into computational, vision, and communications payloads and develop sensing algorithms to support vision-based target tracking. We first simulated and then applied onto real tactical platforms an implementation of Decentralized Data Fusion, a novel technique for fusing track estimates from PackBot and Raven platforms for a moving target in an open environment. In addition, system integration with AeroVironment's Digital Data Link onto both air and ground platforms has extended our capabilities in communications range to operate the PackBot as well as in increased video and data throughput. The system is brought together through a unified Operator Control Unit (OCU) for the PackBot and Raven that provides simultaneous waypoint navigation and traditional teleoperation. We also present several recent capability accomplishments toward PackBot-Raven coordinated operations, including single OCU display design and operation, early target track results, and Digital Data Link integration efforts, as well as our near-term capability goals.

Data Gathering and Energy Transfer Dilemma in UAV-Assisted Flying Access Network for IoT.

PubMed

Arabi, Sara; Sabir, Essaid; Elbiaze, Halima; Sadik, Mohamed

2018-05-11

Recently, Unmanned Aerial Vehicles (UAVs) have emerged as an alternative solution to assist wireless networks, thanks to numerous advantages they offer in comparison to terrestrial fixed base stations. For instance, a UAV can be used to embed a flying base station providing an on-demand nomadic access to network services. A UAV can also be used to wirelessly recharge out-of-battery ground devices. In this paper, we aim to deal with both data collection and recharging depleted ground Internet-of-Things (IoT) devices through a UAV station used as a flying base station. To extend the network lifetime, we present a novel use of UAV with energy harvesting module and wireless recharging capabilities. However, the UAV is used as an energy source to empower depleted IoT devices. On one hand, the UAV charges depleted ground IoT devices under three policies: (1) low-battery first scheme; (2) high-battery first scheme; and (3) random scheme. On the other hand, the UAV station collects data from IoT devices that have sufficient energy to transmit their packets, and in the same phase, the UAV exploits the Radio Frequency (RF) signals transmitted by IoT devices to extract and harvest energy. Furthermore, and as the UAV station has a limited coverage time due to its energy constraints, we propose and investigate an efficient trade-off between ground users recharging time and data gathering time. Furthermore, we suggest to control and optimize the UAV trajectory in order to complete its travel within a minimum time, while minimizing the energy spent and/or enhancing the network lifetime. Extensive numerical results and simulations show how the system behaves under different scenarios and using various metrics in which we examine the added value of UAV with energy harvesting module.

From large-eddy simulation to multi-UAVs sampling of shallow cumulus clouds

NASA Astrophysics Data System (ADS)

Lamraoui, Fayçal; Roberts, Greg; Burnet, Frédéric

2016-04-01

In-situ sampling of clouds that can provide simultaneous measurements at satisfying spatio-temporal resolutions to capture 3D small scale physical processes continues to present challenges. This project (SKYSCANNER) aims at bringing together cloud sampling strategies using a swarm of unmanned aerial vehicles (UAVs) based on Large-eddy simulation (LES). The multi-UAV-based field campaigns with a personalized sampling strategy for individual clouds and cloud fields will significantly improve the understanding of the unresolved cloud physical processes. An extensive set of LES experiments for case studies from ARM-SGP site have been performed using MesoNH model at high resolutions down to 10 m. The carried out simulations led to establishing a macroscopic model that quantifies the interrelationship between micro- and macrophysical properties of shallow convective clouds. Both the geometry and evolution of individual clouds are critical to multi-UAV cloud sampling and path planning. The preliminary findings of the current project reveal several linear relationships that associate many cloud geometric parameters to cloud related meteorological variables. In addition, the horizontal wind speed indicates a proportional impact on cloud number concentration as well as triggering and prolonging the occurrence of cumulus clouds. In the framework of the joint collaboration that involves a Multidisciplinary Team (including institutes specializing in aviation, robotics and atmospheric science), this model will be a reference point for multi-UAVs sampling strategies and path planning.

Putting Technology to Work in Science - How to Select Unmanned Aerial Vehicles (UAV) and their Instrumentation for Atmospheric and Earth Surface Observations

NASA Astrophysics Data System (ADS)

Teller, Amit; Lange, Manfred; Ioannou, Stelios; Keleshis, Christos

2010-05-01

The Autonomous Flying Platforms for Atmospheric and Earth Surface Observations project (APAESO) of the Energy, Environment and Water Research Center (EEWRC) at the Cyprus Institute is aimed at the dual purpose of carrying out atmospheric and earth-surface observations in the Mediterranean. The APAESO platforms will offer the unique potential to determine physical, chemical and radiative atmospheric properties, aerosol and dust concentrations, atmospheric dynamics, surface morphology, vegetation and land use patterns as well as ocean surface properties (biology, waves, currents) and to carry out archaeological site reconnaissance and contaminant detection at high spatial resolution. The first phase of APAESO was dedicated to the preliminary design and the selection of an Unmanned Aerial Vehicle (UAV) as the backbone of the APAESO infrastructure. Selection of a UAV suitable for the many research objectives as outlined above is challenging because the UAV technology is new and rapidly evolving. This notwithstanding, a very large number of systems, mostly utilized for defense purposes, are currently available. The major challenge in the selection process lies in considering the trade-off between different platform characteristics (e.g. payload weight, endurance, max. altitude for operation and price) and in optimizing the potential performance of the UAV. Based on the required characteristics for the UAV platform, a survey of possible UAVs and suitable sensors was prepared based on various data sources. We used an elimination process in order to consider only a few models for the final selection process out of about 1000 commercially available UAV models that were initially investigated. The presentation will discuss the main scientific objectives that determine the specification of the UAV platform, major considerations in selecting best available technology for our needs and will briefly describe the next phases of the project.

Near Real Time Structural Health Monitoring with Multiple Sensors in a Cloud Environment

NASA Astrophysics Data System (ADS)

Bock, Y.; Todd, M.; Kuester, F.; Goldberg, D.; Lo, E.; Maher, R.

2017-12-01

A repeated near real time 3-D digital surrogate representation of critical engineered structures can be used to provide actionable data on subtle time-varying displacements in support of disaster resiliency. We describe a damage monitoring system of optimally-integrated complementary sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical Systems (MEMS) accelerometers coupled with the GNSS (seismogeodesy), light multi-rotor Unmanned Aerial Vehicles (UAVs) equipped with high-resolution digital cameras and GNSS/IMU, and ground-based Light Detection and Ranging (LIDAR). The seismogeodetic system provides point measurements of static and dynamic displacements and seismic velocities of the structure. The GNSS ties the UAV and LIDAR imagery to an absolute reference frame with respect to survey stations in the vicinity of the structure to isolate the building response to ground motions. The GNSS/IMU can also estimate the trajectory of the UAV with respect to the absolute reference frame. With these constraints, multiple UAVs and LIDAR images can provide 4-D displacements of thousands of points on the structure. The UAV systematically circumnavigates the target structure, collecting high-resolution image data, while the ground LIDAR scans the structure from different perspectives to create a detailed baseline 3-D reference model. UAV- and LIDAR-based imaging can subsequently be repeated after extreme events, or after long time intervals, to assess before and after conditions. The unique challenge is that disaster environments are often highly dynamic, resulting in rapidly evolving, spatio-temporal data assets with the need for near real time access to the available data and the tools to translate these data into decisions. The seismogeodetic analysis has already been demonstrated in the NASA AIST Managed Cloud Environment (AMCE) designed to manage large NASA Earth Observation data projects on Amazon Web Services (AWS). The Cloud provides distinct advantages in terms of extensive storage and computing resources required for processing UAV and LIDAR imagery. Furthermore, it avoids single points of failure and allows for remote operations during emergencies, when near real time access to structures may be limited.

Establishing a Disruptive New Capability for NASA to Fly UAV's into Hazardous Conditions

NASA Technical Reports Server (NTRS)

Ely, Jay; Nguyen, Truong; Wilson, Jennifer; Brown, Robert; Laughter, Sean; Teets, Ed; Parker, Allen; Chan, Patrick Hon Man; Richards, Lance

2015-01-01

A 2015 NASA Aeronautics Mission "Seedling" Proposal is described for a Severe-Environment UAV (SE-UAV) that can perform in-situ measurements in hazardous atmospheric conditions like lightning, volcanic ash and radiation. Specifically, this paper describes the design of a proof-of-concept vehicle and measurement system that can survive lightning attachment during flight operations into thunderstorms. Elements from three NASA centers draw together for the SE-UAV concept. 1) The NASA KSC Genesis UAV was developed in collaboration with the DARPA Nimbus program to measure electric field and X-rays present within thunderstorms. 2) A novel NASA LaRC fiber-optic sensor uses Faraday-effect polarization rotation to measure total lightning electric current on an air vehicle fuselage. 3) NASA AFRC's state-of-the-art Fiber Optics and Systems Integration Laboratory is envisioned to transition the Faraday system to a compact, light-weight, all-fiber design. The SE-UAV will provide in-flight lightning electric-current return stroke and recoil leader data, and serve as a platform for development of emerging sensors and new missions into hazardous environments. NASA's Aeronautics and Science Missions are interested in a capability to perform in-situ volcanic plume measurements and long-endurance UAV operations in various weather conditions. (Figure 1 shows an artist concept of a SE-UAV flying near a volcano.) This paper concludes with an overview of the NASA Aeronautics Strategic Vision, Programs, and how a SE-UAV is envisioned to impact them. The SE-UAV concept leverages high-value legacy research products into a new capability for NASA to fly a pathfinder UAV into hazardous conditions, and is presented in the SPIE DSS venue to explore teaming, collaboration and advocacy opportunities outside NASA.

Establishing a disruptive new capability for NASA to fly UAV's into hazardous conditions

NASA Astrophysics Data System (ADS)

Ely, Jay; Nguyen, Truong; Wilson, Jennifer; Brown, Robert; Laughter, Sean; Teets, Ed; Parker, Allen; Chan, Hon M.; Richards, Lance

2015-05-01

A 2015 NASA Aeronautics Mission "Seedling" Proposal is described for a Severe-Environment UAV (SE-UAV) that can perform in-situ measurements in hazardous atmospheric conditions like lightning, volcanic ash and radiation. Specifically, this paper describes the design of a proof-of-concept vehicle and measurement system that can survive lightning attachment during flight operations into thunderstorms. Elements from three NASA centers draw together for the SE-UAV concept. 1) The NASA KSC Genesis UAV was developed in collaboration with the DARPA Nimbus program to measure electric field and X-rays present within thunderstorms. 2) A novel NASA LaRC fiber-optic sensor uses Faraday-effect polarization rotation to measure total lightning electric current on an air vehicle fuselage. 3) NASA AFRC's state-of-the-art Fiber Optics and Systems Integration Laboratory is envisioned to transition the Faraday system to a compact, light-weight, all-fiber design. The SE-UAV will provide in-flight lightning electric-current return stroke and recoil leader data, and serve as a platform for development of emerging sensors and new missions into hazardous environments. NASA's Aeronautics and Science Missions are interested in a capability to perform in-situ volcanic plume measurements and long-endurance UAV operations in various weather conditions. (Figure 1 shows an artist concept of a SE-UAV flying near a volcano.) This paper concludes with an overview of the NASA Aeronautics Strategic Vision, Programs, and how a SE-UAV is envisioned to impact them. The SE-UAV concept leverages high-value legacy research products into a new capability for NASA to fly a pathfinder UAV into hazardous conditions, and is presented in the SPIE DSS venue to explore teaming, collaboration and advocacy opportunities outside NASA.

Pigeon interaction mode switch-based UAV distributed flocking control under obstacle environments.

PubMed

Qiu, Huaxin; Duan, Haibin

2017-11-01

Unmanned aerial vehicle (UAV) flocking control is a serious and challenging problem due to local interactions and changing environments. In this paper, a pigeon flocking model and a pigeon coordinated obstacle-avoiding model are proposed based on a behavior that pigeon flocks will switch between hierarchical and egalitarian interaction mode at different flight phases. Owning to the similarity between bird flocks and UAV swarms in essence, a distributed flocking control algorithm based on the proposed pigeon flocking and coordinated obstacle-avoiding models is designed to coordinate a heterogeneous UAV swarm to fly though obstacle environments with few informed individuals. The comparative simulation results are elaborated to show the feasibility, validity and superiority of our proposed algorithm. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Stabilization and control of quad-rotor helicopter using a smartphone device

NASA Astrophysics Data System (ADS)

Desai, Alok; Lee, Dah-Jye; Moore, Jason; Chang, Yung-Ping

2013-01-01

In recent years, autonomous, micro-unmanned aerial vehicles (micro-UAVs), or more specifically hovering micro- UAVs, have proven suitable for many promising applications such as unknown environment exploration and search and rescue operations. The early versions of UAVs had no on-board control capabilities, and were difficult for manual control from a ground station. Many UAVs now are equipped with on-board control systems that reduce the amount of control required from the ground-station operator. However, the limitations on payload, power consumption and control without human interference remain the biggest challenges. This paper proposes to use a smartphone as the sole computational device to stabilize and control a quad-rotor. The goal is to use the readily available sensors in a smartphone such as the GPS, the accelerometer, the rate-gyros, and the camera to support vision-related tasks such as flight stabilization, estimation of the height above ground, target tracking, obstacle detection, and surveillance. We use a quad-rotor platform that has been built in the Robotic Vision Lab at Brigham Young University for our development and experiments. An Android smartphone is connected through the USB port to an external hardware that has a microprocessor and circuitries to generate pulse-width modulation signals to control the brushless servomotors on the quad-rotor. The high-resolution camera on the smartphone is used to detect and track features to maintain a desired altitude level. The vision algorithms implemented include template matching, Harris feature detector, RANSAC similarity-constrained homography, and color segmentation. Other sensors are used to control yaw, pitch, and roll of the quad-rotor. This smartphone-based system is able to stabilize and control micro-UAVs and is ideal for micro-UAVs that have size, weight, and power limitations.

Exploitation of Self Organization in UAV Swarms for Optimization in Combat Environments

DTIC Science & Technology

2008-03-01

behaviors and entangled hierarchy into Swarmfare [59] UAV simulation environment to include these models. • Validate this new model’s success through...Figure 4.3. The hierarchy of control emerges from the entangled hierarchy of the state relations at the simulation , swarm and rule/behaviors level...majors, major) Abstract Model Types (AMT) Figure A.1: SO Abstract Model Type Table 142 Appendix B. Simulators Comparision Name MATLAB Multi UAV MultiUAV

Multimodal UAV detection: study of various intrusion scenarios

NASA Astrophysics Data System (ADS)

Hengy, Sebastien; Laurenzis, Martin; Schertzer, Stéphane; Hommes, Alexander; Kloeppel, Franck; Shoykhetbrod, Alex; Geibig, Thomas; Johannes, Winfried; Rassy, Oussama; Christnacher, Frank

2017-10-01

Small unmanned aerial vehicles (UAVs) are becoming increasingly popular and affordable the last years for professional and private consumer market, with varied capacities and performances. Recent events showed that illicit or hostile uses constitute an emergent, quickly evolutionary threat. Recent developments in UAV technologies tend to bring autonomous, highly agile and capable unmanned aerial vehicles to the market. These UAVs can be used for spying operations as well as for transporting illicit or hazardous material (smuggling, flying improvised explosive devices). The scenario of interest concerns the protection of sensitive zones against the potential threat constituted by small drones. In the recent past, field trials were carried out to investigate the detection and tracking of multiple UAV flying at low altitude. Here, we present results which were achieved using a heterogeneous sensor network consisting of acoustic antennas, small FMCW RADAR systems and optical sensors. While acoustics and RADAR was applied to monitor a wide azimuthal area (360°), optical sensors were used for sequentially identification. The localization results have been compared to the ground truth data to estimate the efficiency of each detection system. Seven-microphone acoustic arrays allow single source localization. The mean azimuth and elevation estimation error has been measured equal to 1.5 and -2.5 degrees respectively. The FMCW radar allows tracking of multiple UAVs by estimating their range, azimuth and motion speed. Both technologies can be linked to the electro-optical system for final identification of the detected object.

Intification and modelling of flight characteristics for self-build shock flyer type UAV

NASA Astrophysics Data System (ADS)

Rashid., Z. A.; Dardin, A. S. F. Syed.; Azid, A. A.; Ahmad, K. A.

2018-02-01

The development of an autonomous Unmanned Aerial Vehicle (UAV) requires a fundamentals studies of the UAV's flight characteristic. The aim of this study is to identify and model the flight characteristic of a conventional fixed-wing type UAV. Subsequence to this, the mode of flight of the UAV can be investigated. One technique to identify the characteristic of a UAV is a flight test where it required specific maneuvering to be executed while measuring the attitude sensor. In this study, a simple shock flyer type UAV was used as the aircraft. The result shows that the modeled flight characteristic has a significant relation with actual values but the fitting value is rather small. It is suggested that the future study is conducted with an improvement of the physical UAV, data filtering and better system identification methods.

Increasing the UAV data value by an OBIA methodology

NASA Astrophysics Data System (ADS)

García-Pedrero, Angel; Lillo-Saavedra, Mario; Rodriguez-Esparragon, Dionisio; Rodriguez-Gonzalez, Alejandro; Gonzalo-Martin, Consuelo

2017-10-01

Recently, there has been a noteworthy increment of using images registered by unmanned aerial vehicles (UAV) in different remote sensing applications. Sensors boarded on UAVs has lower operational costs and complexity than other remote sensing platforms, quicker turnaround times as well as higher spatial resolution. Concerning this last aspect, particular attention has to be paid on the limitations of classical algorithms based on pixels when they are applied to high resolution images. The objective of this study is to investigate the capability of an OBIA methodology developed for the automatic generation of a digital terrain model of an agricultural area from Digital Elevation Model (DEM) and multispectral images registered by a Parrot Sequoia multispectral sensor board on a eBee SQ agricultural drone. The proposed methodology uses a superpixel approach for obtaining context and elevation information used for merging superpixels and at the same time eliminating objects such as trees in order to generate a Digital Terrain Model (DTM) of the analyzed area. Obtained results show the potential of the approach, in terms of accuracy, when it is compared with a DTM generated by manually eliminating objects.

Wetland Vegetation Integrity Assessment with Low Altitude Multispectral Uav Imagery

NASA Astrophysics Data System (ADS)

Boon, M. A.; Tesfamichael, S.

2017-08-01

The use of multispectral sensors on Unmanned Aerial Vehicles (UAVs) was until recently too heavy and bulky although this changed in recent times and they are now commercially available. The focus on the usage of these sensors is mostly directed towards the agricultural sector where the focus is on precision farming. Applications of these sensors for mapping of wetland ecosystems are rare. Here, we evaluate the performance of low altitude multispectral UAV imagery to determine the state of wetland vegetation in a localised spatial area. Specifically, NDVI derived from multispectral UAV imagery was used to inform the determination of the integrity of the wetland vegetation. Furthermore, we tested different software applications for the processing of the imagery. The advantages and disadvantages we experienced of these applications are also shortly presented in this paper. A JAG-M fixed-wing imaging system equipped with a MicaScene RedEdge multispectral camera were utilised for the survey. A single surveying campaign was undertaken in early autumn of a 17 ha study area at the Kameelzynkraal farm, Gauteng Province, South Africa. Structure-from-motion photogrammetry software was used to reconstruct the camera position's and terrain features to derive a high resolution orthoretified mosaic. MicaSense Atlas cloud-based data platform, Pix4D and PhotoScan were utilised for the processing. The WET-Health level one methodology was followed for the vegetation assessment, where wetland health is a measure of the deviation of a wetland's structure and function from its natural reference condition. An on-site evaluation of the vegetation integrity was first completed. Disturbance classes were then mapped using the high resolution multispectral orthoimages and NDVI. The WET-Health vegetation module completed with the aid of the multispectral UAV products indicated that the vegetation of the wetland is largely modified ("D" PES Category) and that the condition is expected to deteriorate (change score) in the future. However a lower impact score were determined utilising the multispectral UAV imagery and NDVI. The result is a more accurate estimation of the impacts in the wetland.

Fault tolerant attitude sensing and force feedback control for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Jagadish, Chirag

Two aspects of an unmanned aerial vehicle are studied in this work. One is fault tolerant attitude determination and the other is to provide force feedback to the joy-stick of the UAV so as to prevent faulty inputs from the pilot. Determination of attitude plays an important role in control of aerial vehicles. One way of defining the attitude is through Euler angles. These angles can be determined based on the measurements of the projections of the gravity and earth magnetic fields on the three body axes of the vehicle. Attitude determination in unmanned aerial vehicles poses additional challenges due to limitations of space, payload, power and cost. Therefore it provides for almost no room for any bulky sensors or extra sensor hardware for backup and as such leaves no room for sensor fault issues either. In the face of these limitations, this study proposes a fault tolerant computing of Euler angles by utilizing multiple different computation methods, with each method utilizing a different subset of the available sensor measurement data. Twenty-five such methods have been presented in this document. The capability of computing the Euler angles in multiple ways provides a diversified redundancy required for fault tolerance. The proposed approach can identify certain sets of sensor failures and even separate the reference fields from the disturbances. A bank-to-turn maneuver of the NASA GTM UAV is used to demonstrate the fault tolerance provided by the proposed method as well as to demonstrate the method of determining the correct Euler angles despite interferences by inertial acceleration disturbances. Attitude computation is essential for stability. But as of today most UAVs are commanded remotely by human pilots. While basic stability control is entrusted to machine or the on-board automatic controller, overall guidance is usually with humans. It is therefore the pilot who sets the command/references through a joy-stick. While this is a good compromise between complete automation and complete human control, it still poses some unique challenges. Pilots of manned aircraft are present inside the cockpit of the aircraft they fly and thus have a better feel of the flying environment and also the limitations of the flight. The same might not be true for UAV pilots stationed on the ground. A major handicap is that visual feedback is the only one available for the UAV pilot. An additional parameter like force feedback on the remote control joy-stick can help the UAV pilot to physically feel the limitation of the safe flight envelope. This can make the flying itself easier and safer. A method proposed here is to design a joy-stick assembly with an additional actuator. This actuator is controlled so as to generate a force feedback on the joy-stick. The control developed for this system is such that the actuator allows free movement for the pilot as long as the UAV is within the safe flight envelope. On the other hand, if it is outside this safe range, the actuator opposes the pilot's applied torque and prevents him/her from giving erroneous commands to the UAV.

Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery

DOE PAGES

Habib, Ayman; Han, Youkyung; Xiong, Weifeng; ...

2016-09-24

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging ismore » based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Lastly, experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.« less

Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Habib, Ayman; Han, Youkyung; Xiong, Weifeng

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging ismore » based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Lastly, experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.« less

Utilization of Unmanned Aerial Vehicles for Rangeland Resources Monitoring in a Changing Regulatory Environment (Invited)

NASA Astrophysics Data System (ADS)

Rango, A.; Vivoni, E. R.; Browning, D. M.; Anderson, C.; Laliberte, A. S.

2013-12-01

It is taking longer than expected to realize the immense potential of Unmanned Aerial Vehicles (UAVs)for civil applications due to the complexity of regulations being developed by the Federal Aviation Authority (FAA) that can be applied to both manned and unmanned flight in the National Airspace System (NAS). As a result, FAA has required that for all UAV flights in the NAS, an external pilot must maintain line-of-sight contact with the UAV. Properly trained observers must also be present to assist the external pilot in collision avoidance. Additionally, in order to fly in the NAS, formal approval must be requested from FAA through application for a Certificate of Authorization (COA for government applicants or a Special Airworthiness Certificate (SAC) in the experimental category for non-government applicants. Flight crews of UAVs must pass exams also required for manned airplane pilots. Although flight crews for UAVs are not required to become manned airplane pilots, UAV flight missions are much more efficient if one or two of the UAV flight crew are also manned aircraft pilots so they can serve as the UAV mission commander. Our group has performed numerous UAV flights within the Jornada Experimental Range in southern New Mexico. Two developments with Jornada UAVs can be recommended to other UAV operators that would increase flight time experience and study areas covered by UAV images. First, do not overlook the possibility of obtaining permission to fly in Restricted Military Airspace (RMA). At the Jornada, our airspace is approximately 50% NAS and 50% RMA. With experiments ongoing in both types of airspace, we can fly in both areas and continue to increase UAV flights. Second, we have developed an air- and-ground vehicle approach for long distance, continuous pilot transport that always maintains line-of-sight requirements. This allows flying several target areas on a single mission and increasing the number of acquired UAV images - over 90,000 UAV images have now been acquired at Jornada. Most of our UAV flights have taken place over rangelands or watersheds in the western U.S. These flights have been successful used for classification of vegetation cover and type, measuring gaps between vegetation patches, identifing locations of potentially erosive soil, deriving digital elevation models, and monitoring plant phenology.. These measurements can be directly compared to more costly and time-intensive traditional techniques used in rangeland health determinations. New UAVs are becoming available with increased sensor payload capacity. At Jornada we have concentrated on flying at low altitudes (~215 m) to acquire hyperspatial resolutions with digital cameras of about 5-6 cm. We also fly a six band multispectral camera with spatial resolution of ~ 13 cm. We have recently acquired a larger Bat-4 UAV to go with the Bat-3 UAV. The major improvement associated with this upgrade is an increase in sensor payload from 1.4 kg to 14 kg. We are surveying the type of sensors that we could add to best increase our information content.

Feasibility of Turing-Style Tests for Autonomous Aerial Vehicle "Intelligence"

NASA Technical Reports Server (NTRS)

Young, Larry A.

2007-01-01

A new approach is suggested to define and evaluate key metrics as to autonomous aerial vehicle performance. This approach entails the conceptual definition of a "Turing Test" for UAVs. Such a "UAV Turing test" would be conducted by means of mission simulations and/or tailored flight demonstrations of vehicles under the guidance of their autonomous system software. These autonomous vehicle mission simulations and flight demonstrations would also have to be benchmarked against missions "flown" with pilots/human-operators in the loop. In turn, scoring criteria for such testing could be based upon both quantitative mission success metrics (unique to each mission) and by turning to analog "handling quality" metrics similar to the well-known Cooper-Harper pilot ratings used for manned aircraft. Autonomous aerial vehicles would be considered to have successfully passed this "UAV Turing Test" if the aggregate mission success metrics and handling qualities for the autonomous aerial vehicle matched or exceeded the equivalent metrics for missions conducted with pilots/human-operators in the loop. Alternatively, an independent, knowledgeable observer could provide the "UAV Turing Test" ratings of whether a vehicle is autonomous or "piloted." This observer ideally would, in the more sophisticated mission simulations, also have the enhanced capability of being able to override the scripted mission scenario and instigate failure modes and change of flight profile/plans. If a majority of mission tasks are rated as "piloted" by the observer, when in reality the vehicle/simulation is fully- or semi- autonomously controlled, then the vehicle/simulation "passes" the "UAV Turing Test." In this regards, this second "UAV Turing Test" approach is more consistent with Turing s original "imitation game" proposal. The overall feasibility, and important considerations and limitations, of such an approach for judging/evaluating autonomous aerial vehicle "intelligence" will be discussed from a theoretical perspective.

Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients †

PubMed Central

Fidan, Barış; Umay, Ilknur

2015-01-01

Accurate signal-source and signal-reflector target localization tasks via mobile sensory units and wireless sensor networks (WSNs), including those for environmental monitoring via sensory UAVs, require precise knowledge of specific signal propagation properties of the environment, which are permittivity and path loss coefficients for the electromagnetic signal case. Thus, accurate estimation of these coefficients has significant importance for the accuracy of location estimates. In this paper, we propose a geometric cooperative technique to instantaneously estimate such coefficients, with details provided for received signal strength (RSS) and time-of-flight (TOF)-based range sensors. The proposed technique is integrated to a recursive least squares (RLS)-based adaptive localization scheme and an adaptive motion control law, to construct adaptive target localization and adaptive target tracking algorithms, respectively, that are robust to uncertainties in aforementioned environmental signal propagation coefficients. The efficiency of the proposed adaptive localization and tracking techniques are both mathematically analysed and verified via simulation experiments. PMID:26690441

Co-Registration of DSMs Generated by Uav and Terrestrial Laser Scanning Systems

NASA Astrophysics Data System (ADS)

Ancil Persad, Ravi; Armenakis, Costas

2016-06-01

An approach for the co-registration of Digital Surface Models (DSMs) derived from Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) is proposed. Specifically, a wavelet-based feature descriptor for matching surface keypoints on the 2.5D DSMs is developed. DSMs are useful in wide-scope of various applications such as 3D building modelling and reconstruction, cultural heritage, urban and environmental planning, aircraft navigation/path routing, accident and crime scene reconstruction, mining as well as, topographic map revision and change detection. For these listed applications, it is not uncommon that there will be a need for automatically aligning multi-temporal DSMs which may have been acquired from multiple sensors, with different specifications over a period of time, and may have various overlaps. Terrestrial laser scanners usually capture urban facades in an accurate manner; however this is not the case for building roof structures. On the other hand, vertical photography from UAVs can capture the roofs. Therefore, the automatic fusion of UAV and laser-scanning based DSMs is addressed here as it serves various geospatial applications.

Detection of Nuclear Sources by UAV Teleoperation Using a Visuo-Haptic Augmented Reality Interface

PubMed Central

Micconi, Giorgio; Caselli, Stefano; Benassi, Giacomo; Zambelli, Nicola; Bettelli, Manuele

2017-01-01

A visuo-haptic augmented reality (VHAR) interface is presented enabling an operator to teleoperate an unmanned aerial vehicle (UAV) equipped with a custom CdZnTe-based spectroscopic gamma-ray detector in outdoor environments. The task is to localize nuclear radiation sources, whose location is unknown to the user, without the close exposure of the operator. The developed detector also enables identification of the localized nuclear sources. The aim of the VHAR interface is to increase the situation awareness of the operator. The user teleoperates the UAV using a 3DOF haptic device that provides an attractive force feedback around the location of the most intense detected radiation source. Moreover, a fixed camera on the ground observes the environment where the UAV is flying. A 3D augmented reality scene is displayed on a computer screen accessible to the operator. Multiple types of graphical overlays are shown, including sensor data acquired by the nuclear radiation detector, a virtual cursor that tracks the UAV and geographical information, such as buildings. Experiments performed in a real environment are reported using an intense nuclear source. PMID:28961198

Detection of Nuclear Sources by UAV Teleoperation Using a Visuo-Haptic Augmented Reality Interface.

PubMed

Aleotti, Jacopo; Micconi, Giorgio; Caselli, Stefano; Benassi, Giacomo; Zambelli, Nicola; Bettelli, Manuele; Zappettini, Andrea

2017-09-29

A visuo-haptic augmented reality (VHAR) interface is presented enabling an operator to teleoperate an unmanned aerial vehicle (UAV) equipped with a custom CdZnTe-based spectroscopic gamma-ray detector in outdoor environments. The task is to localize nuclear radiation sources, whose location is unknown to the user, without the close exposure of the operator. The developed detector also enables identification of the localized nuclear sources. The aim of the VHAR interface is to increase the situation awareness of the operator. The user teleoperates the UAV using a 3DOF haptic device that provides an attractive force feedback around the location of the most intense detected radiation source. Moreover, a fixed camera on the ground observes the environment where the UAV is flying. A 3D augmented reality scene is displayed on a computer screen accessible to the operator. Multiple types of graphical overlays are shown, including sensor data acquired by the nuclear radiation detector, a virtual cursor that tracks the UAV and geographical information, such as buildings. Experiments performed in a real environment are reported using an intense nuclear source.

Development of an Unmanned Aircraft System and Cyberinfrastructure for Environmental Science Research

NASA Astrophysics Data System (ADS)

Brady, J. J.; Tweedie, C. E.; Escapita, I. J.

2009-12-01

There is a fundamental need to improve capacities for monitoring environmental change using remote sensing technologies. Recently, researchers have begun using Unmanned Aerial Vehicles (UAVs) to expand and improve upon remote sensing capabilities. Limitations to most non-military and relatively small-scale Unmanned Aircraft Systems (UASs) include a need to develop more reliable communications between ground and aircraft, tools to optimize flight control, real time data processing, and visually ascertaining the quantity of data collected while in air. Here we present a prototype software system that has enhanced communication between ground and the vehicle, can synthesize near real time data acquired from sensors on board, can log operation data during flights, and can visually demonstrate the amount and quality of data for a sampling area. This software has the capacity to greatly improve the utilization of UAS in the environmental sciences. The software system is being designed for use on a paraglider UAV that has a suite of sensors suitable for characterizing the footprints of eddy covariance towers situated in the Chihuahuan Desert and in the Arctic. Sensors on board relay operational flight data (airspeed, ground speed, latitude, longitude, pitch, yaw, roll, acceleration, and video) as well as a suite of customized sensors. Additional sensors can be added to an on board laptop or a CR1000 data logger thereby allowing data from these sensors to be visualized in the prototype software. This poster will describe the development, use and customization of our UAS and multimedia will be available during AGU to illustrate the system in use. UAV on workbench in the lab UAV in flight

The Systems Engineering Design of a Smart Forward Operating Base Surveillance System for Forward Operating Base Protection

DTIC Science & Technology

2013-06-01

fixed sensors located along the perimeter of the FOB. The video is analyzed for facial recognition to alert the Network Operations Center (NOC...the UAV is processed on board for facial recognition and video for behavior analysis is sent directly to the Network Operations Center (NOC). Video...captured by the fixed sensors are sent directly to the NOC for facial recognition and behavior analysis processing. The multi- directional signal

High Resolution UAV-based Passive Microwave L-band Imaging of Soil Moisture

NASA Astrophysics Data System (ADS)

Gasiewski, A. J.; Stachura, M.; Elston, J.; McIntyre, E. M.

2013-12-01

Due to long electrical wavelengths and aperture size limitations the scaling of passive microwave remote sensing of soil moisture from spaceborne low-resolution applications to high resolution applications suitable for precision agriculture requires use of low flying aerial vehicles. This presentation summarizes a project to develop a commercial Unmanned Aerial Vehicle (UAV) hosting a precision microwave radiometer for mapping of soil moisture in high-value shallow root-zone crops. The project is based on the use of the Tempest electric-powered UAV and a compact digital L-band (1400-1427 MHz) passive microwave radiometer developed specifically for extremely small and lightweight aerial platforms or man-portable, tractor, or tower-based applications. Notable in this combination are a highly integrated UAV/radiometer antenna design and use of both the upwelling emitted signal from the surface and downwelling cold space signal for precise calibration using a lobe-correlating radiometer architecture. The system achieves a spatial resolution comparable to the altitude of the UAV above the ground while referencing upwelling measurements to the constant and well-known background temperature of cold space. The radiometer incorporates digital sampling and radio frequency interference mitigation along with infrared, near-infrared, and visible (red) sensors for surface temperature and vegetation biomass correction. This NASA-sponsored project is being developed both for commercial application in cropland water management, L-band satellite validation, and estuarian plume studies.

Advanced Broadband Links for TIER III UAV Data Communication

NASA Astrophysics Data System (ADS)

Griethe, Wolfgang; Gregory, Mark; Heine, Frank; Kampfner, Hartmut

2011-08-01

Unmanned Aeronautical Vehicle (UAV) are getting more and more importance because of their prominent role as national reconnaissance systems, for disaster monitoring, and environmental mapping. However, the existence of reliable and robust data links are indispensable for Unmanned Aircraft System (UAS) missions. In particular for Beyond Line-Of-Sight operations (BLOS) of Tier III UAVs, satellite data links are a key element since extensive sensor data have to be transmitted preferably in real-time or near real-time.The paper demonstrates that the continuously increasing number of UAS and the intensified use of high resolution sensors will reveal RF-bandwidth as a limitating factor in the communication chain of Tier III UAVs. The RF-bandwidth gap can be partly closed by use of high-order modulation, of course, but much more progress in terms of bandwidth allocation can be achieved by using optical transmission technology. Consequently, the paper underlines that meanwhile this technology has been sufficiently verified in space, and shows that optical links are suited as well for broadband communications of Tier III UAVs. Moreover, the advantages of LaserCom in UAV scenarios and its importance for Network Centric Warfare (NCW) as well as for Command, Control, Communications, Computers, Intelligens, Surveillance, and Reconnaissance (C4ISR) are emphasized. Numerous practical topics and design requirements, relevant for the establishment of optical links onboard of Tier III UAVs, are discussed.

State estimation for autonomous flight in cluttered environments

NASA Astrophysics Data System (ADS)

Langelaan, Jacob Willem

Safe, autonomous operation in complex, cluttered environments is a critical challenge facing autonomous mobile systems. The research described in this dissertation was motivated by a particularly difficult example of autonomous mobility: flight of a small Unmanned Aerial Vehicle (UAV) through a forest. In cluttered environments (such as forests or natural and urban canyons) signals from navigation beacons such as GPS may frequently be occluded. Direct measurements of vehicle position are therefore unavailable, and information required for flight control, obstacle avoidance, and navigation must be obtained using only on-board sensors. However, payload limitations of small UAVs restrict both the mass and physical dimensions of sensors that can be carried. This dissertation describes the development and proof-of-concept demonstration of a navigation system that uses only a low-cost inertial measurement unit and a monocular camera. Micro electromechanical inertial measurements units are well suited to small UAV applications and provide measurements of acceleration and angular rate. However, they do not provide information about nearby obstacles (needed for collision avoidance) and their noise and bias characteristics lead to unbounded growth in computed position. A monocular camera can provide bearings to nearby obstacles and landmarks. These bearings can be used both to enable obstacle avoidance and to aid navigation. Presented here is a solution to the problem of estimating vehicle state (position, orientation and velocity) as well as positions of obstacles in the environment using only inertial measurements and bearings to obstacles. This is a highly nonlinear estimation problem, and standard estimation techniques such as the Extended Kalman Filter are prone to divergence in this application. In this dissertation a Sigma Point Kalman Filter is implemented, resulting in an estimator which is able to cope with the significant nonlinearities in the system equations and uncertainty in state estimates while remaining tractable for real-time operation. In addition, the issues of data association and landmark initialization are addressed. Estimator performance is examined through Monte Carlo simulations in both two and three dimensions for scenarios involving UAV flight in cluttered environments. Hardware tests and simulations demonstrate navigation through an obstacle-strewn environment by a small Unmanned Ground Vehicle.

Prototyping a GNSS-Based Passive Radar for UAVs: An Instrument to Classify the Water Content Feature of Lands

PubMed Central

Troglia Gamba, Micaela; Marucco, Gianluca; Pini, Marco; Ugazio, Sabrina; Falletti, Emanuela; Lo Presti, Letizia

2015-01-01

Global Navigation Satellite Systems (GNSS) broadcast signals for positioning and navigation, which can be also employed for remote sensing applications. Indeed, the satellites of any GNSS can be seen as synchronized sources of electromagnetic radiation, and specific processing of the signals reflected back from the ground can be used to estimate the geophysical properties of the Earth’s surface. Several experiments have successfully demonstrated GNSS-reflectometry (GNSS-R), whereas new applications are continuously emerging and are presently under development, either from static or dynamic platforms. GNSS-R can be implemented at a low cost, primarily if small devices are mounted on-board unmanned aerial vehicles (UAVs), which today can be equipped with several types of sensors for environmental monitoring. So far, many instruments for GNSS-R have followed the GNSS bistatic radar architecture and consisted of custom GNSS receivers, often requiring a personal computer and bulky systems to store large amounts of data. This paper presents the development of a GNSS-based sensor for UAVs and small manned aircraft, used to classify lands according to their soil water content. The paper provides details on the design of the major hardware and software components, as well as the description of the results obtained through field tests. PMID:26569242

Prototyping a GNSS-Based Passive Radar for UAVs: An Instrument to Classify the Water Content Feature of Lands.

PubMed

Gamba, Micaela Troglia; Marucco, Gianluca; Pini, Marco; Ugazio, Sabrina; Falletti, Emanuela; Lo Presti, Letizia

2015-11-10

Global Navigation Satellite Systems (GNSS) broadcast signals for positioning and navigation, which can be also employed for remote sensing applications. Indeed, the satellites of any GNSS can be seen as synchronized sources of electromagnetic radiation, and specific processing of the signals reflected back from the ground can be used to estimate the geophysical properties of the Earth's surface. Several experiments have successfully demonstrated GNSS-reflectometry (GNSS-R), whereas new applications are continuously emerging and are presently under development, either from static or dynamic platforms. GNSS-R can be implemented at a low cost, primarily if small devices are mounted on-board unmanned aerial vehicles (UAVs), which today can be equipped with several types of sensors for environmental monitoring. So far, many instruments for GNSS-R have followed the GNSS bistatic radar architecture and consisted of custom GNSS receivers, often requiring a personal computer and bulky systems to store large amounts of data. This paper presents the development of a GNSS-based sensor for UAVs and small manned aircraft, used to classify lands according to their soil water content. The paper provides details on the design of the major hardware and software components, as well as the description of the results obtained through field tests.

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles.

PubMed

Liu, Zhong; Gao, Xiaoguang; Fu, Xiaowei

2018-05-08

In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

Evaluation of experimental UAV video change detection

NASA Astrophysics Data System (ADS)

Bartelsen, J.; Saur, G.; Teutsch, C.

2016-10-01

During the last ten years, the availability of images acquired from unmanned aerial vehicles (UAVs) has been continuously increasing due to the improvements and economic success of flight and sensor systems. From our point of view, reliable and automatic image-based change detection may contribute to overcoming several challenging problems in military reconnaissance, civil security, and disaster management. Changes within a scene can be caused by functional activities, i.e., footprints or skid marks, excavations, or humidity penetration; these might be recognizable in aerial images, but are almost overlooked when change detection is executed manually. With respect to the circumstances, these kinds of changes may be an indication of sabotage, terroristic activity, or threatening natural disasters. Although image-based change detection is possible from both ground and aerial perspectives, in this paper we primarily address the latter. We have applied an extended approach to change detection as described by Saur and Kruger,1 and Saur et al.2 and have built upon the ideas of Saur and Bartelsen.3 The commercial simulation environment Virtual Battle Space 3 (VBS3) is used to simulate aerial "before" and "after" image acquisition concerning flight path, weather conditions and objects within the scene and to obtain synthetic videos. Video frames, which depict the same part of the scene, including "before" and "after" changes and not necessarily from the same perspective, are registered pixel-wise against each other by a photogrammetric concept, which is based on a homography. The pixel-wise registration is used to apply an automatic difference analysis, which, to a limited extent, is able to suppress typical errors caused by imprecise frame registration, sensor noise, vegetation and especially parallax effects. The primary concern of this paper is to seriously evaluate the possibilities and limitations of our current approach for image-based change detection with respect to the flight path, viewpoint change and parametrization. Hence, based on synthetic "before" and "after" videos of a simulated scene, we estimated the precision and recall of automatically detected changes. In addition and based on our approach, we illustrate the results showing the change detection in short, but real video sequences. Future work will improve the photogrammetric approach for frame registration, and extensive real video material, capable of change detection, will be acquired.

An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

PubMed Central

Villa, Tommaso Francesco; Gonzalez, Felipe; Miljievic, Branka; Ristovski, Zoran D.; Morawska, Lidia

2016-01-01

Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research. PMID:27420065

A High-Throughput Processor for Flight Control Research Using Small UAVs

NASA Technical Reports Server (NTRS)

Klenke, Robert H.; Sleeman, W. C., IV; Motter, Mark A.

2006-01-01

There are numerous autopilot systems that are commercially available for small (<100 lbs) UAVs. However, they all share several key disadvantages for conducting aerodynamic research, chief amongst which is the fact that most utilize older, slower, 8- or 16-bit microcontroller technologies. This paper describes the development and testing of a flight control system (FCS) for small UAV s based on a modern, high throughput, embedded processor. In addition, this FCS platform contains user-configurable hardware resources in the form of a Field Programmable Gate Array (FPGA) that can be used to implement custom, application-specific hardware. This hardware can be used to off-load routine tasks such as sensor data collection, from the FCS processor thereby further increasing the computational throughput of the system.

Data Gathering and Energy Transfer Dilemma in UAV-Assisted Flying Access Network for IoT

PubMed Central

Arabi, Sara; Sadik, Mohamed

2018-01-01

Recently, Unmanned Aerial Vehicles (UAVs) have emerged as an alternative solution to assist wireless networks, thanks to numerous advantages they offer in comparison to terrestrial fixed base stations. For instance, a UAV can be used to embed a flying base station providing an on-demand nomadic access to network services. A UAV can also be used to wirelessly recharge out-of-battery ground devices. In this paper, we aim to deal with both data collection and recharging depleted ground Internet-of-Things (IoT) devices through a UAV station used as a flying base station. To extend the network lifetime, we present a novel use of UAV with energy harvesting module and wireless recharging capabilities. However, the UAV is used as an energy source to empower depleted IoT devices. On one hand, the UAV charges depleted ground IoT devices under three policies: (1) low-battery first scheme; (2) high-battery first scheme; and (3) random scheme. On the other hand, the UAV station collects data from IoT devices that have sufficient energy to transmit their packets, and in the same phase, the UAV exploits the Radio Frequency (RF) signals transmitted by IoT devices to extract and harvest energy. Furthermore, and as the UAV station has a limited coverage time due to its energy constraints, we propose and investigate an efficient trade-off between ground users recharging time and data gathering time. Furthermore, we suggest to control and optimize the UAV trajectory in order to complete its travel within a minimum time, while minimizing the energy spent and/or enhancing the network lifetime. Extensive numerical results and simulations show how the system behaves under different scenarios and using various metrics in which we examine the added value of UAV with energy harvesting module. PMID:29751662

Development of autonomous controller system of high speed UAV from simulation to ready to fly condition

NASA Astrophysics Data System (ADS)

Yudhi Irwanto, Herma

2018-02-01

The development of autonomous controller system that is specially used in our high speed UAV, it’s call RKX-200EDF/TJ controlled vehicle needs to be continued as a step to mastery and to developt control system of LAPAN’s satellite launching rocket. The weakness of the existing control system in this high speed UAV needs to be repaired and replaced using the autonomous controller system. Conversion steps for ready-to-fly system involved controlling X tail fin, adjusting auto take off procedure by adding X axis sensor, procedure of way points reading and process of measuring distance and heading to the nearest way point, developing user-friendly ground station, and adding tools for safety landing. The development of this autonomous controller system also covered a real flying test in Pandanwangi, Lumajang in November 2016. Unfortunately, the flying test was not successful because the booster rocket was blown right after burning. However, the system could record the event and demonstrated that the controller system had worked according to plan.

Shigaraki UAV-Radar Experiment (ShUREX): overview of the campaign with some preliminary results

NASA Astrophysics Data System (ADS)

Kantha, Lakshmi; Lawrence, Dale; Luce, Hubert; Hashiguchi, Hiroyuki; Tsuda, Toshitaka; Wilson, Richard; Mixa, Tyler; Yabuki, Masanori

2017-12-01

The Shigaraki unmanned aerial vehicle (UAV)-Radar Experiment (ShUREX) is an international (USA-Japan-France) observational campaign, whose overarching goal is to demonstrate the utility of small, lightweight, inexpensive, autonomous UAVs in probing and monitoring the lower troposphere and to promote synergistic use of UAVs and very high frequency (VHF) radars. The 2-week campaign lasting from June 1 to June 14, 2015, was carried out at the Middle and Upper Atmosphere (MU) Observatory in Shigaraki, Japan. During the campaign, the DataHawk UAV, developed at the University of Colorado, Boulder, and equipped with high-frequency response cold wire and pitot tube sensors (as well as an iMET radiosonde), was flown near and over the VHF-band MU radar. Measurements in the atmospheric column in the immediate vicinity of the radar were obtained. Simultaneous and continuous operation of the radar in range imaging mode enabled fine-scale structures in the atmosphere to be visualized by the radar. It also permitted the UAV to be commanded to sample interesting structures, guided in near real time by the radar images. This overview provides a description of the ShUREX campaign and some interesting but preliminary results of the very first simultaneous and intensive probing of turbulent structures by UAVs and the MU radar. The campaign demonstrated the validity and utility of the radar range imaging technique in obtaining very high vertical resolution ( 20 m) images of echo power in the atmospheric column, which display evolving fine-scale atmospheric structures in unprecedented detail. The campaign also permitted for the very first time the evaluation of the consistency of turbulent kinetic energy dissipation rates in turbulent structures inferred from the spectral broadening of the backscattered radar signal and direct, in situ measurements by the high-frequency response velocity sensor on the UAV. The data also enabled other turbulence parameters such as the temperature structure function parameter {C}_T^2 and refractive index structure function parameter {C}_n^2 to be measured by sensors on the UAV, along with radar-inferred refractive index structure function parameter {C}_{n,radar}^2 . The comprehensive dataset collected during the campaign (from the radar, the UAV, the boundary layer lidar, the ceilometer, and radiosondes) is expected to help obtain a better understanding of turbulent atmospheric structures, as well as arrive at a better interpretation of the radar data.

Close Range Uav Accurate Recording and Modeling of St-Pierre Neo-Romanesque Church in Strasbourg (france)

NASA Astrophysics Data System (ADS)

Murtiyoso, A.; Grussenmeyer, P.; Freville, T.

2017-02-01

Close-range photogrammetry is an image-based technique which has often been used for the 3D documentation of heritage objects. Recently, advances in the field of image processing and UAVs (Unmanned Aerial Vehicles) have resulted in a renewed interest in this technique. However, commercially ready-to-use UAVs are often equipped with smaller sensors in order to minimize payload and the quality of the documentation is still an issue. In this research, two commercial UAVs (the Sensefly Albris and DJI Phantom 3 Professional) were setup to record the 19th century St-Pierre-le-Jeune church in Strasbourg, France. Several software solutions (commercial and open source) were used to compare both UAVs' images in terms of calibration, accuracy of external orientation, as well as dense matching. Results show some instability in regards to the calibration of Phantom 3, while the Albris had issues regarding its aerotriangulation results. Despite these shortcomings, both UAVs succeeded in producing dense point clouds of up to a few centimeters in accuracy, which is largely sufficient for the purposes of a city 3D GIS (Geographical Information System). The acquisition of close range images using UAVs also provides greater LoD flexibility in processing. These advantages over other methods such as the TLS (Terrestrial Laser Scanning) or terrestrial close range photogrammetry can be exploited in order for these techniques to complement each other.

Taking flight with sensing equipment will deliver benefits across MDOT : research spotlight.

DOT National Transportation Integrated Search

2015-04-01

Recent strides in technology have opened the doors for using unmanned : aerial vehicles (UAVs, sometimes called drones) throughout MDOT. An : extensive study on the viability of UAVs instrumented with remote : sensors demonstrated a wide range of cos...

UAV-guided navigation for ground robot tele-operation in a military reconnaissance environment.

PubMed

Chen, Jessie Y C

2010-08-01

A military reconnaissance environment was simulated to examine the performance of ground robotics operators who were instructed to utilise streaming video from an unmanned aerial vehicle (UAV) to navigate his/her ground robot to the locations of the targets. The effects of participants' spatial ability on their performance and workload were also investigated. Results showed that participants' overall performance (speed and accuracy) was better when she/he had access to images from larger UAVs with fixed orientations, compared with other UAV conditions (baseline- no UAV, micro air vehicle and UAV with orbiting views). Participants experienced the highest workload when the UAV was orbiting. Those individuals with higher spatial ability performed significantly better and reported less workload than those with lower spatial ability. The results of the current study will further understanding of ground robot operators' target search performance based on streaming video from UAVs. The results will also facilitate the implementation of ground/air robots in military environments and will be useful to the future military system design and training community.

Design and implementation of a remote UAV-based mobile health monitoring system

NASA Astrophysics Data System (ADS)

Li, Songwei; Wan, Yan; Fu, Shengli; Liu, Mushuang; Wu, H. Felix

2017-04-01

Unmanned aerial vehicles (UAVs) play increasing roles in structure health monitoring. With growing mobility in modern Internet-of-Things (IoT) applications, the health monitoring of mobile structures becomes an emerging application. In this paper, we develop a UAV-carried vision-based monitoring system that allows a UAV to continuously track and monitor a mobile infrastructure and transmit back the monitoring information in real- time from a remote location. The monitoring system uses a simple UAV-mounted camera and requires only a single feature located on the mobile infrastructure for target detection and tracking. The computation-effective vision-based tracking solution based on a single feature is an improvement over existing vision-based lead-follower tracking systems that either have poor tracking performance due to the use of a single feature, or have improved tracking performance at a cost of the usage of multiple features. In addition, a UAV-carried aerial networking infrastructure using directional antennas is used to enable robust real-time transmission of monitoring video streams over a long distance. Automatic heading control is used to self-align headings of directional antennas to enable robust communication in mobility. Compared to existing omni-communication systems, the directional communication solution significantly increases the operation range of remote monitoring systems. In this paper, we develop the integrated modeling framework of camera and mobile platforms, design the tracking algorithm, develop a testbed of UAVs and mobile platforms, and evaluate system performance through both simulation studies and field tests.

Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak

NASA Astrophysics Data System (ADS)

Dash, Jonathan P.; Watt, Michael S.; Pearse, Grant D.; Heaphy, Marie; Dungey, Heidi S.

2017-09-01

Research into remote sensing tools for monitoring physiological stress caused by biotic and abiotic factors is critical for maintaining healthy and highly-productive plantation forests. Significant research has focussed on assessing forest health using remotely sensed data from satellites and manned aircraft. Unmanned aerial vehicles (UAVs) may provide new tools for improved forest health monitoring by providing data with very high temporal and spatial resolutions. These platforms also pose unique challenges and methods for health assessments must be validated before use. In this research, we simulated a disease outbreak in mature Pinus radiata D. Don trees using targeted application of herbicide. The objective was to acquire a time-series simulated disease expression dataset to develop methods for monitoring physiological stress from a UAV platform. Time-series multi-spectral imagery was acquired using a UAV flown over a trial at regular intervals. Traditional field-based health assessments of crown health (density) and needle health (discolouration) were carried out simultaneously by experienced forest health experts. Our results showed that multi-spectral imagery collected from a UAV is useful for identifying physiological stress in mature plantation trees even during the early stages of tree stress. We found that physiological stress could be detected earliest in data from the red edge and near infra-red bands. In contrast to previous findings, red edge data did not offer earlier detection of physiological stress than the near infra-red data. A non-parametric approach was used to model physiological stress based on spectral indices and was found to provide good classification accuracy (weighted kappa = 0.694). This model can be used to map physiological stress based on high-resolution multi-spectral data.

Cooperative Monocular-Based SLAM for Multi-UAV Systems in GPS-Denied Environments †

PubMed Central

Guerra, Edmundo

2018-01-01

This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation. PMID:29701722

Cooperative Monocular-Based SLAM for Multi-UAV Systems in GPS-Denied Environments.

PubMed

Trujillo, Juan-Carlos; Munguia, Rodrigo; Guerra, Edmundo; Grau, Antoni

2018-04-26

This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.

Open source software and low cost sensors for teaching UAV science

NASA Astrophysics Data System (ADS)

Kefauver, S. C.; Sanchez-Bragado, R.; El-Haddad, G.; Araus, J. L.

2016-12-01

Drones, also known as UASs (unmanned aerial systems), UAVs (Unmanned Aerial Vehicles) or RPAS (Remotely piloted aircraft systems), are both useful advanced scientific platforms and recreational toys that are appealing to younger generations. As such, they can make for excellent education tools as well as low-cost scientific research project alternatives. However, the process of taking pretty pictures to remote sensing science can be daunting if one is presented with only expensive software and sensor options. There are a number of open-source tools and low cost platform and sensor options available that can provide excellent scientific research results, and, by often requiring more user-involvement than commercial software and sensors, provide even greater educational benefits. Scale-invariant feature transform (SIFT) algorithm implementations, such as the Microsoft Image Composite Editor (ICE), which can create quality 2D image mosaics with some motion and terrain adjustments and VisualSFM (Structure from Motion), which can provide full image mosaicking with movement and orthorectification capacities. RGB image quantification using alternate color space transforms, such as the BreedPix indices, can be calculated via plugins in the open-source software Fiji (http://fiji.sc/Fiji; http://github.com/george-haddad/CIMMYT). Recent analyses of aerial images from UAVs over different vegetation types and environments have shown RGB metrics can outperform more costly commercial sensors. Specifically, Hue-based pixel counts, the Triangle Greenness Index (TGI), and the Normalized Green Red Difference Index (NGRDI) consistently outperformed NDVI in estimating abiotic and biotic stress impacts on crop health. Also, simple kits are available for NDVI camera conversions. Furthermore, suggestions for multivariate analyses of the different RGB indices in the "R program for statistical computing", such as classification and regression trees can allow for a more approachable interpretation of results in the classroom.

Fusion of spatio-temporal UAV and proximal sensing data for an agricultural decision support system

NASA Astrophysics Data System (ADS)

Katsigiannis, P.; Galanis, G.; Dimitrakos, A.; Tsakiridis, N.; Kalopesas, C.; Alexandridis, T.; Chouzouri, A.; Patakas, A.; Zalidis, G.

2016-08-01

Over the last few years, multispectral and thermal remote sensing imagery from unmanned aerial vehicles (UAVs) has found application in agriculture and has been regarded as a means of field data collection and crop condition monitoring source. The integration of information derived from the analysis of these remotely sensed data into agricultural management applications facilitates and aids the stakeholder's decision making. Whereas agricultural decision support systems (DSS) have long been utilised in farming applications, there are still critical gaps to be addressed; as the current approach often neglects the plant's level information and lacks the robustness to account for the spatial and temporal variability of environmental parameters within agricultural systems. In this paper, we demonstrate the use of a custom built autonomous UAV platform in providing critical information for an agricultural DSS. This hexacopter UAV bears two cameras which can be triggered simultaneously and can capture both the visible, near-infrared (VNIR) and the thermal infrared (TIR) wavelengths. The platform was employed for the rapid extraction of the normalized difference vegetation index (NDVI) and the crop water stress index (CWSI) of three different plantations, namely a kiwi, a pomegranate, and a vine field. The simultaneous recording of these two complementary indices and the creation of maps was advantageous for the accurate assessment of the plantation's status. Fusion of UAV and soil scanner system products pinpointed the necessity for adjustment of the irrigation management applied. It is concluded that timely CWSI and NDVI measures retrieved for different crop growing stages can provide additional information and can serve as a tool to support the existing irrigation DSS that had so far been exclusively based on telemetry data from soil and agrometeorological sensors. Additionally, the use of the multi-sensor UAV was found to be beneficial in collecting timely, spatio-temporal information for the fusion with ground-based proximal sensing data. This research work was designed and deployed in the frame of the project "AGRO_LESS: Joint reference strategies for rural activities of reduced inputs".

Measuring orthometric water heights from lightweight Unmanned Aerial Vehicles (UAVs)

NASA Astrophysics Data System (ADS)

Bandini, Filippo; Olesen, Daniel; Jakobsen, Jakob; Reyna-Gutierrez, Jose Antonio; Bauer-Gottwein, Peter

2016-04-01

A better quantitative understanding of hydrologic processes requires better observations of hydrological variables, such as surface water area, water surface level, its slope and its temporal change. However, ground-based measurements of water heights are restricted to the in-situ measuring stations. Hence, the objective of remote sensing hydrology is to retrieve these hydraulic variables from spaceborne and airborne platforms. The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will be able to acquire water heights with an expected accuracy of 10 centimeters for rivers that are at least 100 m wide. Nevertheless, spaceborne missions will always face the limitations of: i) a low spatial resolution which makes it difficult to separate water from interfering surrounding areas and a tracking of the terrestrial water bodies not able to detect water heights in small rivers or lakes; ii) a limited temporal resolution which limits the ability to determine rapid temporal changes, especially during extremes. Unmanned Aerial Vehicles (UAVs) are one technology able to fill the gap between spaceborne and ground-based observations, ensuring 1) high spatial resolution; 2) tracking of the water bodies better than any satellite technology; 3) timing of the sampling which only depends on the operator 4) flexibility of the payload. Hence, this study focused on categorizing and testing sensors capable of measuring the range between the UAV and the water surface. The orthometric height of the water surface is then retrieved by subtracting the height above water measured by the sensors from the altitude above sea level retrieved by the onboard GPS. The following sensors were tested: a) a radar, b) a sonar c) a laser digital-camera based prototype developed at Technical University of Denmark. The tested sensors comply with the weight constraint of small UAVs (around 1.5 kg). The sensors were evaluated in terms of accuracy, maximum ranging distance and beam divergence. The sonar demonstrated a maximum ranging distance of 10 m, the laser prototype of 15 m, whilst the radar is potentially able to measure the range to water surface from a height up to 50 m. After numerous test flights above a lake with an approximately horizontal water surface, estimation of orthometric water height error, including overall accuracy of the system GPS-sensors, was possible. The RTK GPS system proved able to deliver a relative vertical accuracy better than 5-7 cm. The radar confirmed to have the best reliability with an accuracy which is generally few cm (0.7-1.3% of the ranging distance). Whereas the accuracy of the sonar and laser varies from few cm (0.7-1.6% of the ranging distance) to some tens of cm because sonar measurements are generally influenced by noise and turbulence generated by the propellers of the UAV and the laser prototype is affected by drone vibrations and water waviness. However, the laser prototype demonstrated the lowest beam divergence, which is required to measure unconventional remote sensing targets, such as sinkholes and Mexican cenotes, and to clearly distinguish between rivers and interfering surroundings, such as riparian vegetation.

A Generic, Agent-Based Framework for Design and Development of UAV/UCAV Control Systems

DTIC Science & Technology

2004-02-27

37 EID Principles .................................................................................................. 38 Experimental Support for EID...Year 2 Interface design and implementation; creation of the simulation environment; Year 3 Demonstration of the concept and experimental evaluation...UAV/UCAV control in which operators can experience high cognitive workloads. There are several ways in which systems can construct user models by

Optimal trajectory planning for a UAV glider using atmospheric thermals

NASA Astrophysics Data System (ADS)

Kagabo, Wilson B.

An Unmanned Aerial Vehicle Glider (UAV glider) uses atmospheric energy in its different forms to remain aloft for extended flight durations. This UAV glider's aim is to extract atmospheric thermal energy and use it to supplement its battery energy usage and increase the mission period. Given an infrared camera identified atmospheric thermal of known strength and location; current wind speed and direction; current battery level; altitude and location of the UAV glider; and estimating the expected altitude gain from the thermal, is it possible to make an energy-efficient based motivation to fly to an atmospheric thermal so as to achieve UAV glider extended flight time? For this work, an infrared thermal camera aboard the UAV glider takes continuous forward-looking ground images of "hot spots". Through image processing a candidate atmospheric thermal strength and location is estimated. An Intelligent Decision Model incorporates this information with the current UAV glider status and weather conditions to provide an energy-based recommendation to modify the flight path of the UAV glider. Research, development, and simulation of the Intelligent Decision Model is the primary focus of this work. Three models are developed: (1) Battery Usage Model, (2) Intelligent Decision Model, and (3) Altitude Gain Model. The Battery Usage Model comes from the candidate flight trajectory, wind speed & direction and aircraft dynamic model. Intelligent Decision Model uses a fuzzy logic based approach. The Altitude Gain Model requires the strength and size of the thermal and is found a priori.

Fleets of enduring drones to probe atmospheric phenomena with clouds

NASA Astrophysics Data System (ADS)

Lacroix, Simon; Roberts, Greg; Benard, Emmanuel; Bronz, Murat; Burnet, Frédéric; Bouhoubeiny, Elkhedim; Condomines, Jean-Philippe; Doll, Carsten; Hattenberger, Gautier; Lamraoui, Fayçal; Renzaglia, Alessandro; Reymann, Christophe

2016-04-01

A full spatio-temporal four-dimensional characterization of the microphysics and dynamics of cloud formation including the onset of precipitation has never been reached. Such a characterization would yield a better understanding of clouds, e.g. to assess the dominant mixing mechanism and the main source of cloudy updraft dilution. It is the sampling strategy that matters: fully characterizing the evolution over time of the various parameters (P, T, 3D wind, liquid water content, aerosols...) within a cloud volume requires dense spatial sampling for durations of the order of one hour. A fleet of autonomous lightweight UAVs that coordinate themselves in real-time as an intelligent network can fulfill this purpose. The SkyScanner project targets the development of a fleet of autonomous UAVs to adaptively sample cumuli, so as to provide relevant data to address long standing questions in atmospheric science. It mixes basic researches and experimental developments, and gathers scientists in UAV conception, in optimal flight control, in intelligent cooperative behaviors, and of course atmospheric scientists. Two directions of researches are explored: optimal UAV conception and control, and optimal control of a fleet of UAVs. The design of UAVs for atmospheric science involves the satisfaction of trade-offs between payload, endurance, ease of deployment... A rational conception scheme that integrates the constraints to optimize a series of criteria, in particular energy consumption, would yield the definition of efficient UAVs. This requires a fine modeling of each involved sub-system and phenomenon, from the motor/propeller efficiency to the aerodynamics at small scale, including the flight control algorithms. The definition of mission profiles is also essential, considering the aerodynamics of clouds, to allow energy harvesting schemes that exploit thermals or gusts. The conception also integrates specific sensors, in particular wind sensor, for which classic technologies are challenged at the low speeds of lightweight UAVs. The overall control of the fleet so as to gather series of synchronized data in the cloud volume is a poorly informed and highly constrained adaptive sampling problem, in which the UAV motions must be defined to maximize the amount of gathered information and the mission duration. The overall approach casts the problem in a hierarchy of two modeling and decision stages. A macroscopic parametrized model of the cloud is built from the gathered data and exploited at the higher level by an operator, who sets information gathering goals. A subset of the UAV fleet is allocated to each goal, considering the current fleet state. These high level goals are handled by the lower level, which autonomously optimizes the selected UAVs trajectories using an on-line updated dense model of the variables of interest. Building the models involves Gaussian processes techniques (kriging) to fuse the gathered data with a generic cumulus conceptual model, the latter being defined from thorough statistics on realistic MesoNH cloud simulations. The model is exploited by a planner to generate trajectories that minimize the uncertainty in the map, while steering the vehicles within the air flows to save energy.

[Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data].

PubMed

Gao, Lin; Li, Chang-chun; Wang, Bao-shan; Yang Gui-jun; Wang, Lei; Fu, Kui

2016-01-01

With the innovation of remote sensing technology, remote sensing data sources are more and more abundant. The main aim of this study was to analyze retrieval accuracy of soybean leaf area index (LAI) based on multi-source remote sensing data including ground hyperspectral, unmanned aerial vehicle (UAV) multispectral and the Gaofen-1 (GF-1) WFV data. Ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), and triangle vegetation index (TVI) were used to establish LAI retrieval models, respectively. The models with the highest calibration accuracy were used in the validation. The capability of these three kinds of remote sensing data for LAI retrieval was assessed according to the estimation accuracy of models. The experimental results showed that the models based on the ground hyperspectral and UAV multispectral data got better estimation accuracy (R² was more than 0.69 and RMSE was less than 0.4 at 0.01 significance level), compared with the model based on WFV data. The RVI logarithmic model based on ground hyperspectral data was little superior to the NDVI linear model based on UAV multispectral data (The difference in E(A), R² and RMSE were 0.3%, 0.04 and 0.006, respectively). The models based on WFV data got the lowest estimation accuracy with R2 less than 0.30 and RMSE more than 0.70. The effects of sensor spectral response characteristics, sensor geometric location and spatial resolution on the soybean LAI retrieval were discussed. The results demonstrated that ground hyperspectral data were advantageous but not prominent over traditional multispectral data in soybean LAI retrieval. WFV imagery with 16 m spatial resolution could not meet the requirements of crop growth monitoring at field scale. Under the condition of ensuring the high precision in retrieving soybean LAI and working efficiently, the approach to acquiring agricultural information by UAV remote sensing could yet be regarded as an optimal plan. Therefore, in the case of more and more available remote sensing information sources, agricultural UAV remote sensing could become an important information resource for guiding field-scale crop management and provide more scientific and accurate information for precision agriculture research.

Validation of Inertial and Optical Navigation Techniques for Space Applications with UAVS

NASA Astrophysics Data System (ADS)

Montaño, J.; Wis, M.; Pulido, J. A.; Latorre, A.; Molina, P.; Fernández, E.; Angelats, E.; Colomina, I.

2015-09-01

PERIGEO is an R&D project, funded by the INNPRONTA 2011-2014 programme from Spanish CDTI, which aims to investigate the use of UAV technologies and processes for the validation of space oriented technologies. For this purpose, among different space missions and technologies, a set of activities for absolute and relative navigation are being carried out to deal with the attitude and position estimation problem from a temporal image sequence from a camera on the visible spectrum and/or Light Detection and Ranging (LIDAR) sensor. The process is covered entirely: from sensor measurements and data acquisition (images, LiDAR ranges and angles), data pre-processing (calibration and co-registration of camera and LIDAR data), features and landmarks extraction from the images and image/LiDAR-based state estimation. In addition to image processing area, classical navigation system based on inertial sensors is also included in the research. The reason of combining both approaches is to enable the possibility to keep navigation capability in environments or missions where the radio beacon or reference signal as the GNSS satellite is not available (as for example an atmospheric flight in Titan). The rationale behind the combination of those systems is that they complement each other. The INS is capable of providing accurate position, velocity and full attitude estimations at high data rates. However, they need an absolute reference observation to compensate the time accumulative errors caused by inertial sensor inaccuracies. On the other hand, imaging observables can provide absolute and relative positioning and attitude estimations. However they need that the sensor head is pointing toward ground (something that may not be possible if the carrying platform is maneuvering) to provide accurate estimations and they are not capable of provide some hundreds of Hz that can deliver an INS. This mutual complementarity has been observed in PERIGEO and because of this they are combined into one system. The inertial navigation system implemented in PERIGEO is based on a classical loosely coupled INS/GNSS approach that is very similar to the implementation of the INS/Imaging navigation system that is mentioned above. The activities envisaged in PERIGEO cover the algorithms development and validation and technology testing on UAVs under representative conditions. Past activities have covered the design and development of the algorithms and systems. This paper presents the most recent activities and results on the area of image processing for robust estimation within PERIGEO, which are related with the hardware platforms definition (including sensors) and its integration in UAVs. Results for the tests performed during the flight campaigns in representative outdoor environments will be also presented (at the time of the full paper submission the tests will be performed), as well as analyzed, together with a roadmap definition for future developments.

Cooperative Mobile Sensing Systems for In Situ Measurements in Hazardous Environments

NASA Astrophysics Data System (ADS)

Argrow, B.

2005-12-01

Sondes are typically deployed from manned aircraft or taken to altitude by a balloon before they are dropped. There are obvious safety and physical limitations that dictate where and how sondes are deployed. These limitations have severely constrained sonde deployment into highly dynamic and dangerous environments. Additionally, conventional parachute dropsondes provide no means for active control. The "smartsonde" idea is to integrate miniature sonde packages into micro air vehicles (MAVs). These MAVs will be ferried into the hard to reach and hazardous environments to provide in situ measurements in regions that have been heretofore out of reach. Once deployed, the MAV will provide some means of control of the sonde, to enable it to remain aloft and to provide some measure of directional control. Preliminary smartsonde communications experiments have been completed. These experiments focused on characterizing the capabilities of the 802.11.4 wireless protocol. Range measurements with 60-mW, 2.4-GHz radios showed 100% throughput rate over 2.7 km during air to ground tests. The experiments also demonstrated the integration of an in-house distributed computing system that provides the interface between the sensors, UAV flight computers, and the telemetry system. The University of Colorado's Research and Engineering Center for Unmanned Vehicles (RECUV) is developing an engineering system that integrates small mobile sensor attributes into flexible mobile sensor infrastructures to be deployed for in situ sensing in hazardous environments. There are three focus applications: 1) Wildfire, to address sensing, communications, situational awareness, and safety needs to support fire-fighting operations and to increase capabilities for dynamic data acquisition for modeling and prediction; 2) Polar, where heterogeneous mixes of platforms and sensors will provide in-situ data acquisition from beneath the ocean surface into the troposphere; 3) Storm, to address the challenges of volumetric in-situ data acquisition in the extremely dynamic environments of severe storms. The common thread among these applications is the need for a cooperative mobile sensing system, where sensor packages are integrated into custom platforms that enable targeting of areas of interest through the cooperative control, with varying levels of autonomy, of small unmanned vehicles. RECUV has demonstrated mobile ad hoc networks using WiFi (802.11b) radios simultaneously deployed in fixed and mobile ground nodes and unmanned aerial vehicles (UAVs). Recently, an autonomous UAV was deployed with a miniature sensor package that returned real-time temperature, pressure, and humidity data, through the ad hoc communications network. The UAV demonstrated the ability to autonomously make flight-path decisions based on the sensor data that was monitored by the flight computer. Current work is now focused on integrating the sensor package into a smartsonde to be deployed from a UAV mothership. Benign scenarios for upcoming tests to validate the collaborative mobile sensing system paradigm include scenarios with features similar to those that will be encountered in the hazardous and dynamic environments a of the wildfire, polar, and storm applications. These include a fly-through of a dust devil on the planes of eastern Colorado and deployment of a dual-mode smartsonde that transmits at high data rates while airborne then, upon landing, it switches to quiet, power-saving mode , where in situ data is logged and only transmitted when the sonde package is queried during overflights of a UAV mothership.

UAV-based high-throughput phenotyping in legume crops

NASA Astrophysics Data System (ADS)

Sankaran, Sindhuja; Khot, Lav R.; Quirós, Juan; Vandemark, George J.; McGee, Rebecca J.

2016-05-01

In plant breeding, one of the biggest obstacles in genetic improvement is the lack of proven rapid methods for measuring plant responses in field conditions. Therefore, the major objective of this research was to evaluate the feasibility of utilizing high-throughput remote sensing technology for rapid measurement of phenotyping traits in legume crops. The plant responses of several chickpea and peas varieties to the environment were assessed with an unmanned aerial vehicle (UAV) integrated with multispectral imaging sensors. Our preliminary assessment showed that the vegetation indices are strongly correlated (p<0.05) with seed yield of legume crops. Results endorse the potential of UAS-based sensing technology to rapidly measure those phenotyping traits.

Data Acquisition (DAQ) system dedicated for remote sensing applications on Unmanned Aerial Vehicles (UAV)

NASA Astrophysics Data System (ADS)

Keleshis, C.; Ioannou, S.; Vrekoussis, M.; Levin, Z.; Lange, M. A.

2014-08-01

Continuous advances in unmanned aerial vehicles (UAV) and the increased complexity of their applications raise the demand for improved data acquisition systems (DAQ). These improvements may comprise low power consumption, low volume and weight, robustness, modularity and capability to interface with various sensors and peripherals while maintaining the high sampling rates and processing speeds. Such a system has been designed and developed and is currently integrated on the Autonomous Flying Platforms for Atmospheric and Earth Surface Observations (APAESO/NEA-YΠOΔOMH/NEKΠ/0308/09) however, it can be easily adapted to any UAV or any other mobile vehicle. The system consists of a single-board computer with a dual-core processor, rugged surface-mount memory and storage device, analog and digital input-output ports and many other peripherals that enhance its connectivity with various sensors, imagers and on-board devices. The system is powered by a high efficiency power supply board. Additional boards such as frame-grabbers, differential global positioning system (DGPS) satellite receivers, general packet radio service (3G-4G-GPRS) modems for communication redundancy have been interfaced to the core system and are used whenever there is a mission need. The onboard DAQ system can be preprogrammed for automatic data acquisition or it can be remotely operated during the flight from the ground control station (GCS) using a graphical user interface (GUI) which has been developed and will also be presented in this paper. The unique design of the GUI and the DAQ system enables the synchronized acquisition of a variety of scientific and UAV flight data in a single core location. The new DAQ system and the GUI have been successfully utilized in several scientific UAV missions. In conclusion, the novel DAQ system provides the UAV and the remote-sensing community with a new tool capable of reliably acquiring, processing, storing and transmitting data from any sensor integrated on an UAV.

Designing and Testing a UAV Mapping System for Agricultural Field Surveying

PubMed Central

Skovsen, Søren

2017-01-01

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35–0.58 m are correlated to the applied nitrogen treatments of 0–300 kgNha. The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations. PMID:29168783

Designing and Testing a UAV Mapping System for Agricultural Field Surveying.

PubMed

Christiansen, Martin Peter; Laursen, Morten Stigaard; Jørgensen, Rasmus Nyholm; Skovsen, Søren; Gislum, René

2017-11-23

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35-0.58 m are correlated to the applied nitrogen treatments of 0-300 kg N ha . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

Adaptive pattern for autonomous UAV guidance

NASA Astrophysics Data System (ADS)

Sung, Chen-Ko; Segor, Florian

2013-09-01

The research done at the Fraunhofer IOSB in Karlsruhe within the AMFIS project is focusing on a mobile system to support rescue forces in accidents or disasters. The system consists of a ground control station which has the capability to communicate with a large number of heterogeneous sensors and sensor carriers and provides several open interfaces to allow easy integration of additional sensors into the system. Within this research we focus mainly on UAV such as VTOL (Vertical takeoff and Landing) systems because of their ease of use and their high maneuverability. To increase the positioning capability of the UAV, different onboard processing chains of image exploitation for real time detection of patterns on the ground and the interfacing technology for controlling the UAV from the payload during flight were examined. The earlier proposed static ground pattern was extended by an adaptive component which admits an additional visual communication channel to the aircraft. For this purpose different components were conceived to transfer additive information using changeable patterns on the ground. The adaptive ground pattern and their application suitability had to be tested under external influence. Beside the adaptive ground pattern, the onboard process chains and the adaptations to the demands of changing patterns are introduced in this paper. The tracking of the guiding points, the UAV navigation and the conversion of the guiding point positions from the images to real world co-ordinates in video sequences, as well as use limits and the possibilities of an adaptable pattern are examined.

Army AL&T, April-June 2008

DTIC Science & Technology

2008-06-01

IR )/laser designator (LD)/laser range finder (LRF) sensor. The Class I UAS consists of a Class I UAV, a cen- tralized controller and a minimal set...utility of a backpackable, affordable, easy-to- operate and responsive reconnais- sance and surveillance system through experimentation. • Use EO/ IR ...ARMY AL&T 33APRIL - JUNE 2008 • “The IR sensor pinpointed the enemy even after the sun went down. We could have really used this in Iraq.” • “The UAV

Assessing the Utility of Uav-Borne Hyperspectral Image and Photogrammetry Derived 3d Data for Wetland Species Distribution Quick Mapping

NASA Astrophysics Data System (ADS)

Li, Q. S.; Wong, F. K. K.; Fung, T.

2017-08-01

Lightweight unmanned aerial vehicle (UAV) loaded with novel sensors offers a low cost and minimum risk solution for data acquisition in complex environment. This study assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area of Hong Kong. Multiple feature reduction methods and different classifiers were compared. The best result was obtained when transformed components from minimum noise fraction (MNF) and DSM were combined in support vector machine (SVM) classifier. Wavelength regions at chlorophyll absorption green peak, red, red edge and Oxygen absorption at near infrared were identified for better species discrimination. In addition, input of DSM data reduces overestimation of low plant species and misclassification due to the shadow effect and inter-species morphological variation. This study establishes a framework for quick survey and update on wetland environment using UAV system. The findings indicate that the utility of UAV-borne hyperspectral and derived tree height information provides a solid foundation for further researches such as biological invasion monitoring and bio-parameters modelling in wetland.

A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs

NASA Astrophysics Data System (ADS)

Yahyanejad, Saeed; Rinner, Bernhard

2015-06-01

The use of multiple small-scale UAVs to support first responders in disaster management has become popular because of their speed and low deployment costs. We exploit such UAVs to perform real-time monitoring of target areas by fusing individual images captured from heterogeneous aerial sensors. Many approaches have already been presented to register images from homogeneous sensors. These methods have demonstrated robustness against scale, rotation and illumination variations and can also cope with limited overlap among individual images. In this paper we focus on thermal and visual image registration and propose different methods to improve the quality of interspectral registration for the purpose of real-time monitoring and mobile mapping. Images captured by low-altitude UAVs represent a very challenging scenario for interspectral registration due to the strong variations in overlap, scale, rotation, point of view and structure of such scenes. Furthermore, these small-scale UAVs have limited processing and communication power. The contributions of this paper include (i) the introduction of a feature descriptor for robustly identifying corresponding regions of images in different spectrums, (ii) the registration of image mosaics, and (iii) the registration of depth maps. We evaluated the first method using a test data set consisting of 84 image pairs. In all instances our approach combined with SIFT or SURF feature-based registration was superior to the standard versions. Although we focus mainly on aerial imagery, our evaluation shows that the presented approach would also be beneficial in other scenarios such as surveillance and human detection. Furthermore, we demonstrated the advantages of the other two methods in case of multiple image pairs.

a Sensor Aided H.264/AVC Video Encoder for Aerial Video Sequences with in the Loop Metadata Correction

NASA Astrophysics Data System (ADS)

Cicala, L.; Angelino, C. V.; Ruatta, G.; Baccaglini, E.; Raimondo, N.

2015-08-01

Unmanned Aerial Vehicles (UAVs) are often employed to collect high resolution images in order to perform image mosaicking and/or 3D reconstruction. Images are usually stored on board and then processed with on-ground desktop software. In such a way the computational load, and hence the power consumption, is moved on ground, leaving on board only the task of storing data. Such an approach is important in the case of small multi-rotorcraft UAVs because of their low endurance due to the short battery life. Images can be stored on board with either still image or video data compression. Still image system are preferred when low frame rates are involved, because video coding systems are based on motion estimation and compensation algorithms which fail when the motion vectors are significantly long and when the overlapping between subsequent frames is very small. In this scenario, UAVs attitude and position metadata from the Inertial Navigation System (INS) can be employed to estimate global motion parameters without video analysis. A low complexity image analysis can be still performed in order to refine the motion field estimated using only the metadata. In this work, we propose to use this refinement step in order to improve the position and attitude estimation produced by the navigation system in order to maximize the encoder performance. Experiments are performed on both simulated and real world video sequences.

Vision and Control for UAVs: A Survey of General Methods and of Inexpensive Platforms for Infrastructure Inspection

PubMed Central

Máthé, Koppány; Buşoniu, Lucian

2015-01-01

Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. In this report, we survey vision and control methods that can be applied to low-cost UAVs, and we list some popular inexpensive platforms and application fields where they are useful. We also highlight the sensor suites used where this information is available. We overview, among others, feature detection and tracking, optical flow and visual servoing, low-level stabilization and high-level planning methods. We then list popular low-cost UAVs, selecting mainly quadrotors. We discuss applications, restricting our focus to the field of infrastructure inspection. Finally, as an example, we formulate two use-cases for railway inspection, a less explored application field, and illustrate the usage of the vision and control techniques reviewed by selecting appropriate ones to tackle these use-cases. To select vision methods, we run a thorough set of experimental evaluations. PMID:26121608

DDDAMS-based Urban Surveillance and Crowd Control via UAVs and UGVs

DTIC Science & Technology

2015-12-04

for crowd dynamics modeling by incorporating multi-resolution data, where a grid-based method is used to model crowd motion with UAVs’ low -resolution...information and more computational intensive (and time-consuming). Given that the deployment of fidelity selection results in simulation faces computational... low fidelity information FOV y (A) DR x (A) DR y (A) Not detected high fidelity information Table 1: Parameters for UAV and UGV for their detection

Micro-UAV tracking framework for EO exploitation

NASA Astrophysics Data System (ADS)

Browning, David; Wilhelm, Joe; Van Hook, Richard; Gallagher, John

2012-06-01

Historically, the Air Force's research into aerial platforms for sensing systems has focused on low-, mid-, and highaltitude platforms. Though these systems are likely to comprise the majority of the Air Force's assets for the foreseeable future, they have limitations. Specifically, these platforms, their sensor packages, and their data exploitation software are unsuited for close-quarter surveillance, such as in alleys and inside of buildings. Micro-UAVs have been gaining in popularity, especially non-fixed-wing platforms such as quad-rotors. These platforms are much more appropriate for confined spaces. However, the types of video exploitation techniques that can effectively be used are different from the typical nadir-looking aerial platform. This paper discusses the creation of a framework for testing existing and new video exploitation algorithms, as well as describes a sample micro-UAV-based tracker.

Application Possibility of Smartphone as Payload for Photogrammetric Uav System

NASA Astrophysics Data System (ADS)

Yun, M. H.; Kim, J.; Seo, D.; Lee, J.; Choi, C.

2012-07-01

Smartphone can not only be operated under 3G network environment anytime and anyplace but also cost less than the existing photogrammetric UAV since it provides high-resolution image, 3D location and attitude data on a real-time basis from a variety of built-in sensors. This study is aimed to assess the possibility of smartphone as a payload for photogrammetric UAV system. Prior to such assessment, a smartphone-based photogrammetric UAV system application was developed, through which real-time image, location and attitude data was obtained using smartphone under both static and dynamic conditions. Subsequently the accuracy assessment on the location and attitude data obtained and sent by this system was conducted. The smartphone images were converted into ortho-images through image triangulation. The image triangulation was conducted in accordance with presence or absence of consideration of the interior orientation (IO) parameters determined by camera calibration. In case IO parameters were taken into account in the static experiment, the results from triangulation for any smartphone type were within 1.5 pixel (RMSE), which was improved at least by 35% compared to when IO parameters were not taken into account. On the contrary, the improvement effect of considering IO parameters on accuracy in triangulation for smartphone images in dynamic experiment was not significant compared to the static experiment. It was due to the significant impact of vibration and sudden attitude change of UAV on the actuator for automatic focus control within the camera built in smartphone under the dynamic condition. This cause appears to have a negative impact on the image-based DEM generation. Considering these study findings, it is suggested that smartphone is very feasible as a payload for UAV system. It is also expected that smartphone may be loaded onto existing UAV playing direct or indirect roles significantly.

ARBRES: Light-Weight CW/FM SAR Sensors for Small UAVs

PubMed Central

Aguasca, Albert; Acevo-Herrera, Rene; Broquetas, Antoni; Mallorqui, Jordi J.; Fabregas, Xavier

2013-01-01

This paper describes a pair of compact CW/FM airborne SAR systems for small UAV-based operation (wingspan of 3.5 m) for low-cost testing of innovative SAR concepts. Two different SAR instruments, using the C and X bands, have been developed in the context of the ARBRES project, each of them achieving a payload weight below 5 Kg and a volume of 13.5 dm3 (sensor and controller). Every system has a dual receiving channel which allows operation in interferometric or polarimetric modes. Planar printed array antennas are used in both sensors for easy system integration and better isolation between transmitter and receiver subsystems. First experimental tests on board a 3.2 m wingspan commercial radio-controlled aircraft are presented. The SAR images of a field close to an urban area have been focused using a back-projection algorithm. Using the dual channel capability, a single pass interferogram and Digital Elevation Model (DEM) has been obtained which agrees with the scene topography. A simple Motion Compensation (MoCo) module, based on the information from an Inertial+GPS unit, has been included to compensate platform motion errors with respect to the nominal straight trajectory. PMID:23467032

Initial Study of an Effective Fast-Time Simulation Platform for Unmanned Aircraft System Traffic Management

NASA Technical Reports Server (NTRS)

Xue, Min; Rios, Joseph

2017-01-01

Small Unmanned Aerial Vehicles (sUAVs), typically 55 lbs and below, are envisioned to play a major role in surveilling critical assets, collecting important information, and delivering goods. Large scale small UAV operations are expected to happen in low altitude airspace in the near future. Many static and dynamic constraints exist in low altitude airspace because of manned aircraft or helicopter activities, various wind conditions, restricted airspace, terrain and man-made buildings, and conflict-avoidance among sUAVs. High sensitivity and high maneuverability are unique characteristics of sUAVs that bring challenges to effective system evaluations and mandate such a simulation platform different from existing simulations that were built for manned air traffic system and large unmanned fixed aircraft. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative focuses on enabling safe and efficient sUAV operations in the future. In order to help define requirements and policies for a safe and efficient UTM system to accommodate a large amount of sUAV operations, it is necessary to develop a fast-time simulation platform that can effectively evaluate requirements, policies, and concepts in a close-to-reality environment. This work analyzed the impacts of some key factors including aforementioned sUAV's characteristics and demonstrated the importance of these factors in a successful UTM fast-time simulation platform.

Initial Study of An Effective Fast-Time Simulation Platform for Unmanned Aircraft System Traffic Management

NASA Technical Reports Server (NTRS)

Xue, Min; Rios, Joseph

2017-01-01

Small Unmanned Aerial Vehicles (sUAVs), typically 55 lbs and below, are envisioned to play a major role in surveilling critical assets, collecting important information, and delivering goods. Large scale small UAV operations are expected to happen in low altitude airspace in the near future. Many static and dynamic constraints exist in low altitude airspace because of manned aircraft or helicopter activities, various wind conditions, restricted airspace, terrain and man-made buildings, and conflict-avoidance among sUAVs. High sensitivity and high maneuverability are unique characteristics of sUAVs that bring challenges to effective system evaluations and mandate such a simulation platform different from existing simulations that were built for manned air traffic system and large unmanned fixed aircraft. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative focuses on enabling safe and efficient sUAV operations in the future. In order to help define requirements and policies for a safe and efficient UTM system to accommodate a large amount of sUAV operations, it is necessary to develop a fast-time simulation platform that can effectively evaluate requirements, policies, and concepts in a close-to-reality environment. This work analyzed the impacts of some key factors including aforementioned sUAV's characteristics and demonstrated the importance of these factors in a successful UTM fast-time simulation platform.

Remote Sensing of Almond and Walnut Tree Canopy Temperatures Using an Inexpensive Infrared Sensor on a Small Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Crawford, Kellen Ethan

Improving water use efficiency in agriculture will become increasingly important in the face of decreasing water resources and a growing population. Increasing water use efficiency, or water productivity, has been shown to greatly reduce irrigation water usage in many orchard crops with little to no impact on yield. In some specialty crops, improving water productivity can even lead to a higher value crop. Current irrigation practices depend largely on uniform applications of water over large fields with varying degrees of heterogeneity. As a result, much of the field receives more water than it needs. A system to monitor the needs of each plant or smaller groups of plants within the field would be helpful in distributing irrigation water according to each plant or group of plants' needs. Such a system would help conserve water resources. Stomatal conductance is a good indicator of plant water-based stress, as it is the main response a plant has to limit transpiration-related water losses. The difference between leaf temperature and air temperature, when adjusted for environmental conditions, can give a good indication of stomatal conductance. Recent efforts at UC Davis have employed a handheld sensor suite to measure leaf temperature and other environmental variables like wind speed, air temperature, and humidity in almond and walnut trees. Though effective, this method requires walking or driving through the orchard and measuring several leaves on a given tree, so it is impractical for large-scale monitoring. Satellite and aircraft can measure canopy temperatures remotely, but these applications typically do not have the spatial resolution for precise monitoring or the temporal resolution necessary for irrigation decisions, and they are too expensive and impractical for smaller-scale farms. A smaller unmanned aerial vehicle (UAV) could employ the same methods as satellite and larger aircraft-based systems, but relatively inexpensively and at a scale catered to the needs of a given field for more precise monitoring. The goal of this study was to explore the feasibility of using an inexpensive temperature sensor (Melexis MLX90614; NV Melexis SA, Rozendaalstraat 12, 8900 Ieper, Belgium) on a small UAV (Mikrokopter OktoXL; Hisystems GmbH Flachsmeerstrasse 2, 26802 Moormerland, Germany) to sense the canopy temperatures of almond and walnut trees. To accomplish this goal, we installed an infrared temperature sensor and a digital camera on a small UAV. The camera provided a spatial awareness of the IR temperature measurements which would otherwise require a very expensive thermal imager to obtain. The UAV was flown above almond and walnut trees recording images and temperatures, which were aligned temporally in post-processing. The pixels of each image were classified in to four classes: sunlit leaves, shaded leaves, sunlit soil, and shaded soil. Assuming that the measured temperature could be described as a weighted sum of each class in the field of view of the IR sensor, a linear system of equations was established to estimate the temperature of each class using at least several measurements of the same tree. Results indicated a good correlation between the temperatures estimated from the linear system of equations and the temperatures of those classes sampled on the ground immediately following each flight. With leaf temperatures ranging from about 12 to 40 degrees Celsius between 23 flights over two years, the linear solver was able to estimate the temperature of the sunlit and shaded leaves to within several degrees Celsius of the sampled temperature in most cases, with a coefficient of determination (r2 value) of 0.96 during the first year, and 0.73 during the second year. An additional study was undertaken to detect spatial temperature distribution within the orchard. Ground measurements were taken of every other tree in two walnut rows and one almond row using the handheld sensor, and the UAV was flown over those rows immediately following each ground sampling. An interpolated temperature map of the UAV's temperature measurements indicated a very similar temperature distribution as that measured with the handheld sensor, but the UAV was much faster and, in parts of the rows, it provided a higher spatial resolution than the handheld sensor.

Unmanned Aerial Vehicle (UAV) Dynamic-Tracking Directional Wireless Antennas for Low Powered Applications that Require Reliable Extended Range Operations in Time Critical Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scott G. Bauer; Matthew O. Anderson; James R. Hanneman

2005-10-01

The proven value of DOD Unmanned Aerial Vehicles (UAVs) will ultimately transition to National and Homeland Security missions that require real-time aerial surveillance, situation awareness, force protection, and sensor placement. Public services first responders who routinely risk personal safety to assess and report a situation for emergency actions will likely be the first to benefit from these new unmanned technologies. ‘Packable’ or ‘Portable’ small class UAVs will be particularly useful to the first responder. They require the least amount of training, no fixed infrastructure, and are capable of being launched and recovered from the point of emergency. All UAVs requiremore » wireless communication technologies for real- time applications. Typically on a small UAV, a low bandwidth telemetry link is required for command and control (C2), and systems health monitoring. If the UAV is equipped with a real-time Electro-Optical or Infrared (EO/Ir) video camera payload, a dedicated high bandwidth analog/digital link is usually required for reliable high-resolution imagery. In most cases, both the wireless telemetry and real-time video links will be integrated into the UAV with unity gain omni-directional antennas. With limited on-board power and payload capacity, a small UAV will be limited with the amount of radio-frequency (RF) energy it transmits to the users. Therefore, ‘packable’ and ‘portable’ UAVs will have limited useful operational ranges for first responders. This paper will discuss the limitations of small UAV wireless communications. The discussion will present an approach of utilizing a dynamic ground based real-time tracking high gain directional antenna to provide extend range stand-off operation, potential RF channel reuse, and assured telemetry and data communications from low-powered UAV deployed wireless assets.« less

Using Calibrated RGB Imagery from Low-Cost Uavs for Grassland Monitoring: Case Study at the Rengen Grassland Experiment (rge), Germany

NASA Astrophysics Data System (ADS)

Lussem, U.; Hollberg, J.; Menne, J.; Schellberg, J.; Bareth, G.

2017-08-01

Monitoring the spectral response of intensively managed grassland throughout the growing season allows optimizing fertilizer inputs by monitoring plant growth. For example, site-specific fertilizer application as part of precision agriculture (PA) management requires information within short time. But, this requires field-based measurements with hyper- or multispectral sensors, which may not be feasible on a day to day farming practice. Exploiting the information of RGB images from consumer grade cameras mounted on unmanned aerial vehicles (UAV) can offer cost-efficient as well as near-real time analysis of grasslands with high temporal and spatial resolution. The potential of RGB imagery-based vegetation indices (VI) from consumer grade cameras mounted on UAVs has been explored recently in several. However, for multitemporal analyses it is desirable to calibrate the digital numbers (DN) of RGB-images to physical units. In this study, we explored the comparability of the RGBVI from a consumer grade camera mounted on a low-cost UAV to well established vegetation indices from hyperspectral field measurements for applications in grassland. The study was conducted in 2014 on the Rengen Grassland Experiment (RGE) in Germany. Image DN values were calibrated into reflectance by using the Empirical Line Method (Smith & Milton 1999). Depending on sampling date and VI the correlation between the UAV-based RGBVI and VIs such as the NDVI resulted in varying R2 values from no correlation to up to 0.9. These results indicate, that calibrated RGB-based VIs have the potential to support or substitute hyperspectral field measurements to facilitate management decisions on grasslands.

Multi-Sensor Fusion and Enhancement for Object Detection

NASA Technical Reports Server (NTRS)

Rahman, Zia-Ur

2005-01-01

This was a quick &week effort to investigate the ability to detect changes along the flight path of an unmanned airborne vehicle (UAV) over time. Video was acquired by the UAV during several passes over the same terrain. Concurrently, GPS data and UAV attitude data were also acquired. The purpose of the research was to use information from all of these sources to detect if any change had occurred in the terrain encompassed by the flight path.

A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

PubMed Central

Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong

2015-01-01

It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs’ route planning for small and medium-scale networks. PMID:26076404

The exploitation of data from remote and human sensors for environment monitoring in the SMAT project.

PubMed

Meo, Rosa; Roglia, Elena; Bottino, Andrea

2012-12-17

In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors--the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones.

Towards establishing compact imaging spectrometer standards

USGS Publications Warehouse

Slonecker, E. Terrence; Allen, David W.; Resmini, Ronald G.

2016-01-01

Remote sensing science is currently undergoing a tremendous expansion in the area of hyperspectral imaging (HSI) technology. Spurred largely by the explosive growth of Unmanned Aerial Vehicles (UAV), sometimes called Unmanned Aircraft Systems (UAS), or drones, HSI capabilities that once required access to one of only a handful of very specialized and expensive sensor systems are now miniaturized and widely available commercially. Small compact imaging spectrometers (CIS) now on the market offer a number of hyperspectral imaging capabilities in terms of spectral range and sampling. The potential uses of HSI/CIS on UAVs/UASs seem limitless. However, the rapid expansion of unmanned aircraft and small hyperspectral sensor capabilities has created a number of questions related to technological, legal, and operational capabilities. Lightweight sensor systems suitable for UAV platforms are being advertised in the trade literature at an ever-expanding rate with no standardization of system performance specifications or terms of reference. To address this issue, both the U.S. Geological Survey and the National Institute of Standards and Technology are eveloping draft standards to meet these issues. This paper presents the outline of a combined USGS/NIST cooperative strategy to develop and test a characterization methodology to meet the needs of a new and expanding UAV/CIS/HSI user community.

Mathematical model of unmanned aerial vehicle used for endurance autonomous monitoring

NASA Astrophysics Data System (ADS)

Chelaru, Teodor-Viorel; Chelaru, Adrian

2014-12-01

The paper purpose is to present some aspects regarding the control system of unmanned aerial vehicle - UAV, used to local observations, surveillance and monitoring interest area. The calculus methodology allows a numerical simulation of UAV evolution in bad atmospheric conditions by using nonlinear model, as well as a linear one for obtaining guidance command. The UAV model which will be presented has six DOF (degrees of freedom), and autonomous control system. This theoretical development allows us to build stability matrix, command matrix and control matrix and finally to analyse the stability of autonomous UAV flight. A robust guidance system, based on uncoupled state will be evaluated for different fly conditions and the results will be presented. The flight parameters and guidance will be analysed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chelaru, Teodor-Viorel, E-mail: teodor.chelaru@upb.ro; Chelaru, Adrian, E-mail: achelaru@incas.ro

The paper purpose is to present some aspects regarding the control system of unmanned aerial vehicle - UAV, used to local observations, surveillance and monitoring interest area. The calculus methodology allows a numerical simulation of UAV evolution in bad atmospheric conditions by using nonlinear model, as well as a linear one for obtaining guidance command. The UAV model which will be presented has six DOF (degrees of freedom), and autonomous control system. This theoretical development allows us to build stability matrix, command matrix and control matrix and finally to analyse the stability of autonomous UAV flight. A robust guidance system,more » based on uncoupled state will be evaluated for different fly conditions and the results will be presented. The flight parameters and guidance will be analysed.« less

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Volcano Observations Using an Unmanned Autonomous Helicopter : seismic and GPS observations near the active summit area of Sakurajima and Kirishima volcano, Japan

NASA Astrophysics Data System (ADS)

Ohminato, T.; Kaneko, T.; Koyama, T.; Watanabe, A.; Takeo, M.; Iguchi, M.; Honda, Y.

2012-04-01

Observations in the vicinity of summit area of active volcanoes are very important from various viewpoints such as understanding physical processes in the volcanic conduit. It is, however, highly difficult to install observation sensors near active vents because of the risk of sudden eruptions. We have been developing a safe volcano observation system based on an unmanned aerial vehicle (UAV). As an UAV, we adopted an unmanned autonomous helicopter manufactured by Yamaha-Motor Co., Ltd. We have also developed earthquake observation modules and GPS receiver modules that are exclusively designed for UAV installation at summit areas of active volcanoes. These modules are light weight, compact size, and solar powered. For data transmission, a commercial cellular-phone network is used. Our first application of the sensor installation by the UAV is Sakurajima, one of the most active volcanos in Japan. In November 2009, 2010, and 2011, we installed up to four seismic sensors within 2km from the active summit crater. In the 2010 and 2011 operations, we succeeded in pulling up and collecting the sensor modules by using the UAV. In the 2011 experiment, we installed two GPS receivers near the summit area of Sakurajima volcano. We also applied the UAV installation to another active volcano, Shinmoedake in Kirishima volcano group. Since the sub-plinian eruption in February 2011, entering the area 3km from the summit of Shinmoe-dake has been prohibited. In May and November 2011, we installed seismic sensors and GPS receivers in the off-limit zone. Although the ground coupling of the seismic modules is not perfect due to the way they are installed, the signal-to-noise ratio of the seismic signals recorded by these modules is fairly good. Despite the low antenna height of 50 cm from the ground surface, the location errors in horizontal and vertical GPS components are 1cm and 3cm, respectively. For seismic signals associated with eruptions at Sakurajima from November 2010 to November 2011, we measure temporal variation of the amplitude ratio among the summit stations. In order to correct the amplitude variation due to the source amplitude variation, the amplitude of the recorded signals are normalized by using the amplitude of a permanent station, located on the western flank of Sakurajima 5km from the summit. The daily average of the normalized amplitude ratios among the summit stations shows clear temporal variation. The amplitude ratio variation can be classified to three stages. In the first stage, the amplitude ratios among the summit stations are nearly constant. The 2nd stage is characterized by a gradual increase in the amplitude ratio. The third stage is slightly difficult to define but we can say that the amplitude ratios are almost constant with fluctuations larger than that in the first stage. These changes strongly suggest a change in the source depth, probably migration of the source to the shallower portion in the volcanic conduit. Small change in the source position would have been observed as a big change in the observed amplitude ratio due to the closeness of the sensors to the source.

Multispectral data processing from unmanned aerial vehicles: application in precision agriculture using different sensors and platforms

NASA Astrophysics Data System (ADS)

Piermattei, Livia; Bozzi, Carlo Alberto; Mancini, Adriano; Tassetti, Anna Nora; Karel, Wilfried; Pfeifer, Norbert

2017-04-01

Unmanned aerial vehicles (UAVs) in combination with consumer grade cameras have become standard tools for photogrammetric applications and surveying. The recent generation of multispectral, cost-efficient and lightweight cameras has fostered a breakthrough in the practical application of UAVs for precision agriculture. For this application, multispectral cameras typically use Green, Red, Red-Edge (RE) and Near Infrared (NIR) wavebands to capture both visible and invisible images of crops and vegetation. These bands are very effective for deriving characteristics like soil productivity, plant health and overall growth. However, the quality of results is affected by the sensor architecture, the spatial and spectral resolutions, the pattern of image collection, and the processing of the multispectral images. In particular, collecting data with multiple sensors requires an accurate spatial co-registration of the various UAV image datasets. Multispectral processed data in precision agriculture are mainly presented as orthorectified mosaics used to export information maps and vegetation indices. This work aims to investigate the acquisition parameters and processing approaches of this new type of image data in order to generate orthoimages using different sensors and UAV platforms. Within our experimental area we placed a grid of artificial targets, whose position was determined with differential global positioning system (dGPS) measurements. Targets were used as ground control points to georeference the images and as checkpoints to verify the accuracy of the georeferenced mosaics. The primary aim is to present a method for the spatial co-registration of visible, Red-Edge, and NIR image sets. To demonstrate the applicability and accuracy of our methodology, multi-sensor datasets were collected over the same area and approximately at the same time using the fixed-wing UAV senseFly "eBee". The images were acquired with the camera Canon S110 RGB, the multispectral cameras Canon S110 NIR and S110 RE and with the multi-camera system Parrot Sequoia, which is composed of single-band cameras (Green, Red, Red Edge, NIR and RGB). Imagery from each sensor was georeferenced and mosaicked with the commercial software Agisoft PhotoScan Pro and different approaches for image orientation were compared. To assess the overall spatial accuracy of each dataset the root mean square error was computed between check point coordinates measured with dGPS and coordinates retrieved from georeferenced image mosaics. Additionally, image datasets from different UAV platforms (i.e. DJI Phantom 4Pro, DJI Phantom 3 professional, and DJI Inspire 1 Pro) were acquired over the same area and the spatial accuracy of the orthoimages was evaluated.

The remote characterization of vegetation using Unmanned Aerial Vehicle photography

USDA-ARS?s Scientific Manuscript database

Unmanned Aerial Vehicles (UAVs) can fly in place of piloted aircraft to gather remote sensing information on vegetation characteristics. The type of sensors flown depends on the instrument payload capacity available, so that, depending on the specific UAV, it is possible to obtain video, aerial phot...

SUSI 62 A Robust and Safe Parachute Uav with Long Flight Time and Good Payload

NASA Astrophysics Data System (ADS)

Thamm, H. P.

2011-09-01

In many research areas in the geo-sciences (erosion, land use, land cover change, etc.) or applications (e.g. forest management, mining, land management etc.) there is a demand for remote sensing images of a very high spatial and temporal resolution. Due to the high costs of classic aerial photo campaigns, the use of a UAV is a promising option for obtaining the desired remote sensed information at the time it is needed. However, the UAV must be easy to operate, safe, robust and should have a high payload and long flight time. For that purpose, the parachute UAV SUSI 62 was developed. It consists of a steel frame with a powerful 62 cm3 2- stroke engine and a parachute wing. The frame can be easily disassembled for transportation or to replace parts. On the frame there is a gimbal mounted sensor carrier where different sensors, standard SLR cameras and/or multi-spectral and thermal sensors can be mounted. Due to the design of the parachute, the SUSI 62 is very easy to control. Two different parachute sizes are available for different wind speed conditions. The SUSI 62 has a payload of up to 8 kg providing options to use different sensors at the same time or to extend flight duration. The SUSI 62 needs a runway of between 10 m and 50 m, depending on the wind conditions. The maximum flight speed is approximately 50 km/h. It can be operated in a wind speed of up to 6 m/s. The design of the system utilising a parachute UAV makes it comparatively safe as a failure of the electronics or the remote control only results in the UAV coming to the ground at a slow speed. The video signal from the camera, the GPS coordinates and other flight parameters are transmitted to the ground station in real time. An autopilot is available, which guarantees that the area of investigation is covered at the desired resolution and overlap. The robustly designed SUSI 62 has been used successfully in Europe, Africa and Australia for scientific projects and also for agricultural, forestry and industrial applications.

Acquisition and Processing Protocols for Uav Images: 3d Modeling of Historical Buildings Using Photogrammetry

NASA Astrophysics Data System (ADS)

Murtiyoso, A.; Koehl, M.; Grussenmeyer, P.; Freville, T.

2017-08-01

Photogrammetry has seen an increase in the use of UAVs (Unmanned Aerial Vehicles) for both large and smaller scale cartography. The use of UAVs is also advantageous because it may be used for tasks requiring quick response, including in the case of the inspection and monitoring of buildings. The objective of the project is to study the acquisition and processing protocols which exist in the literature and to adapt them for UAV projects. This implies a study on the calibration of the sensors, flight planning, comparison of software solutions, data management, and analysis on the different products of a UAV project. Two historical buildings of the city of Strasbourg were used as case studies: a part of the Rohan Palace façade and the St-Pierre-le-Jeune Catholic church. In addition, a preliminary test was performed on the Josephine Pavilion. Two UAVs were used in this research; namely the Sensefly Albris and the DJI Phantom 3 Professional. The experiments have shown that the calibration parameters tend to be unstable for small sensors. Furthermore, the dense matching of images remains a particular problem to address in a close range photogrammetry project, more so in the presence of noise on the images. Data management in cases where the number of images is high is also very important. The UAV is nevertheless a suitable solution for the surveying and recording of historical buildings because it is able to take images from points of view which are normally inaccessible to classical terrestrial techniques.

Mini-UAV based sensory system for measuring environmental variables in greenhouses.

PubMed

Roldán, Juan Jesús; Joossen, Guillaume; Sanz, David; del Cerro, Jaime; Barrientos, Antonio

2015-02-02

This paper describes the design, construction and validation of a mobile sensory platform for greenhouse monitoring. The complete system consists of a sensory system on board a small quadrotor (i.e., a four rotor mini-UAV). The goals of this system include taking measures of temperature, humidity, luminosity and CO2 concentration and plotting maps of these variables. These features could potentially allow for climate control, crop monitoring or failure detection (e.g., a break in a plastic cover). The sensors have been selected by considering the climate and plant growth models and the requirements for their integration onboard the quadrotor. The sensors layout and placement have been determined through a study of quadrotor aerodynamics and the influence of the airflows from its rotors. All components of the system have been developed, integrated and tested through a set of field experiments in a real greenhouse. The primary contributions of this paper are the validation of the quadrotor as a platform for measuring environmental variables and the determination of the optimal location of sensors on a quadrotor.

Mini-UAV Based Sensory System for Measuring Environmental Variables in Greenhouses

PubMed Central

Roldán, Juan Jesús; Joossen, Guillaume; Sanz, David; del Cerro, Jaime; Barrientos, Antonio

2015-01-01

This paper describes the design, construction and validation of a mobile sensory platform for greenhouse monitoring. The complete system consists of a sensory system on board a small quadrotor (i.e., a four rotor mini-UAV). The goals of this system include taking measures of temperature, humidity, luminosity and CO2 concentration and plotting maps of these variables. These features could potentially allow for climate control, crop monitoring or failure detection (e.g., a break in a plastic cover). The sensors have been selected by considering the climate and plant growth models and the requirements for their integration onboard the quadrotor. The sensors layout and placement have been determined through a study of quadrotor aerodynamics and the influence of the airflows from its rotors. All components of the system have been developed, integrated and tested through a set of field experiments in a real greenhouse. The primary contributions of this paper are the validation of the quadrotor as a platform for measuring environmental variables and the determination of the optimal location of sensors on a quadrotor. PMID:25648713

Infrared search and track performance estimates for detection of commercial unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Nicholas, Robert; Driggers, Ronald; Shelton, David; Furxhi, Orges

2018-04-01

Unmanned aerial vehicles (UAVs) have become more readily available in the past 5 years and are proliferating rapidly. New aviation regulations are accelerating the use of UAVs in many applications. As a result, there are increasing concerns of potential air threats in situational environments including commercial airport security and drug trafficking. In this study, radiometric signatures of commercially available miniature UAVs is determined for long-wave infrared (LWIR) bands in both clear sky and partial cloudy conditions. Results are presented that compare LWIR performance estimates for the detection of commercial UAVs via infrared search and track (IRST) systems with two candidate sensors.

Positional Quality Assessment of Orthophotos Obtained from Sensors Onboard Multi-Rotor UAV Platforms

PubMed Central

Mesas-Carrascosa, Francisco Javier; Rumbao, Inmaculada Clavero; Berrocal, Juan Alberto Barrera; Porras, Alfonso García-Ferrer

2014-01-01

In this study we explored the positional quality of orthophotos obtained by an unmanned aerial vehicle (UAV). A multi-rotor UAV was used to obtain images using a vertically mounted digital camera. The flight was processed taking into account the photogrammetry workflow: perform the aerial triangulation, generate a digital surface model, orthorectify individual images and finally obtain a mosaic image or final orthophoto. The UAV orthophotos were assessed with various spatial quality tests used by national mapping agencies (NMAs). Results showed that the orthophotos satisfactorily passed the spatial quality tests and are therefore a useful tool for NMAs in their production flowchart. PMID:25587877

Positional quality assessment of orthophotos obtained from sensors onboard multi-rotor UAV platforms.

PubMed

Mesas-Carrascosa, Francisco Javier; Rumbao, Inmaculada Clavero; Berrocal, Juan Alberto Barrera; Porras, Alfonso García-Ferrer

2014-11-26

In this study we explored the positional quality of orthophotos obtained by an unmanned aerial vehicle (UAV). A multi-rotor UAV was used to obtain images using a vertically mounted digital camera. The flight was processed taking into account the photogrammetry workflow: perform the aerial triangulation, generate a digital surface model, orthorectify individual images and finally obtain a mosaic image or final orthophoto. The UAV orthophotos were assessed with various spatial quality tests used by national mapping agencies (NMAs). Results showed that the orthophotos satisfactorily passed the spatial quality tests and are therefore a useful tool for NMAs in their production flowchart.

The use of UAVs for monitoring land degradation

NASA Astrophysics Data System (ADS)

Themistocleous, Kyriacos

2017-10-01

Land degradation is one of the causes of desertification of drylands in the Mediterranean. UAVs can be used to monitor and document the various variables that cause desertification in drylands, including overgrazing, aridity, vegetation loss, etc. This paper examines the use of UAVs and accompanying sensors to monitor overgrazing, vegetation stress and aridity in the study area. UAV images can be used to generate digital elevation models (DEMs) to examine the changes in microtopography as well as ortho-photos were used to detect changes in vegetation patterns. The combined data of the digital elevation models and the orthophotos can be used to identify the mechanisms for desertification in the study area.

Development of a Data Acquisition System for Unmanned Aerial Vehicle (UAV) System Identification

NASA Astrophysics Data System (ADS)

Lear, Donald Joseph

Aircraft system identification techniques are developed for fixed wing Unmanned Aerial Vehicles (UAV). The use of a designed flight experiment with measured system inputs/outputs can be used to derive aircraft stability derivatives. This project set out to develop a methodology to support an experiment to model pitch damping in the longitudinal short-period mode of a UAV. A Central Composite Response Surface Design was formed using angle of attack and power levels as factors to test for the pitching moment coefficient response induced by a multistep pitching maneuver. Selecting a high-quality data acquisition platform was critical to the success of the project. This system was designed to support fixed wing research through the addition of a custom air data vane capable of measuring angle of attack and sideslip, as well as an airspeed sensor. A Pixhawk autopilot system serves as the core and modification of the device firmware allowed for the integration of custom sensors and custom RC channels dedicated to performing system identification maneuvers. Tests were performed on all existing Pixhawk sensors to validate stated uncertainty values. The air data system was calibrated in a low speed wind tunnel and dynamic performance was verified. The assembled system was then installed in a commercially available UAV known as an Air Titan FPV in order to test the Pixhawk's automated flight maneuvers and determine the final performance of each sensor. Flight testing showed all the critical sensors produced acceptable data for further research. The Air Titan FPV airframe was found to be very flexible and did not lend itself well to accurate measurement of inertial properties. This realization prohibited the construction of the required math models for longitudinal dynamics. It is recommended that future projects using the developed methods choose an aircraft with a more rigid airframe.

Sparse array of RF sensors for sensing through the wall

NASA Astrophysics Data System (ADS)

Innocenti, Roberto

2007-04-01

In support of the U.S. Army's need for intelligence on the configuration, content, and human presence inside enclosed areas (buildings), the Army Research Laboratory is currently engaged in an effort to evaluate RF sensors for the "Sensing Through The Wall" initiative (STTW).Detection and location of the presence of enemy combatants in urban settings poses significant technical and operational challenges. This paper shows the potential of hand held RF sensors, with the possible assistance of additional sources like Unattended Aerial Vehicles (UAV), Unattended Ground Sensors (UGS), etc, to fulfill this role. In this study we examine both monostatic and multistatic combination of sensors, especially in configurations that allow the capture of images from different angles, and we demonstrate their capability to provide comprehensive information on a variety of buildings. Finally, we explore the limitations of this type of sensor arrangement vis-a-vis the required precision in the knowledge of the position and timing of the RF sensors. Simulation results are provided to show the potential of this type of sensor arrangement in such a difficult environment.

Low Cost and Flexible UAV Deployment of Sensors

PubMed Central

Sørensen, Lars Yndal; Jacobsen, Lars Toft; Hansen, John Paulin

2017-01-01

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system is illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperature in a lake. PMID:28098819

Low Cost and Flexible UAV Deployment of Sensors.

PubMed

Sørensen, Lars Yndal; Jacobsen, Lars Toft; Hansen, John Paulin

2017-01-14

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system is illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperature in a lake.

a Comparison among Different Optimization Levels in 3d Multi-Sensor Models. a Test Case in Emergency Context: 2016 Italian Earthquake

NASA Astrophysics Data System (ADS)

Chiabrando, F.; Sammartano, G.; Spanò, A.

2017-02-01

In sudden emergency contexts that affect urban centres and built heritage, the latest Geomatics technique solutions must enable the demands of damage documentation, risk assessment, management and data sharing as efficiently as possible, in relation to the danger condition, to the accessibility constraints of areas and to the tight deadlines needs. In recent times, Unmanned Vehicle System (UAV) equipped with cameras are more and more involved in aerial survey and reconnaissance missions, and they are behaving in a very cost-effective way in the direction of 3D documentation and preliminary damage assessment. More and more UAV equipment with low-cost sensors must become, in the future, suitable in every situation of documentation, but above all in damages and uncertainty frameworks. Rapidity in acquisition times and low-cost sensors are challenging marks, and they could be taken into consideration maybe with time spending processing. The paper will analyze and try to classify the information content in 3D aerial and terrestrial models and the importance of metric and non-metric withdrawable information that should be suitable for further uses, as the structural analysis one. The test area is an experience of Team Direct from Politecnico di Torino in centre Italy, where a strong earthquake occurred in August 2016. This study is carried out on a stand-alone damaged building in Pescara del Tronto (AP), with a multi-sensor 3D survey. The aim is to evaluate the contribution of terrestrial and aerial quick documentation by a SLAM based LiDAR and a camera equipped multirotor UAV, for a first reconnaissance inspection and modelling in terms of level of details, metric and non-metric information.

Unmanned aerial vehicles (drones) to prevent drowning.

PubMed

Seguin, Celia; Blaquière, Gilles; Loundou, Anderson; Michelet, Pierre; Markarian, Thibaut

2018-06-01

Drowning literature have highlighted the submersion time as the most powerful predictor in assessing the prognosis. Reducing the time taken to provide a flotation device and prevent submersion appears of paramount importance. Unmanned aerial vehicles (UAVs) can provide the location of the swimmer and a flotation device. The objective of this simulation study was to evaluate the efficiency of a UAV in providing a flotation device in different sea conditions, and to compare the times taken by rescue operations with and without a UAV (standard vs UAV intervention). Several comparisons were made using professional lifeguards acting as simulated victims. A specifically-shaped UAV was used to allow us to drop an inflatable life buoy into the water. During the summer of 2017, 28 tests were performed. UAV use was associated with a reduction of time it took to provide a flotation device to the simulated victim compared with standard rescue operations (p < 0.001 for all measurements) and the time was reduced even further in moderate (81 ± 39 vs 179 ± 78 s; p < 0.001) and rough sea conditions (99 ± 34 vs 198 ± 130 s; p < 0.001). The times taken for UAV to locate the simulated victim, identify them and drop the life buoy were not altered by the weather conditions. UAV can deliver a flotation device to a swimmer safely and quickly. The addition of a UAV in rescue operations could improve the quality and speed of first aid while keeping lifeguards away from dangerous sea conditions. Copyright © 2018 Elsevier B.V. All rights reserved.

On the prospects of cross-calibrating the Cherenkov Telescope Array with an airborne calibration platform

NASA Astrophysics Data System (ADS)

Brown, Anthony M.

2018-01-01

Recent advances in unmanned aerial vehicle (UAV) technology have made UAVs an attractive possibility as an airborne calibration platform for astronomical facilities. This is especially true for arrays of telescopes spread over a large area such as the Cherenkov Telescope Array (CTA). In this paper, the feasibility of using UAVs to calibrate CTA is investigated. Assuming a UAV at 1km altitude above CTA, operating on astronomically clear nights with stratified, low atmospheric dust content, appropriate thermal protection for the calibration light source and an onboard photodiode to monitor its absolute light intensity, inter-calibration of CTA's telescopes of the same size class is found to be achievable with a 6 - 8 % uncertainty. For cross-calibration of different telescope size classes, a systematic uncertainty of 8 - 10 % is found to be achievable. Importantly, equipping the UAV with a multi-wavelength calibration light source affords us the ability to monitor the wavelength-dependent degradation of CTA telescopes' optical system, allowing us to not only maintain this 6 - 10 % uncertainty after the first few years of telescope deployment, but also to accurately account for the effect of multi-wavelength degradation on the cross-calibration of CTA by other techniques, namely with images of air showers and local muons. A UAV-based system thus provides CTA with several independent and complementary methods of cross-calibrating the optical throughput of individual telescopes. Furthermore, housing environmental sensors on the UAV system allows us to not only minimise the systematic uncertainty associated with the atmospheric transmission of the calibration signal, it also allows us to map the dust content above CTA as well as monitor the temperature, humidity and pressure profiles of the first kilometre of atmosphere above CTA with each UAV flight.

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

PubMed Central

Meo, Rosa; Roglia, Elena; Bottino, Andrea

2012-01-01

In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors—the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones. PMID:23247415

MISTRALE: Soil moisture mapping service based on a UAV-embedded GNSS-Reflectometry sensor

NASA Astrophysics Data System (ADS)

Van de Vyvere, Laura; Desenfans, Olivier

2016-04-01

Around 70 percent of worldwide freshwater is used by agriculture. To be able to feed an additional 2 billion people by 2030, water demand is expected to increase tremendously in the next decades. Farmers are challenged to produce "more crop per drop". In order to optimize water resource management, it is crucial to improve soil moisture situation awareness, which implies both a better temporal and spatial resolution. To this end, the objective of the MISTRALE project (Monitoring soIl moiSture and waTeR-flooded Areas for agricuLture and Environment) is to provide UAV-based soil moisture maps that could complement satellite-based and field measurements. In addition to helping farmers make more efficient decisions about where and when to irrigate, MISTRALE moisture maps are an invaluable tool for risk management and damage evaluation, as they provide highly relevant information for wetland and flood-prone area monitoring. In order to measure soil water content, a prototype of a new sensor, called GNSS-Reflectometry (GNSS-R), is being developed in MISTRALE. This approach consists in comparing the direct signal, i.e. the signal travelling directly from satellite to receiver (in this case, embedded in the UAV), with its ground-reflected equivalent. Since soil dielectric properties vary with moisture content, the reflected signal's peak power is affected by soil moisture, unlike the direct one. In order to mitigate the effect of soil surface roughness on measurements, both left-hand and right-hand circular polarization reflected signals have to be recorded and processed. When it comes to soil moisture, using GNSS signals instead of traditional visible/NIR imagery has many advantages: it is operational under cloud cover, during the night, and also under vegetation (bushes, grass, trees). In addition, compared to microwaves, GNSS signal (which lies in L-band, between 1.4 and 1.8 GHz) is less influenced by variation on thermal background. GNSS frequencies are then ideal candidates for soil moisture observation. In the context of the MISTRALE project, both GPS and GALILEO signals will be used. Thanks to a higher number of available satellites and to the GALILEO signals characteristics, the sensor's measurements accuracy will be improved. The GNSS-R sensor will be embedded on Boreal, a fixed-wing UAV weighing less than 20 kg and allowing about 5 kg payload. Boreal is able to fly continuously for 10 hours and has a range of 1000 km. Due to the low elevation (100-150m) of UAV flights, high spatial resolution can be achieved. Test flights have already been performed in Pech Rouge and Camargue areas, France. During these campaigns, soil moisture maps were computed using GNSS-R data. These were successfully correlated with in-situ measurements, considered as ground truth, demonstrating the feasibility of the MISTRALE concept. The MISTRALE project is co-funded by GSA (European GNSS Agency) under H2020 program framework for research and innovation.

UAV based hydromorphological mapping of a river reach to improve hydrodynamic numerical models

NASA Astrophysics Data System (ADS)

Lükő, Gabriella; Baranya, Sándor; Rüther, Nils

2017-04-01

Unmanned Aerial Vehicles (UAVs) are increasingly used in the field of engineering surveys. In river engineering, or in general, water resources engineering, UAV based measurements have a huge potential. For instance, indirect measurements of the flow discharge using e.g. large-scale particle image velocimetry (LSPIV), particle tracking velocimetry (PTV), space-time image velocimetry (STIV) or radars became a real alternative for direct flow measurements. Besides flow detection, topographic surveys are also essential for river flow studies as the channel and floodplain geometry is the primary steering feature of the flow. UAVs can play an important role in this field, too. The widely used laser based topographic survey method (LIDAR) can be deployed on UAVs, moreover, the application of the Structure from Motion (SfM) method, which is based on images taken by UAVs, might be an even more cost-efficient alternative to reveal the geometry of distinct objects in the river or on the floodplain. The goal of this study is to demonstrate the utilization of photogrammetry and videogrammetry from airborne footage to provide geometry and flow data for a hydrodynamic numerical simulation of a 2 km long river reach in Albania. First, the geometry of the river is revealed from photogrammetry using the SfM method. Second, a more detailed view of the channel bed at low water level is taken. Using the fine resolution images, a Matlab based code, BASEGrain, developed by the ETH in Zürich, will be applied to determine the grain size characteristics of the river bed. This information will be essential to define the hydraulic roughness in the numerical model. Third, flow mapping is performed using UAV measurements and LSPIV method to quantitatively asses the flow field at the free surface and to estimate the discharge in the river. All data collection and analysis will be carried out using a simple, low-cost UAV, moreover, for all the data processing, open source, freely available software will be used leading to a cost-efficient methodology. The results of the UAV based measurements will be discussed and future research ideas will be outlined.

On-Board Mining in the Sensor Web

NASA Astrophysics Data System (ADS)

Tanner, S.; Conover, H.; Graves, S.; Ramachandran, R.; Rushing, J.

2004-12-01

On-board data mining can contribute to many research and engineering applications, including natural hazard detection and prediction, intelligent sensor control, and the generation of customized data products for direct distribution to users. The ability to mine sensor data in real time can also be a critical component of autonomous operations, supporting deep space missions, unmanned aerial and ground-based vehicles (UAVs, UGVs), and a wide range of sensor meshes, webs and grids. On-board processing is expected to play a significant role in the next generation of NASA, Homeland Security, Department of Defense and civilian programs, providing for greater flexibility and versatility in measurements of physical systems. In addition, the use of UAV and UGV systems is increasing in military, emergency response and industrial applications. As research into the autonomy of these vehicles progresses, especially in fleet or web configurations, the applicability of on-board data mining is expected to increase significantly. Data mining in real time on board sensor platforms presents unique challenges. Most notably, the data to be mined is a continuous stream, rather than a fixed store such as a database. This means that the data mining algorithms must be modified to make only a single pass through the data. In addition, the on-board environment requires real time processing with limited computing resources, thus the algorithms must use fixed and relatively small amounts of processing time and memory. The University of Alabama in Huntsville is developing an innovative processing framework for the on-board data and information environment. The Environment for On-Board Processing (EVE) and the Adaptive On-board Data Processing (AODP) projects serve as proofs-of-concept of advanced information systems for remote sensing platforms. The EVE real-time processing infrastructure will upload, schedule and control the execution of processing plans on board remote sensors. These plans provide capabilities for autonomous data mining, classification and feature extraction using both streaming and buffered data sources. A ground-based testbed provides a heterogeneous, embedded hardware and software environment representing both space-based and ground-based sensor platforms, including wireless sensor mesh architectures. The AODP project explores the EVE concepts in the world of sensor-networks, including ad-hoc networks of small sensor platforms.

Using machine learning and real-time workload assessment in a high-fidelity UAV simulation environment

NASA Astrophysics Data System (ADS)

Monfort, Samuel S.; Sibley, Ciara M.; Coyne, Joseph T.

2016-05-01

Future unmanned vehicle operations will see more responsibilities distributed among fewer pilots. Current systems typically involve a small team of operators maintaining control over a single aerial platform, but this arrangement results in a suboptimal configuration of operator resources to system demands. Rather than devoting the full-time attention of several operators to a single UAV, the goal should be to distribute the attention of several operators across several UAVs as needed. Under a distributed-responsibility system, operator task load would be continuously monitored, with new tasks assigned based on system needs and operator capabilities. The current paper sought to identify a set of metrics that could be used to assess workload unobtrusively and in near real-time to inform a dynamic tasking algorithm. To this end, we put 20 participants through a variable-difficulty multiple UAV management simulation. We identified a subset of candidate metrics from a larger pool of pupillary and behavioral measures. We then used these metrics as features in a machine learning algorithm to predict workload condition every 60 seconds. This procedure produced an overall classification accuracy of 78%. An automated tasker sensitive to fluctuations in operator workload could be used to efficiently delegate tasks for teams of UAV operators.

UAV-borne lidar with MEMS mirror-based scanning capability

NASA Astrophysics Data System (ADS)

Kasturi, Abhishek; Milanovic, Veljko; Atwood, Bryan H.; Yang, James

2016-05-01

Firstly, we demonstrated a wirelessly controlled MEMS scan module with imaging and laser tracking capability which can be mounted and flown on a small UAV quadcopter. The MEMS scan module was reduced down to a small volume of <90mm x 60mm x 40mm, weighing less than 40g and consuming less than 750mW of power using a ~5mW laser. This MEMS scan module was controlled by a smartphone via Bluetooth while flying on a drone, and could project vector content, text, and perform laser based tracking. Also, a "point-and-range" LiDAR module was developed for UAV applications based on low SWaP (Size, Weight and Power) gimbal-less MEMS mirror beam-steering technology and off-the-shelf OEM LRF modules. For demonstration purposes of an integrated laser range finder module, we used a simple off-the-shelf OEM laser range finder (LRF) with a 100m range, +/-1.5mm accuracy, and 4Hz ranging capability. The LRFs receiver optics were modified to accept 20° of angle, matching the transmitter's FoR. A relatively large (5.0mm) diameter MEMS mirror with +/-10° optical scanning angle was utilized in the demonstration to maintain the small beam divergence of the module. The complete LiDAR prototype can fit into a small volume of <70mm x 60mm x 60mm, and weigh <50g when powered by the UAV's battery. The MEMS mirror based LiDAR system allows for ondemand ranging of points or areas within the FoR without altering the UAV's position. Increasing the LRF ranging frequency and stabilizing the pointing of the laser beam by utilizing the onboard inertial sensors and the camera are additional goals of the next design.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

PubMed Central

Shi, Yeyin; Thomasson, J. Alex; Murray, Seth C.; Pugh, N. Ace; Rooney, William L.; Shafian, Sanaz; Rajan, Nithya; Rouze, Gregory; Morgan, Cristine L. S.; Neely, Haly L.; Rana, Aman; Bagavathiannan, Muthu V.; Henrickson, James; Bowden, Ezekiel; Valasek, John; Olsenholler, Jeff; Bishop, Michael P.; Sheridan, Ryan; Putman, Eric B.; Popescu, Sorin; Burks, Travis; Cope, Dale; Ibrahim, Amir; McCutchen, Billy F.; Baltensperger, David D.; Avant, Robert V.; Vidrine, Misty; Yang, Chenghai

2016-01-01

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs. PMID:27472222

A Technology Analysis to Support Acquisition of UAVs for Gulf Coalition Forces Operations

DTIC Science & Technology

2017-06-01

their selection of the most suitable and cost-effective unmanned aerial vehicles to support detection operations. This study uses Map Aware Non ...being detected by Gulf Coalition Forces and improved time to detect them, support the use of UAVs in detection missions. Computer experimentations and...aerial vehicles to support detection operations. We use Map Aware Non - Uniform Automata, an agent-based simulation software platform, for the

Using virtual instruments to develop an actuator-based hardware-in-the-loop simulation test-bed for autopilot of unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Sun, Yun-Ping; Ju, Jiun-Yan; Liang, Yen-Chu

2008-12-01

Since the unmanned aerial vehicles (UAVs) bring forth many innovative applications in scientific, civilian, and military fields, the development of UAVs is rapidly growing every year. The on-board autopilot that reliably performs attitude and guidance control is a vital part for out-of-sight flights. However, the control law in autopilot is designed according to a simplified plant model in which the dynamics of real hardware are usually not taken into consideration. It is a necessity to develop a test-bed including real servos to make real-time control experiments for prototype autopilots, so called hardware-in-the-loop (HIL) simulation. In this paper on the basis of the graphical application software LabVIEW, the real-time HIL simulation system is realized efficiently by the virtual instrumentation approach. The proportional-integral-derivative (PID) controller in autopilot for the pitch angle control loop is experimentally determined by the classical Ziegler-Nichols tuning rule and exhibits good transient and steady-state response in real-time HIL simulation. From the results the differences between numerical simulation and real-time HIL simulation are also clearly presented. The effectiveness of HIL simulation for UAV autopilot design is definitely confirmed

Implementation of high-gain observer on low-cost fused IR-OS sensor embedded in UAV system

NASA Astrophysics Data System (ADS)

Nor, E. Mohd; Noor, S. B. Mohd; Bahiki, M. R.; Azrad, S.

2017-12-01

This paper presents discrete time implementation of a high gain observer (HGO) and extended term to estimate the state velocity and acceleration from the position measured by a low-cost sensor installed on-board the unmanned aerial vehicle (UAV). Owing to the low-cost sensor, the signal produced from fused IR-OS is noisy and therefore, additional filters are used to remove the noise. This study proposes an alternative to this standard and tedious procedure using HGO. The discrete time implementation of HGO and its extended term is presented and ground tests are conducted to verify the algorithm by inducing a dynamic motion on the UAV platform embedded with the fusion IR-OS onboard. A comparison study is conducted using standard numerical differentiation and ground truth measurement by OptiTrack. The results show that EHGO can produce a velocity signal with the same quality as that of differentiated signal from fused IR-OS using Kalman filter. The novelty of HGO lies in its simplicity and its minimal tuning of parameters.

State estimation for autopilot control of small unmanned aerial vehicles in windy conditions

NASA Astrophysics Data System (ADS)

Poorman, David Paul

The use of small unmanned aerial vehicles (UAVs) both in the military and civil realms is growing. This is largely due to the proliferation of inexpensive sensors and the increase in capability of small computers that has stemmed from the personal electronic device market. Methods for performing accurate state estimation for large scale aircraft have been well known and understood for decades, which usually involve a complex array of expensive high accuracy sensors. Performing accurate state estimation for small unmanned aircraft is a newer area of study and often involves adapting known state estimation methods to small UAVs. State estimation for small UAVs can be more difficult than state estimation for larger UAVs due to small UAVs employing limited sensor suites due to cost, and the fact that small UAVs are more susceptible to wind than large aircraft. The purpose of this research is to evaluate the ability of existing methods of state estimation for small UAVs to accurately capture the states of the aircraft that are necessary for autopilot control of the aircraft in a Dryden wind field. The research begins by showing which aircraft states are necessary for autopilot control in Dryden wind. Then two state estimation methods that employ only accelerometer, gyro, and GPS measurements are introduced. The first method uses assumptions on aircraft motion to directly solve for attitude information and smooth GPS data, while the second method integrates sensor data to propagate estimates between GPS measurements and then corrects those estimates with GPS information. The performance of both methods is analyzed with and without Dryden wind, in straight and level flight, in a coordinated turn, and in a wings level ascent. It is shown that in zero wind, the first method produces significant steady state attitude errors in both a coordinated turn and in a wings level ascent. In Dryden wind, it produces large noise on the estimates for its attitude states, and has a non-zero mean error that increases when gyro bias is increased. The second method is shown to not exhibit any steady state error in the tested scenarios that is inherent to its design. The second method can correct for attitude errors that arise from both integration error and gyro bias states, but it suffers from lack of attitude error observability. The attitude errors are shown to be more observable in wind, but increased integration error in wind outweighs the increase in attitude corrections that such increased observability brings, resulting in larger attitude errors in wind. Overall, this work highlights many technical deficiencies of both of these methods of state estimation that could be improved upon in the future to enhance state estimation for small UAVs in windy conditions.

The Practical Application of Uav-Based Photogrammetry Under Economic Aspects

NASA Astrophysics Data System (ADS)

Sauerbier, M.; Siegrist, E.; Eisenbeiss, H.; Demir, N.

2011-09-01

Nowadays, small size UAVs (Unmanned Aerial Vehicles) have reached a level of practical reliability and functionality that enables this technology to enter the geomatics market as an additional platform for spatial data acquisition. Though one could imagine a wide variety of interesting sensors to be mounted on such a device, here we will focus on photogrammetric applications using digital cameras. In praxis, UAV-based photogrammetry will only be accepted if it a) provides the required accuracy and an additional value and b) if it is competitive in terms of economic application compared to other measurement technologies. While a) was already proven by the scientific community and results were published comprehensively during the last decade, b) still has to be verified under real conditions. For this purpose, a test data set representing a realistic scenario provided by ETH Zurich was used to investigate cost effectiveness and to identify weak points in the processing chain that require further development. Our investigations are limited to UAVs carrying digital consumer cameras, for larger UAVs equipped with medium format cameras the situation has to be considered as significantly different. Image data was acquired during flights using a microdrones MD4-1000 quadrocopter equipped with an Olympus PE-1 digital compact camera. From these images, a subset of 5 images was selected for processing in order to register the effort of time required for the whole production chain of photogrammetric products. We see the potential of mini UAV-based photogrammetry mainly in smaller areas, up to a size of ca. 100 hectares. Larger areas can be efficiently covered by small airplanes with few images, reducing processing effort drastically. In case of smaller areas of a few hectares only, it depends more on the products required. UAVs can be an enhancement or alternative to GNSS measurements, terrestrial laser scanning and ground based photogrammetry. We selected the above mentioned test data from a project featuring an area of interest within the practical range for mini UAVs. While flight planning and flight operation are already quite efficient processes, the bottlenecks identified are mainly related to image processing. Although we used specific software for image processing, the identified gaps in the processing chain today are valid for most commercial photogrammetric software systems on the market. An outlook proposing improvements for a practicable workflow applicable in projects in private economy will be given.

Sensor data fusion for automated threat recognition in manned-unmanned infantry platoons

NASA Astrophysics Data System (ADS)

Wildt, J.; Varela, M.; Ulmke, M.; Brüggermann, B.

2017-05-01

To support a dismounted infantry platoon during deployment we team it with several unmanned aerial and ground vehicles (UAV and UGV, respectively). The unmanned systems integrate seamlessly into the infantry platoon, providing automated reconnaissance during movement while keeping formation as well as conducting close range reconnaissance during halt. The sensor data each unmanned system provides is continuously analyzed in real time by specialized algorithms, detecting humans in live videos of UAV mounted infrared cameras as well as gunshot detection and bearing by acoustic sensors. All recognized threats are fused into a consistent situational picture in real time, available to platoon and squad leaders as well as higher level command and control (C2) systems. This gives friendly forces local information superiority and increased situational awareness without the need to constantly monitor the unmanned systems and sensor data.

Methane Leak Detection and Emissions Quantification with UAVs

NASA Astrophysics Data System (ADS)

Barchyn, T.; Fox, T. A.; Hugenholtz, C.

2016-12-01

Robust leak detection and emissions quantification algorithms are required to accurately monitor greenhouse gas emissions. Unmanned aerial vehicles (UAVs, `drones') could both reduce the cost and increase the accuracy of monitoring programs. However, aspects of the platform create unique challenges. UAVs typically collect large volumes of data that are close to source (due to limited range) and often lower quality (due to weight restrictions on sensors). Here we discuss algorithm development for (i) finding sources of unknown position (`leak detection') and (ii) quantifying emissions from a source of known position. We use data from a simulated leak and field study in Alberta, Canada. First, we detail a method for localizing a leak of unknown spatial location using iterative fits against a forward Gaussian plume model. We explore sources of uncertainty, both inherent to the method and operational. Results suggest this method is primarily constrained by accurate wind direction data, distance downwind from source, and the non-Gaussian shape of close range plumes. Second, we examine sources of uncertainty in quantifying emissions with the mass balance method. Results suggest precision is constrained by flux plane interpolation errors and time offsets between spatially adjacent measurements. Drones can provide data closer to the ground than piloted aircraft, but large portions of the plume are still unquantified. Together, we find that despite larger volumes of data, working with close range plumes as measured with UAVs is inherently difficult. We describe future efforts to mitigate these challenges and work towards more robust benchmarking for application in industrial and regulatory settings.

Mapping Surface Temperatures on a Debris-Covered Glacier with an Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Kraaijenbrink, Philip D. A.; Shea, Joseph M.; Litt, Maxime; Steiner, Jakob F.; Treichler, Désirée; Koch, Inka; Immerzeel, Walter W.

2018-05-01

A mantel of debris cover often accumulates across the surface of glaciers in active mountain ranges with exceptionally steep terrain, such as the Andes, Himalaya and New Zealand Alps. Such a supraglacial debris layer has a major influence on a glacier's surface energy budget, enhancing radiation absorption and melt when the layer is thin, but insulating the ice when thicker than a few cm. Information on spatially distributed debris surface temperature has the potential to provide insight into the properties of the debris, its effects on the ice below and its influence on the near-surface boundary layer. Here, we deploy an unmanned aerial vehicle (UAV) equipped with a thermal infrared sensor on three separate missions over one day to map changing surface temperatures across the debris-covered Lirung Glacier in the Central Himalaya. We present a methodology to georeference and process the acquired thermal imagery, and correct for emissivity and sensor bias. Derived UAV surface temperatures are compared with distributed simultaneous in situ temperature measurements as well as with Landsat 8 thermal satellite imagery. Results show that the UAV-derived surface temperatures vary greatly both spatially and temporally, with -1.4±1.8, 11.0 ±5.2 and 15.3±4.7 °C for the three flights (mean±sd), respectively. The range in surface temperatures over the glacier during the morning is very large with almost 50 °C. Ground-based measurements are generally in agreement with the UAV imagery, but considerable deviations are present that are likely due to differences in measurement technique and approach, and validation is difficult as a result. The difference in spatial and temporal variability captured by the UAV as compared with much coarser satellite imagery is striking and it shows that satellite derived temperature maps should be interpreted with care. We conclude that UAVs provide a suitable means to acquire surface temperature maps of debris-covered glacier surfaces at high spatial and temporal resolution, but that there are caveats with regard to absolute temperature measurement.

Implementation of the Rauch-Tung-Striebel Smoother for Sensor Compatibility Correction of a Fixed-Wing Unmanned Air Vehicle

PubMed Central

Chan, Woei-Leong; Hsiao, Fei-Bin

2011-01-01

This paper presents a complete procedure for sensor compatibility correction of a fixed-wing Unmanned Air Vehicle (UAV). The sensors consist of a differential air pressure transducer for airspeed measurement, two airdata vanes installed on an airdata probe for angle of attack (AoA) and angle of sideslip (AoS) measurement, and an Attitude and Heading Reference System (AHRS) that provides attitude angles, angular rates, and acceleration. The procedure is mainly based on a two pass algorithm called the Rauch-Tung-Striebel (RTS) smoother, which consists of a forward pass Extended Kalman Filter (EKF) and a backward recursion smoother. On top of that, this paper proposes the implementation of the Wiener Type Filter prior to the RTS in order to avoid the complicated process noise covariance matrix estimation. Furthermore, an easy to implement airdata measurement noise variance estimation method is introduced. The method estimates the airdata and subsequently the noise variances using the ground speed and ascent rate provided by the Global Positioning System (GPS). It incorporates the idea of data regionality by assuming that some sort of statistical relation exists between nearby data points. Root mean square deviation (RMSD) is being employed to justify the sensor compatibility. The result shows that the presented procedure is easy to implement and it improves the UAV sensor data compatibility significantly. PMID:22163819

Implementation of the Rauch-Tung-Striebel smoother for sensor compatibility correction of a fixed-wing unmanned air vehicle.

PubMed

Chan, Woei-Leong; Hsiao, Fei-Bin

2011-01-01

This paper presents a complete procedure for sensor compatibility correction of a fixed-wing Unmanned Air Vehicle (UAV). The sensors consist of a differential air pressure transducer for airspeed measurement, two airdata vanes installed on an airdata probe for angle of attack (AoA) and angle of sideslip (AoS) measurement, and an Attitude and Heading Reference System (AHRS) that provides attitude angles, angular rates, and acceleration. The procedure is mainly based on a two pass algorithm called the Rauch-Tung-Striebel (RTS) smoother, which consists of a forward pass Extended Kalman Filter (EKF) and a backward recursion smoother. On top of that, this paper proposes the implementation of the Wiener Type Filter prior to the RTS in order to avoid the complicated process noise covariance matrix estimation. Furthermore, an easy to implement airdata measurement noise variance estimation method is introduced. The method estimates the airdata and subsequently the noise variances using the ground speed and ascent rate provided by the Global Positioning System (GPS). It incorporates the idea of data regionality by assuming that some sort of statistical relation exists between nearby data points. Root mean square deviation (RMSD) is being employed to justify the sensor compatibility. The result shows that the presented procedure is easy to implement and it improves the UAV sensor data compatibility significantly.

State-Of in Uav Remote Sensing Survey - First Insights Into Applications of Uav Sensing Systems

NASA Astrophysics Data System (ADS)

Aasen, H.

2017-08-01

UAVs are increasingly adapted as remote sensing platforms. Together with specialized sensors, they become powerful sensing systems for environmental monitoring and surveying. Spectral data has great capabilities to the gather information about biophysical and biochemical properties. Still, capturing meaningful spectral data in a reproducible way is not trivial. Since a couple of years small and lightweight spectral sensors, which can be carried on small flexible platforms, have become available. With their adaption in the community, the responsibility to ensure the quality of the data is increasingly shifted from specialized companies and agencies to individual researchers or research teams. Due to the complexity of the data acquisition of spectral data, this poses a challenge for the community and standardized protocols, metadata and best practice procedures are needed to make data intercomparable. In November 2016, the ESSEM COST action Innovative optical Tools for proximal sensing of ecophysiological processes (OPTIMISE; http://optimise.dcs.aber.ac.uk/) held a workshop on best practices for UAV spectral sampling. The objective of this meeting was to trace the way from particle to pixel and identify influences on the data quality / reliability, to figure out how well we are currently doing with spectral sampling from UAVs and how we can improve. Additionally, a survey was designed to be distributed within the community to get an overview over the current practices and raise awareness for the topic. This talk will introduce the approach of the OPTIMISE community towards best practises in UAV spectral sampling and present first results of the survey (http://optimise.dcs.aber.ac.uk/uav-survey/). This contribution briefly introduces the survey and gives some insights into the first results given by the interviewees.

Formation flight and collision avoidance for multiple UAVs based on modified tentacle algorithm in unstructured environments

PubMed Central

2017-01-01

This paper presents a method for formation flight and collision avoidance of multiple UAVs. Due to the shortcomings such as collision avoidance caused by UAV’s high-speed and unstructured environments, this paper proposes a modified tentacle algorithm to ensure the high performance of collision avoidance. Different from the conventional tentacle algorithm which uses inverse derivation, the modified tentacle algorithm rapidly matches the radius of each tentacle and the steering command, ensuring that the data calculation problem in the conventional tentacle algorithm is solved. Meanwhile, both the speed sets and tentacles in one speed set are reduced and reconstructed so as to be applied to multiple UAVs. Instead of path iterative optimization, the paper selects the best tentacle to obtain the UAV collision avoidance path quickly. The simulation results show that the method presented in the paper effectively enhances the performance of flight formation and collision avoidance for multiple high-speed UAVs in unstructured environments. PMID:28763498

Real-time UAV trajectory generation using feature points matching between video image sequences

NASA Astrophysics Data System (ADS)

Byun, Younggi; Song, Jeongheon; Han, Dongyeob

2017-09-01

Unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance mission. In this paper, we present a systematic approach for the generation of UAV trajectory using a video image matching system based on SURF (Speeded up Robust Feature) and Preemptive RANSAC (Random Sample Consensus). Video image matching to find matching points is one of the most important steps for the accurate generation of UAV trajectory (sequence of poses in 3D space). We used the SURF algorithm to find the matching points between video image sequences, and removed mismatching by using the Preemptive RANSAC which divides all matching points to outliers and inliers. The inliers are only used to determine the epipolar geometry for estimating the relative pose (rotation and translation) between image sequences. Experimental results from simulated video image sequences showed that our approach has a good potential to be applied to the automatic geo-localization of the UAVs system

A Natural Interaction Interface for UAVs Using Intuitive Gesture Recognition

NASA Technical Reports Server (NTRS)

Chandarana, Meghan; Trujillo, Anna; Shimada, Kenji; Allen, Danette

2016-01-01

The popularity of unmanned aerial vehicles (UAVs) is increasing as technological advancements boost their favorability for a broad range of applications. One application is science data collection. In fields like Earth and atmospheric science, researchers are seeking to use UAVs to augment their current portfolio of platforms and increase their accessibility to geographic areas of interest. By increasing the number of data collection platforms UAVs will significantly improve system robustness and allow for more sophisticated studies. Scientists would like be able to deploy an available fleet of UAVs to fly a desired flight path and collect sensor data without needing to understand the complex low-level controls required to describe and coordinate such a mission. A natural interaction interface for a Ground Control System (GCS) using gesture recognition is developed to allow non-expert users (e.g., scientists) to define a complex flight path for a UAV using intuitive hand gesture inputs from the constructed gesture library. The GCS calculates the combined trajectory on-line, verifies the trajectory with the user, and sends it to the UAV controller to be flown.

Autonomous mission management for UAVs using soar intelligent agents

NASA Astrophysics Data System (ADS)

Gunetti, Paolo; Thompson, Haydn; Dodd, Tony

2013-05-01

State-of-the-art unmanned aerial vehicles (UAVs) are typically able to autonomously execute a pre-planned mission. However, UAVs usually fly in a very dynamic environment which requires dynamic changes to the flight plan; this mission management activity is usually tasked to human supervision. Within this article, a software system that autonomously accomplishes the mission management task for a UAV will be proposed. The system is based on a set of theoretical concepts which allow the description of a flight plan and implemented using a combination of Soar intelligent agents and traditional control techniques. The system is capable of automatically generating and then executing an entire flight plan after being assigned a set of objectives. This article thoroughly describes all system components and then presents the results of tests that were executed using a realistic simulation environment.

An arm wearable haptic interface for impact sensing on unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Choi, Yunshil; Hong, Seung-Chan; Lee, Jung-Ryul

2017-04-01

In this paper, an impact monitoring system using fiber Bragg grating (FBG) sensors and vibro-haptic actuators has been introduced. The system is suggested for structural health monitoring (SHM) for unmanned aerial vehicles (UAVs), by making a decision with human-robot interaction. The system is composed with two major subsystems; an on-board system equipped on UAV and an arm-wearable interface for ground pilot. The on-board system acquires impact-induced wavelength changes and performs localization process, which was developed based on arrival time calculation. The arm-wearable interface helps ground pilots to make decision about impact location themselves by stimulating their tactile-sense with motor vibration.

Multi-UAV Routing for Area Coverage and Remote Sensing with Minimum Time

PubMed Central

Avellar, Gustavo S. C.; Pereira, Guilherme A. S.; Pimenta, Luciano C. A.; Iscold, Paulo

2015-01-01

This paper presents a solution for the problem of minimum time coverage of ground areas using a group of unmanned air vehicles (UAVs) equipped with image sensors. The solution is divided into two parts: (i) the task modeling as a graph whose vertices are geographic coordinates determined in such a way that a single UAV would cover the area in minimum time; and (ii) the solution of a mixed integer linear programming problem, formulated according to the graph variables defined in the first part, to route the team of UAVs over the area. The main contribution of the proposed methodology, when compared with the traditional vehicle routing problem’s (VRP) solutions, is the fact that our method solves some practical problems only encountered during the execution of the task with actual UAVs. In this line, one of the main contributions of the paper is that the number of UAVs used to cover the area is automatically selected by solving the optimization problem. The number of UAVs is influenced by the vehicles’ maximum flight time and by the setup time, which is the time needed to prepare and launch a UAV. To illustrate the methodology, the paper presents experimental results obtained with two hand-launched, fixed-wing UAVs. PMID:26540055

Multi-UAV Routing for Area Coverage and Remote Sensing with Minimum Time.

PubMed

Avellar, Gustavo S C; Pereira, Guilherme A S; Pimenta, Luciano C A; Iscold, Paulo

2015-11-02

This paper presents a solution for the problem of minimum time coverage of ground areas using a group of unmanned air vehicles (UAVs) equipped with image sensors. The solution is divided into two parts: (i) the task modeling as a graph whose vertices are geographic coordinates determined in such a way that a single UAV would cover the area in minimum time; and (ii) the solution of a mixed integer linear programming problem, formulated according to the graph variables defined in the first part, to route the team of UAVs over the area. The main contribution of the proposed methodology, when compared with the traditional vehicle routing problem's (VRP) solutions, is the fact that our method solves some practical problems only encountered during the execution of the task with actual UAVs. In this line, one of the main contributions of the paper is that the number of UAVs used to cover the area is automatically selected by solving the optimization problem. The number of UAVs is influenced by the vehicles' maximum flight time and by the setup time, which is the time needed to prepare and launch a UAV. To illustrate the methodology, the paper presents experimental results obtained with two hand-launched, fixed-wing UAVs.

Construction of an unmanned aerial vehicle remote sensing system for crop monitoring

NASA Astrophysics Data System (ADS)

Jeong, Seungtaek; Ko, Jonghan; Kim, Mijeong; Kim, Jongkwon

2016-04-01

We constructed a lightweight unmanned aerial vehicle (UAV) remote sensing system and determined the ideal method for equipment setup, image acquisition, and image processing. Fields of rice paddy (Oryza sativa cv. Unkwang) grown under three different nitrogen (N) treatments of 0, 50, or 115 kg/ha were monitored at Chonnam National University, Gwangju, Republic of Korea, in 2013. A multispectral camera was used to acquire UAV images from the study site. Atmospheric correction of these images was completed using the empirical line method, and three-point (black, gray, and white) calibration boards were used as pseudo references. Evaluation of our corrected UAV-based remote sensing data revealed that correction efficiency and root mean square errors ranged from 0.77 to 0.95 and 0.01 to 0.05, respectively. The time series maps of simulated normalized difference vegetation index (NDVI) produced using the UAV images reproduced field variations of NDVI reasonably well, both within and between the different N treatments. We concluded that the UAV-based remote sensing technology utilized in this study is potentially an easy and simple way to quantitatively obtain reliable two-dimensional remote sensing information on crop growth.

A Multi-Purpose Simulation Environment for UAV Research

DTIC Science & Technology

2003-05-01

Maximum 200 Words) Unmanned aerial vehicles (UAVs) are playing an important role in today’s military initiatives. UAVs have proven to be invaluable in...battlefield commanders. Integration of new technologies necessitates simulation prior to fielding new systems in order to avoid costly er- rors. The unique...collection ofinformation if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD

Feasibility Study for an Autonomous UAV -Magnetometer System -- Final Report on SERDP SEED 1509:2206

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roelof Versteeg; Mark McKay; Matt Anderson

2007-09-01

Large areas across the United States are potentially contaminated with UXO, with some ranges encompassing tens to hundreds of thousands of acres. Technologies are needed which will allow for cost effective wide area scanning with 1) near 100 % coverage and 2) near 100 % detection of subsurface ordnance or features indicative of subsurface ordnance. The current approach to wide area scanning is a multi-level one, in which medium altitude fixed wing optical imaging is used for an initial site assessment. This assessment is followed with low altitude manned helicopter based magnetometry followed by surface investigations using either towed geophysicalmore » sensor arrays or man portable sensors. In order to be effective for small UXO detection, the sensing altitude for magnetic site investigations needs to be on the order of 1 – 3 meters. These altitude requirements means that manned helicopter surveys will generally only be feasible in large, open and relatively flat terrains. While such surveys are effective in mapping large areas relatively fast there are substantial mobilization/demobilization, staffing and equipment costs associated with these surveys (resulting in costs of approximately $100-$150/acre). Surface towed arrays provide high resolution maps but have other limitations, e.g. in their ability to navigate rough terrain effectively. Thus, other systems are needed allowing for effective data collection. An UAV (Unmanned Aerial Vehicle) magnetometer platform is an obvious alternative. The motivation behind such a system is that it would be safer for the operators, cheaper in initial and O&M costs, and more effective in terms of site characterization. However, while UAV data acquisition from fixed wing platforms for large (> 200 feet) stand off distances is relatively straight forward, a host of challenges exist for low stand-off distance (~ 6 feet) UAV geophysical data acquisition. The objective of SERDP SEED 1509:2006 was to identify the primary challenges associated with a low stand off distance autonomous UAV magnetometer platform and to investigate whether these challenges can be resolved successfully such that a successful UAV magnetometer platform can be constructed. The primary challenges which were identified and investigated include: 1. The feasibility of assembling a payload package which integrates magnetometers, accurate positioning systems (DGPS, height above ground measurement), obstacle avoidance systems, power infrastructure, communications and data storage as well as auxiliary flight controls 2. The availability of commercial UAV platforms with autonomous flight capability which can accommodate this payload package 3. The feasibility of integrating obstacle avoidance controls in UAV platform control 4. The feasibility of collecting high quality magnetic data in the vicinity of an UAV.« less

Chosen Aspects of the Production of the Basic Map Using Uav Imagery

NASA Astrophysics Data System (ADS)

Kedzierski, M.; Fryskowska, A.; Wierzbicki, D.; Nerc, P.

2016-06-01

For several years there has been an increasing interest in the use of unmanned aerial vehicles in acquiring image data from a low altitude. Considering the cost-effectiveness of the flight time of UAVs vs. conventional airplanes, the use of the former is advantageous when generating large scale accurate ortophotos. Through the development of UAV imagery, we can update large-scale basic maps. These maps are cartographic products which are used for registration, economic, and strategic planning. On the basis of these maps other cartographic maps are produced, for example maps used building planning. The article presents an assessesment of the usefulness of orthophotos based on UAV imagery to upgrade the basic map. In the research a compact, non-metric camera, mounted on a fixed wing powered by an electric motor was used. The tested area covered flat, agricultural and woodland terrains. The processing and analysis of orthorectification were carried out with the INPHO UASMaster programme. Due to the effect of UAV instability on low-altitude imagery, the use of non-metric digital cameras and the low-accuracy GPS-INS sensors, the geometry of images is visibly lower were compared to conventional digital aerial photos (large values of phi and kappa angles). Therefore, typically, low-altitude images require large along- and across-track direction overlap - usually above 70 %. As a result of the research orthoimages were obtained with a resolution of 0.06 meters and a horizontal accuracy of 0.10m. Digitized basic maps were used as the reference data. The accuracy of orthoimages vs. basic maps was estimated based on the study and on the available reference sources. As a result, it was found that the geometric accuracy and interpretative advantages of the final orthoimages allow the updating of basic maps. It is estimated that such an update of basic maps based on UAV imagery reduces processing time by approx. 40%.

Light-Weight Multispectral Uav Sensors and Their Capabilities for Predicting Grain Yield and Detecting Plant Diseases

NASA Astrophysics Data System (ADS)

Nebiker, S.; Lack, N.; Abächerli, M.; Läderach, S.

2016-06-01

In this paper we investigate the performance of new light-weight multispectral sensors for micro UAV and their application to selected tasks in agronomical research and agricultural practice. The investigations are based on a series of flight campaigns in 2014 and 2015 covering a number of agronomical test sites with experiments on rape, barley, onion, potato and other crops. In our sensor comparison we included a high-end multispectral multiSPEC 4C camera with bandpass colour filters and reference channel in zenith direction and a low-cost, consumer-grade Canon S110 NIR camera with Bayer pattern colour filters. Ground-based reference measurements were obtained using a terrestrial hyperspectral field spectrometer. The investigations show that measurements with the high-end system consistently match very well with ground-based field spectrometer measurements with a mean deviation of just 0.01-0.04 NDVI values. The low-cost system, while delivering better spatial resolutions, expressed significant biases. The sensors were subsequently used to address selected agronomical questions. These included crop yield estimation in rape and barley and plant disease detection in potato and onion cultivations. High levels of correlation between different vegetation indices and reference yield measurements were obtained for rape and barley. In case of barley, the NDRE index shows an average correlation of 87% with reference yield, when species are taken into account. With high geometric resolutions and respective GSDs of down to 2.5 cm the effects of a thrips infestation in onion could be analysed and potato blight was successfully detected at an early stage of infestation.

Technical Report: Unmanned Helicopter Solution for Survey-Grade Lidar and Hyperspectral Mapping

NASA Astrophysics Data System (ADS)

Kaňuk, Ján; Gallay, Michal; Eck, Christoph; Zgraggen, Carlo; Dvorný, Eduard

2018-05-01

Recent development of light-weight unmanned airborne vehicles (UAV) and miniaturization of sensors provide new possibilities for remote sensing and high-resolution mapping. Mini-UAV platforms are emerging, but powerful UAV platforms of higher payload capacity are required to carry the sensors for survey-grade mapping. In this paper, we demonstrate a technological solution and application of two different payloads for highly accurate and detailed mapping. The unmanned airborne system (UAS) comprises a Scout B1-100 autonomously operating UAV helicopter powered by a gasoline two-stroke engine with maximum take-off weight of 75 kg. The UAV allows for integrating of up to 18 kg of a customized payload. Our technological solution comprises two types of payload completely independent of the platform. The first payload contains a VUX-1 laser scanner (Riegl, Austria) and a Sony A6000 E-Mount photo camera. The second payload integrates a hyperspectral push-broom scanner AISA Kestrel 10 (Specim, Finland). The two payloads need to be alternated if mapping with both is required. Both payloads include an inertial navigation system xNAV550 (Oxford Technical Solutions Ltd., United Kingdom), a separate data link, and a power supply unit. Such a constellation allowed for achieving high accuracy of the flight line post-processing in two test missions. The standard deviation was 0.02 m (XY) and 0.025 m (Z), respectively. The intended application of the UAS was for high-resolution mapping and monitoring of landscape dynamics (landslides, erosion, flooding, or crops growth). The legal regulations for such UAV applications in Switzerland and Slovakia are also discussed.

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing.

PubMed

Kim, Hyunjun; Lee, Junhwa; Ahn, Eunjong; Cho, Soojin; Shin, Myoungsu; Sim, Sung-Han

2017-09-07

Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

A UAV-Based Fog Collector Design for Fine-Scale Aerobiological Sampling

NASA Technical Reports Server (NTRS)

Gentry, Diana; Guarro, Marcello; Demachkie, Isabella Siham; Stumfall, Isabel; Dahlgren, Robert P.

2017-01-01

Airborne microbes are found throughout the troposphere and into the stratosphere. Knowing how the activity of airborne microorganisms can alter water, carbon, and other geochemical cycles is vital to a full understanding of local and global ecosystems. Just as on the land or in the ocean, atmospheric regions vary in habitability; the underlying geochemical, climatic, and ecological dynamics must be characterized at different scales to be effectively modeled. Most aerobiological studies have focused on a high level: 'How high are airborne microbes found?' and 'How far can they travel?' Most fog and cloud water studies collect from stationary ground stations (point) or along flight transects (1D). To complement and provide context for this data, we have designed a UAV-based modified fog and cloud water collector to retrieve 4D-resolved samples for biological and chemical analysis.Our design uses a passive impacting collector hanging from a rigid rod suspended between two multi-rotor UAVs. The suspension design reduces the effect of turbulence and potential for contamination from the UAV downwash. The UAVs are currently modeled in a leader-follower configuration, taking advantage of recent advances in modular UAVs, UAV swarming, and flight planning.The collector itself is a hydrophobic mesh. Materials including Tyvek, PTFE, nylon, and polypropylene monofilament fabricated via laser cutting, CNC knife, or 3D printing were characterized for droplet collection efficiency using a benchtop atomizer and particle counter. Because the meshes can be easily and inexpensively fabricated, a set can be pre-sterilized and brought to the field for 'hot swapping' to decrease cross-contamination between flight sessions or use as negative controls.An onboard sensor and logging system records the time and location of each sample; when combined with flight tracking data, the samples can be resolved into a 4D volumetric map of the fog bank. Collected samples can be returned to the lab for a variety of analyses. Based on a review of existing flight studies, we have identified ion chromatography, metagenomic sequencing, cell staining and quantification, and ATP quantification as high-priority assays for implementation. Support for specific toxicology assays, such as methylmercury quantification, is also planned.

A UAV-Based Fog Collector Design for Fine-Scale Aerobiological Sampling

NASA Astrophysics Data System (ADS)

Gentry, D.; Guarro, M.; Demachkie, I. S.; Stumfall, I.; Dahlgren, R. P.

2016-12-01

Airborne microbes are found throughout the troposphere and into the stratosphere. Knowing how the activity of airborne microorganisms can alter water, carbon, and other geochemical cycles is vital to a full understanding of local and global ecosystems. Just as on the land or in the ocean, atmospheric regions vary in habitability; the underlying geochemical, climatic, and ecological dynamics must be characterized at different scales to be effectively modeled. Most aerobiological studies have focused on a high level: 'How high are airborne microbes found?' and 'How far can they travel?' Most fog and cloud water studies collect from stationary ground stations (point) or along flight transects (1D). To complement and provide context for this data, we have designed a UAV-based modified fog and cloud water collector to retrieve 4D-resolved samples for biological and chemical analysis. Our design uses a passive impacting collector hanging from a rigid rod suspended between two multi-rotor UAVs. The suspension design reduces the effect of turbulence and potential for contamination from the UAV downwash. The UAVs are currently modeled in a leader-follower configuration, taking advantage of recent advances in modular UAVs, UAV swarming, and flight planning. The collector itself is a hydrophobic mesh. Materials including Tyvek, PTFE, nylon, and polypropylene monofilament fabricated via laser cutting, CNC knife, or 3D printing were characterized for droplet collection efficiency using a benchtop atomizer and particle counter. Because the meshes can be easily and inexpensively fabricated, a set can be pre-sterilized and brought to the field for 'hot swapping' to decrease cross-contamination between flight sessions or use as negative controls. An onboard sensor and logging system records the time and location of each sample; when combined with flight tracking data, the samples can be resolved into a 4D volumetric map of the fog bank. Collected samples can be returned to the lab for a variety of analyses. Based on a review of existing flight studies, we have identified ion chromatography, metagenomic sequencing, cell staining and quantification, and ATP quantification as high-priority assays for implementation. Support for specific toxicology assays, such as methylmercury quantification, is also planned.

Risk-Free Volcano Observations Using an Unmanned Autonomous Helicopter: seismic observations near the active vent of Sakurajima volcano, Japan

NASA Astrophysics Data System (ADS)

Ohminato, T.; Kaneko, T.; Koyama, T.; Yasuda, A.; Watanabe, A.; Takeo, M.; Honda, Y.; Kajiwara, K.; Kanda, W.; Iguchi, M.; Yanagisawa, T.

2010-12-01

Observations in the vicinity of summit area of active volcanoes are important not only for understanding physical processes in the volcanic conduit but also for eruption prediction and volcanic hazards mitigation. It is, however, challenging to install observation sensors near active vents because of the danger of sudden eruptions. We need safe and efficient ways of installing sensors near the summit of active volcanoes. We have been developing an volcano observation system based on an unmanned autonomous vehicle (UAV) for risk-free volcano observations. Our UAV is an unmanned autonomous helicopter manufactured by Yamaha-Motor Co., Ltd. The UAV is 3.6m long and weighs 84kg with maximum payload of 10kg. The UAV can aviate autonomously along a previously programmed path within a meter accuracy using real-time kinematics differential GPS equipment. The maximum flight time and distance from the operator are 90 minutes and 5km, respectively. We have developed various types of volcano observation techniques adequate for the UAV, such as aeromagnetic survey, taking infrared and visible images from onboard high-resolution cameras, volcanic ash sampling in the vicinity of active vents. Recently, we have developed an earthquake observation module (EOM), which is exclusively designed for the UAV installation in the vicinity of active volcanic vent. In order to meet the various requirements for UAV installation, the EOM is very compact, light-weight (5-6kg), and is solar-powered. It is equipped with GPS for timing, a communication device using cellular-phone network, and triaxial accelerometers. Our first application of the EOM installation using the UAV is one of the most active volcanoes in Japan, Sakurajima volcano. Since 2006, explosive eruptions have been continuing at the reopened Showa crater at the eastern flank near the summit of Sakurajima. Entering the area within 2 km from the active craters is prohibited, and thus there were no observation station in the vicinity of active vents at the summit area. From November 2nd to 12th, 2009, we could successfully install four EOMs in the summit area within 2km from the active craters by using the UAV. Although the state of communication was not perfect since the installation points were outside of the service area of the cellular-phone network, we succeeded in retrieving the seismic waveform data accompanying moderate eruptions at Showa crater. Except for contamination by the mechanical resonance of the frame of EOM around 35 Hz, the recorded waveforms of the explosive eruptions are as good as the best permanent stations in Sakurajima. Preliminary results of the analyses show that the source location distribution of the explosion earthquakes at Showa crater is improved by the inclusion of the near source stations newly installed by using the UAV.

Uav Cameras: Overview and Geometric Calibration Benchmark

NASA Astrophysics Data System (ADS)

Cramer, M.; Przybilla, H.-J.; Zurhorst, A.

2017-08-01

Different UAV platforms and sensors are used in mapping already, many of them equipped with (sometimes) modified cameras as known from the consumer market. Even though these systems normally fulfil their requested mapping accuracy, the question arises, which system performs best? This asks for a benchmark, to check selected UAV based camera systems in well-defined, reproducible environments. Such benchmark is tried within this work here. Nine different cameras used on UAV platforms, representing typical camera classes, are considered. The focus is laid on the geometry here, which is tightly linked to the process of geometrical calibration of the system. In most applications the calibration is performed in-situ, i.e. calibration parameters are obtained as part of the project data itself. This is often motivated because consumer cameras do not keep constant geometry, thus, cannot be seen as metric cameras. Still, some of the commercial systems are quite stable over time, as it was proven from repeated (terrestrial) calibrations runs. Already (pre-)calibrated systems may offer advantages, especially when the block geometry of the project does not allow for a stable and sufficient in-situ calibration. Especially for such scenario close to metric UAV cameras may have advantages. Empirical airborne test flights in a calibration field have shown how block geometry influences the estimated calibration parameters and how consistent the parameters from lab calibration can be reproduced.

Low-cost lightweight airborne laser-based sensors for pipeline leak detection and reporting

NASA Astrophysics Data System (ADS)

Frish, Michael B.; Wainner, Richard T.; Laderer, Matthew C.; Allen, Mark G.; Rutherford, James; Wehnert, Paul; Dey, Sean; Gilchrist, John; Corbi, Ron; Picciaia, Daniele; Andreussi, Paolo; Furry, David

2013-05-01

Laser sensing enables aerial detection of natural gas pipeline leaks without need to fly through a hazardous gas plume. This paper describes adaptations of commercial laser-based methane sensing technology that provide relatively low-cost lightweight and battery-powered aerial leak sensors. The underlying technology is near-infrared Standoff Tunable Diode Laser Absorption Spectroscopy (sTDLAS). In one configuration, currently in commercial operation for pipeline surveillance, sTDLAS is combined with automated data reduction, alerting, navigation, and video imagery, integrated into a single-engine single-pilot light fixed-wing aircraft or helicopter platform. In a novel configuration for mapping landfill methane emissions, a miniaturized ultra-lightweight sTDLAS sensor flies aboard a small quad-rotor unmanned aerial vehicle (UAV).

UAV Deployed Sensor System for Arctic Ocean Remote Sensing

NASA Astrophysics Data System (ADS)

Palo, S. E.; Lawrence, D.; Weibel, D.; LoDolce, G.; Krist, S.; Crocker, I.; Maslanik, J. A.

2012-12-01

The Marginal Ice Zone Observations and Processes Experiment (MIZOPEX), is an Arctic field project scheduled for summer 2013. The goals of the project are to understand how warming of the marginal ice zone affects sea ice melt and if this warming has been over or underestimated by satellite measurements. To achieve these goals calibrated physical measurements, both remote and in-situ, of the marginal ice zone over scales of square kilometers with a resolution of square meters is required. This will be accomplished with a suite of unmanned aerial vehicles (UAVs) equipped with both remote sensing and in-situ instruments, air deployed microbuoys, and ship deployed buoys. In this talk we will present details about the air-deployed micro-buoy (ADMB) and self-deployed surface-sonde (SDSS) components of the MIZOPEX project, developed at the University of Colorado. These systems were designed to explore the potential of low-cost, on-demand access to high-latitude areas of important scientific interest. Both the ADMB and SDSS share a common measurement suite with the capability to measure water temperature at three distinct depths and provide position information via GPS. The ADMBs are dropped from the InSitu ScanEagle UAV and expected to operate and log ocean temperatures for 14 days. The SDSS are micro UAVs that are designed to fly one-way to a region of interest and land at specified coordinates, thereafter becoming a surface sensor similar to the ADMB. A ScanEagle will periodically return to the deployment zone to gather ADMB/SDSS data via low power radio links. Design decisions based upon operational constraints and the current status of the ADMB and SDSS will be presented.

A Modular, Reconfigurable Surveillance UAV Architecture

DTIC Science & Technology

2003-09-02

Una Società Galileo Avionica A Modular, Reconfigurable Surveillance UAV Architecture METEOR, Finmeccanica Group Zona Industriale di Soleschiano Via...ES) METEOR, Finmeccanica Group Zona Industriale di Soleschiano Via Mario Stoppani 21 34077 Ronchi dei Legionari (GO) ITALY 8. PERFORMING...PMSFMS RS1Backup FMS NSU Payload Control Actuators Router Router RS2 Recovery Devices Una Società Galileo Avionica • Daylight TV Camera • IR Sensor • HR

Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data - potential of unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Roosjen, Peter P. J.; Brede, Benjamin; Suomalainen, Juha M.; Bartholomeus, Harm M.; Kooistra, Lammert; Clevers, Jan G. P. W.

2018-04-01

In addition to single-angle reflectance data, multi-angular observations can be used as an additional information source for the retrieval of properties of an observed target surface. In this paper, we studied the potential of multi-angular reflectance data for the improvement of leaf area index (LAI) and leaf chlorophyll content (LCC) estimation by numerical inversion of the PROSAIL model. The potential for improvement of LAI and LCC was evaluated for both measured data and simulated data. The measured data was collected on 19 July 2016 by a frame-camera mounted on an unmanned aerial vehicle (UAV) over a potato field, where eight experimental plots of 30 × 30 m were designed with different fertilization levels. Dozens of viewing angles, covering the hemisphere up to around 30° from nadir, were obtained by a large forward and sideways overlap of collected images. Simultaneously to the UAV flight, in situ measurements of LAI and LCC were performed. Inversion of the PROSAIL model was done based on nadir data and based on multi-angular data collected by the UAV. Inversion based on the multi-angular data performed slightly better than inversion based on nadir data, indicated by the decrease in RMSE from 0.70 to 0.65 m2/m2 for the estimation of LAI, and from 17.35 to 17.29 μg/cm2 for the estimation of LCC, when nadir data were used and when multi-angular data were used, respectively. In addition to inversions based on measured data, we simulated several datasets at different multi-angular configurations and compared the accuracy of the inversions of these datasets with the inversion based on data simulated at nadir position. In general, the results based on simulated (synthetic) data indicated that when more viewing angles, more well distributed viewing angles, and viewing angles up to larger zenith angles were available for inversion, the most accurate estimations were obtained. Interestingly, when using spectra simulated at multi-angular sampling configurations as were captured by the UAV platform (view zenith angles up to 30°), already a huge improvement could be obtained when compared to solely using spectra simulated at nadir position. The results of this study show that the estimation of LAI and LCC by numerical inversion of the PROSAIL model can be improved when multi-angular observations are introduced. However, for the potato crop, PROSAIL inversion for measured data only showed moderate accuracy and slight improvements.

Coordinating UAV information for executing national security-oriented collaboration

NASA Astrophysics Data System (ADS)

Isenor, Anthony W.; Allard, Yannick; Lapinski, Anna-Liesa S.; Demers, Hugues; Radulescu, Dan

2014-10-01

Unmanned Aerial Vehicles (UAVs) are being used by numerous nations for defence-related missions. In some cases, the UAV is considered a cost-effective means to acquire data such as imagery over a location or object. Considering Canada's geographic expanse, UAVs are also being suggested as a potential platform for use in surveillance of remote areas, such as northern Canada. However, such activities are typically associated with security as opposed to defence. The use of a defence platform for security activities introduces the issue of information exchange between the defence and security communities and their software applications. This paper explores the flow of information from the system used by the UAVs employed by the Royal Canadian Navy. Multiple computers are setup, each with the information system used by the UAVs, including appropriate communication between the systems. Simulated data that may be expected from a typical maritime UAV mission is then fed into the information system. The information structures common to the Canadian security community are then used to store and transfer the simulated data. The resulting data flow from the defence-oriented UAV system to the security-oriented information structure is then displayed using an open source geospatial application. Use of the information structures and applications relevant to the security community avoids the distribution restrictions often associated with defence-specific applications.

Volcano surveillance by ACR silver fox

USGS Publications Warehouse

Patterson, M.C.L.; Mulligair, A.; Douglas, J.; Robinson, J.; Pallister, J.S.

2005-01-01

Recent growth in the business of unmanned air vehicles (UAVs) both in the US and abroad has improved their overall capability, resulting in a reduction in cost, greater reliability and adoption into areas where they had previously not been considered. Uses in coastal and border patrol, forestry and agriculture have recently been evaluated in an effort to expand the observed area and reduce surveillance and reconnaissance costs for information gathering. The scientific community has both contributed and benefited greatly in this development. A larger suite of light-weight miniaturized sensors now exists for a range of applications which in turn has led to an increase in the gathering of information from these autonomous vehicles. In October 2004 the first eruption of Mount St Helens since 1986 caused tremendous interest amoUg people worldwide. Volcanologists at the U.S. Geological Survey rapidly ramped up the level of monitoring using a variety of ground-based sensors deployed in the crater and on the flanks of the volcano using manned helicopters. In order to develop additional unmanned sensing methods that can be used in potentially hazardous and low visibility conditions, a UAV experiment was conducted during the ongoing eruption early in November. The Silver Fox UAV was flown over and inside the crater to perform routine observation and data gathering, thereby demonstrating a technology that could reduce physical risk to scientists and other field operatives. It was demonstrated that UAVs can be flown autonomously at an active volcano and can deliver real time data to a remote location. Although still relatively limited in extent, these initial flights provided information on volcanic activity and thermal conditions within the crater and at the new (2004) lava dome. The flights demonstrated that readily available visual and infrared video sensors mounted in a small and relatively low-cost aerial platform can provide useful data on volcanic phenomena. This was made possible by utilizing GPS and computer-controlled flight direction and stabilization to acquire and track target areas within the Mount St. Helens crater. It was also determined that additional light-weight sensor development will be needed to enable autonomous measurements of volcanic gasses and imaging in poor-weather conditions. Copyright ?? 2005 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

The use of accelerated radiation testing for avionics

NASA Astrophysics Data System (ADS)

Quinn, Heather

2013-04-01

In recent years, the use of unmanned aerial vehicles (UAVs) for military and national security applications has been increasing. One possible use of these vehicles is as remote sensing platforms, where the UAV carries several sensors to provide real-time information about biological, chemical or radiological agents that might have been released into the environment. One such UAV, the Global Hawk, has a payload space that can carry nearly one ton of sensing equipment, which makes these platforms significantly larger than many satellites. Given the size of the potential payload and the heightened radiation environment at high altitudes, these systems could be affected by the radiation-induced failure mechanisms from the naturally occurring terrestrial environment. In this paper, we will explore the use of accelerated radiation testing to prepare UAV payloads for deployment.

Measurement of greenhouse gases in UAE by using Unmanned Aerial Vehicle (UAV)

NASA Astrophysics Data System (ADS)

Abou-Elnour, Ali; Odeh, Mohamed; Abdelrhman, Mohammed; Balkis, Ahmed; Amira, Abdelraouf

2017-04-01

In the present work, a reliable and low cost system has been designed and implemented to measure greenhouse gases (GHG) in United Arab Emirates (UAE) by using unmanned aerial vehicle (UAV). A set of accurate gas, temperature, pressure, humidity sensors are integrated together with a wireless communication system on a microcontroller based platform to continuously measure the required data. The system instantaneously sends the measured data to a center monitoring unit via the wireless communication system. In addition, the proposed system has the features that all measurements are recorded directly in a storage device to allow effective monitoring in regions with weak or no wireless coverage. The obtained data will be used in all further sophisticated calculations for environmental research and monitoring purposes.

Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring.

PubMed

Trasviña-Moreno, Carlos A; Blasco, Rubén; Marco, Álvaro; Casas, Roberto; Trasviña-Castro, Armando

2017-02-24

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.

Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring

PubMed Central

Trasviña-Moreno, Carlos A.; Blasco, Rubén; Marco, Álvaro; Casas, Roberto; Trasviña-Castro, Armando

2017-01-01

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario. PMID:28245587

AirSTAR: A UAV Platform for Flight Dynamics and Control System Testing

NASA Technical Reports Server (NTRS)

Jordan, Thomas L.; Foster, John V.; Bailey, Roger M.; Belcastro, Christine M.

2006-01-01

As part of the NASA Aviation Safety Program at Langley Research Center, a dynamically scaled unmanned aerial vehicle (UAV) and associated ground based control system are being developed to investigate dynamics modeling and control of large transport vehicles in upset conditions. The UAV is a 5.5% (seven foot wingspan), twin turbine, generic transport aircraft with a sophisticated instrumentation and telemetry package. A ground based, real-time control system is located inside an operations vehicle for the research pilot and associated support personnel. The telemetry system supports over 70 channels of data plus video for the downlink and 30 channels for the control uplink. Data rates are in excess of 200 Hz. Dynamic scaling of the UAV, which includes dimensional, weight, inertial, actuation, and control system scaling, is required so that the sub-scale vehicle will realistically simulate the flight characteristics of the full-scale aircraft. This testbed will be utilized to validate modeling methods, flight dynamics characteristics, and control system designs for large transport aircraft, with the end goal being the development of technologies to reduce the fatal accident rate due to loss-of-control.

UAV magnetometry in mineral exploration and infrastructure detection

NASA Astrophysics Data System (ADS)

Braun, A.; Parvar, K.; Burns, M.

2015-12-01

Magnetic surveys are critical tools in mineral exploration and UAVs have the potential to carry magnetometers. UAV surveys can offer higher spatial resolution than traditional airborne surveys, and higher coverage than terrestrial surveys. However, the main advantage is their ability to sense the magnetic field in 3-D, while most airborne or terrestrial surveys are restricted to 2-D acquisition. This study compares UAV magnetic data from two different UAVs (JIB drone, DJI Phantom 2) and three different magnetometers (GEM GSPM35, Honeywell HMR2300, GEM GST-19). The first UAV survey was conducted using a JIB UAV with a GSPM35 flying at 10-15 m above ground. The survey's goal was to detect intrusive Rhyolite bodies for primary mineral exploration. The survey resulted in a better understanding of the validity/resolution of UAV data and led to improved knowledge about the geological structures in the area. The results further drove the design of a following terrestrial survey. Comparing the UAV data with an available airborne survey (upward continued to 250 m) reveals that the UAV data has superior spatial resolution, but exhibits a higher noise level. The magnetic anomalies related to the Rhyolite intrusions is about 109 nT and translates into an estimated depth of approximately 110 meters. The second survey was conducted using an in-house developed UAV magnetometer system equipped with a DJI Phantom 2 and a Honeywell HMR2300 fluxgate magnetometer. By flying the sensor in different altitudes, the vertical and horizontal gradients can be derived leading to full 3-D magnetic data volumes which can provide improved constraints for source depth/geometry characterization. We demonstrate that a buried steam pipeline was detectable with the UAV magnetometer system and compare the resulting data with a terrestrial survey using a GEM GST-19 Proton Precession Magnetometer.

a Three-Dimensional Simulation and Visualization System for Uav Photogrammetry

NASA Astrophysics Data System (ADS)

Liang, Y.; Qu, Y.; Cui, T.

2017-08-01

Nowadays UAVs has been widely used for large-scale surveying and mapping. Compared with manned aircraft, UAVs are more cost-effective and responsive. However, UAVs are usually more sensitive to wind condition, which greatly influences their positions and orientations. The flight height of a UAV is relative low, and the relief of the terrain may result in serious occlusions. Moreover, the observations acquired by the Position and Orientation System (POS) are usually less accurate than those acquired in manned aerial photogrammetry. All of these factors bring in uncertainties to UAV photogrammetry. To investigate these uncertainties, a three-dimensional simulation and visualization system has been developed. The system is demonstrated with flight plan evaluation, image matching, POS-supported direct georeferencing, and ortho-mosaicing. Experimental results show that the presented system is effective for flight plan evaluation. The generated image pairs are accurate and false matches can be effectively filtered. The presented system dynamically visualizes the results of direct georeferencing in three-dimensions, which is informative and effective for real-time target tracking and positioning. The dynamically generated orthomosaic can be used in emergency applications. The presented system has also been used for teaching theories and applications of UAV photogrammetry.

Evaluating RGB photogrammetry and multi-temporal digital surface models for detecting soil erosion

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Seeger, Manuel

2013-04-01

Photogrammetry is a widely used tool for generating high-resolution digital surface models. Unmanned Aerial Vehicles (UAVs), equipped with a Red Green Blue (RGB) camera, have great potential in quickly acquiring multi-temporal high-resolution orthophotos and surface models. Such datasets would ease the monitoring of geomorphological processes, such as local soil erosion and rill formation after heavy rainfall events. In this study we test a photogrammetric setup to determine data requirements for soil erosion studies with UAVs. We used a rainfall simulator (5 m2) and above a rig with attached a Panasonic GX1 16 megapixel digital camera and 20mm lens. The soil material in the simulator consisted of loamy sand at an angle of 5 degrees. Stereo pair images were taken before and after rainfall simulation with 75-85% overlap. Acquired images were automatically mosaicked to create high-resolution orthorectified images and digital surface models (DSM). We resampled the DSM to different spatial resolutions to analyze the effect of cell size to the accuracy of measured rill depth and soil loss estimations, and determined an optimal cell size (thus flight altitude). Furthermore, the high spatial accuracy of the acquired surface models allows further analysis of rill formation and channel initiation related to e.g. surface roughness. We suggest implementing near-infrared and temperature sensors to combine soil moisture and soil physical properties with surface morphology for future investigations.

Development of an Effective System Identification and Control Capability for Quad-copter UAVs

NASA Astrophysics Data System (ADS)

Wei, Wei

In recent years, with the promise of extensive commercial applications, the popularity of Unmanned Aerial Vehicles (UAVs) has dramatically increased as witnessed by publications and mushrooming research and educational programs. Over the years, multi-copter aircraft have been chosen as a viable configuration for small-scale VTOL UAVs in the form of quad-copters, hexa-copters and octo-copters. Compared to the single main rotor configuration such as the conventional helicopter, multi-copter airframes require a simpler feedback control system and fewer mechanical parts. These characteristics make these UAV platforms, such as quad-copter which is the main emphasis in this dissertation, a rugged and competitive candidate for many applications in both military and civil areas. Because of its configuration and relative size, the small-scale quad-copter UAV system is inherently very unstable. In order to develop an effective control system through simulation techniques, obtaining an accurate dynamic model of a given quad-copter is imperative. Moreover, given the anticipated stringent safety requirements, fault tolerance will be a crucial component of UAV certification. Accurate dynamic modeling and control of this class of UAV is an enabling technology and is imperative for future commercial applications. In this work, the dynamic model of a quad-copter system in hover flight was identified using frequency-domain system identification techniques. A new and unique experimental system, data acquisition and processing procedure was developed catering specifically to the class of electric powered multi-copter UAV systems. The Comprehensive Identification from FrEquency Responses (CIFER RTM) software package, developed by US Army Aviation Development Directorate -- AFDD, was utilized along with flight tests to develop dynamic models of the quad-copter system. A new set of flight tests were conducted and the predictive capability of the dynamic models were successfully validated. A PID controller and two fuzzy logic controllers were developed based on the validated dynamic models. The controller performances were evaluated and compared in both simulation environment and flight testing. Flight controllers were optimized to comply with US Aeronautical Design Standard Performance Specification Handling Quality Requirements for Military Rotorcraft (ADS-33E-PRF). Results showed a substantial improvement for developed controllers when compared to the nominal controllers based on hand tuning. The scope of this research involves experimental system hardware and software development, flight instrumentation, flight testing, dynamics modeling, system identification, dynamic model validation, control system modeling using PID and fuzzy logic, analysis of handling qualities, flight control optimization and validation. Both closed-loop and open-loop dynamics of the quad-copter system were analyzed. A cost-effective and high quality system identification procedure was applied and results proved in simulations as well as in flight tests.

Characterization of UAV Performance and Development of a Formation Flight Controller for Multiple Small UAVS

DTIC Science & Technology

2006-06-01

61 Figure 29 - Simulation #1 2-D Deviation at Waypoints...92 Figure 60 - Simulation #5 Airspeed 16m/s, Track Convergence 250..................................... 92 Figure 61 - Simulation #6...oz fuel tank, which puts its endurance close to 2 hrs. The Rascal makes use of a hybrid airfoil that, according to the manufacturer, is a

The Role of Unmanned Aerial Systems-Sensors in Air Quality Research

EPA Science Inventory

The use of unmanned aerial systems (UASs) and miniaturized sensors for a variety of scientific and security purposes has rapidly increased. UASs include aerostats (tethered balloons) and remotely controlled, unmanned aerial vehicles (UAVs) including lighter-than-air vessels, fix...

Distributed autonomous systems: resource management, planning, and control algorithms

NASA Astrophysics Data System (ADS)

Smith, James F., III; Nguyen, ThanhVu H.

2005-05-01

Distributed autonomous systems, i.e., systems that have separated distributed components, each of which, exhibit some degree of autonomy are increasingly providing solutions to naval and other DoD problems. Recently developed control, planning and resource allocation algorithms for two types of distributed autonomous systems will be discussed. The first distributed autonomous system (DAS) to be discussed consists of a collection of unmanned aerial vehicles (UAVs) that are under fuzzy logic control. The UAVs fly and conduct meteorological sampling in a coordinated fashion determined by their fuzzy logic controllers to determine the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy planning algorithm determines the optimal trajectory, sampling rate and pattern for the UAVs and an interferometer platform while taking into account risk, reliability, priority for sampling in certain regions, fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV will give the UAV limited autonomy allowing it to change course immediately without consulting with any commander, request other UAVs to help it, alter its sampling pattern and rate when observing interesting phenomena, or to terminate the mission and return to base. The algorithms developed will be compared to a resource manager (RM) developed for another DAS problem related to electronic attack (EA). This RM is based on fuzzy logic and optimized by evolutionary algorithms. It allows a group of dissimilar platforms to use EA resources distributed throughout the group. For both DAS types significant theoretical and simulation results will be presented.

Merging science, engineering, and data with FUN: Recreational Drones in STEaM Education Activities and Science Fair Projects

NASA Astrophysics Data System (ADS)

Olds, S. E.; Mooney, M. E.; Dahlman, L. E.

2016-12-01

Recreational drones, also known as unmanned aerial vehicles (UAVs), provide an ideal platform for engaging students in science, technology, engineering, and math (STEM) investigations for science fair projects, after-school clubs, and in-class activities. UAVs are very popular (estimate of >1 million received as gifts this past year), relatively inexpensive (<$100), weigh less than 250g (don't require FAA registration), are modifiable, and can carry small instrument packages. Seeing the world from above can stimulate curiosity and give students a reason to engage in the Next Generation Science Standards (NGSS) process of science and engineering practices by designing and carrying out their own investigations. Using drones to facilitate experiments, students also participate in engineering design: they may choose off-the-shelf sensors or build DIY sensors to carry on their UAVs. Leveraging the learning potential of UAVs, the Federation of Earth Science Information Partners (ESIP) Education Committee has been developing an e-book of learning activities and investigation suggestions for secondary education students. The freely available download incorporates UAV civility and safety through a pre-flight checklist and flying guidelines, suggests science and flight team roles, and advocates robust data and metadata-collection practices. The ESIP team also worked with an engineer to build a 33-gram prototype environmental logger called SABEL (Shelley (Olds) and Bob's Environmental Logger). SABEL collects temperature, humidity, and GPS position assembled on an Arduino board. This presentation will elaborate upon the year-long process of working with educators via webinars and a 1-day workshop at the 2016 ESIP summer meeting and beyond. It will also provide examples of student-led investigations, instructions for building the SABEL sensor package, insights gleaned from workshop feedback - and - the status of the new e-book compilation of student-focused activities using recreational drones to pursue STEM investigations!

A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification

PubMed Central

Kumar, G. Ajay; Patil, Ashok Kumar; Patil, Rekha; Park, Seong Sill; Chai, Young Ho

2017-01-01

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering. PMID:28574474

a Micro-Uav with the Capability of Direct Georeferencing

NASA Astrophysics Data System (ADS)

Rehak, M.; Mabillard, R.; Skaloud, J.

2013-08-01

This paper presents the development of a low cost UAV (Unmanned Aerial Vehicle) with the capability of direct georeferencing. The advantage of such system lies in its high maneuverability, operation flexibility as well as capability to acquire image data without the need of establishing ground control points (GCPs). Moreover, the precise georeferencing offers an improvement in the final mapping accuracy when employing integrated sensor orientation. Such mode of operation limits the number and distribution of GCPs, which in turns save time in their signalization and surveying. Although the UAV systems feature high flexibility and capability of flying into areas that are inhospitable or inaccessible to humans, the lack of precision in positioning and attitude estimation on-board decrease the gained value of the captured imagery and limits their mode of operation to specific configurations and need of groundreference. Within a scope of this study we show the potential of present technologies in the field of position and orientation determination on a small UAV. The hardware implementation and especially the non-trivial synchronization of all components is clarified. Thanks to the implementation of a multi-frequency, low power GNSS receiver and its coupling with redundant MEMSIMU, we can attain the characteristic of a much larger systems flown on large carries while keeping the sensor size and weight suitable for MAV operations.

Detection and Learning of Unexpected Behaviors of Systems of Dynamical Systems by Using the Q2 Abstractions

DTIC Science & Technology

2017-11-01

Finite State Machine ............................................... 21 9 Main Ontological Concepts for Representing Structure of a Multi -Agent...19 NetLogo Simulation of persistent surveillance of circular plume by 4 UAVs ........................36 20 Flocking Emergent Behaviors in Multi -UAV...Region) - Undesirable Group Formation ................................................................................... 40 24 Two UAVs Moving in

Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators

PubMed Central

Ruano, Susana; Cuevas, Carlos; Gallego, Guillermo; García, Narciso

2017-01-01

Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other displays information about the mission plan and information coming from other sensors. To avoid the burden of fusing information displayed in the two screens, an Augmented Reality (AR) tool is proposed in this paper. The AR system has two functionalities for Medium-Altitude Long-Endurance (MALE) UAVs: route orientation and target identification. Route orientation allows the operator to identify the upcoming waypoints and the path that the UAV is going to follow. Target identification allows a fast target localization, even in the presence of occlusions. The AR tool is implemented following the North Atlantic Treaty Organization (NATO) standards so that it can be used in different GCSs. The experiments show how the AR tool improves significantly the situational awareness of the UAV operators. PMID:28178189

Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators.

PubMed

Ruano, Susana; Cuevas, Carlos; Gallego, Guillermo; García, Narciso

2017-02-06

Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other displays information about the mission plan and information coming from other sensors. To avoid the burden of fusing information displayed in the two screens, an Augmented Reality (AR) tool is proposed in this paper. The AR system has two functionalities for Medium-Altitude Long-Endurance (MALE) UAVs: route orientation and target identification. Route orientation allows the operator to identify the upcoming waypoints and the path that the UAV is going to follow. Target identification allows a fast target localization, even in the presence of occlusions. The AR tool is implemented following the North Atlantic Treaty Organization (NATO) standards so that it can be used in different GCSs. The experiments show how the AR tool improves significantly the situational awareness of the UAV operators.

The pan-sharpening of satellite and UAV imagery for agricultural applications

NASA Astrophysics Data System (ADS)

Jenerowicz, Agnieszka; Woroszkiewicz, Malgorzata

2016-10-01

Remote sensing techniques are widely used in many different areas of interest, i.e. urban studies, environmental studies, agriculture, etc., due to fact that they provide rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. Agricultural management is one of the most common application of remote sensing methods nowadays. Monitoring of agricultural sites and creating information regarding spatial distribution and characteristics of crops are important tasks to provide data for precision agriculture, crop management and registries of agricultural lands. For monitoring of cultivated areas many different types of remote sensing data can be used- most popular are multispectral satellites imagery. Such data allow for generating land use and land cover maps, based on various methods of image processing and remote sensing methods. This paper presents fusion of satellite and unnamed aerial vehicle (UAV) imagery for agricultural applications, especially for distinguishing crop types. Authors in their article presented chosen data fusion methods for satellite images and data obtained from low altitudes. Moreover the authors described pan- sharpening approaches and applied chosen pan- sharpening methods for multiresolution image fusion of satellite and UAV imagery. For such purpose, satellite images from Landsat- 8 OLI sensor and data collected within various UAV flights (with mounted RGB camera) were used. In this article, the authors not only had shown the potential of fusion of satellite and UAV images, but also presented the application of pan- sharpening in crop identification and management.

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

PubMed Central

Lee, Junhwa; Ahn, Eunjong; Cho, Soojin; Shin, Myoungsu

2017-01-01

Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%. PMID:28880254

Application of Vehicle Dynamic Modeling in Uavs for Precise Determination of Exterior Orientation

NASA Astrophysics Data System (ADS)

Khaghani, M.; Skaloud, J.

2016-06-01

Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

Gimbal Influence on the Stability of Exterior Orientation Parameters of UAV Acquired Images.

PubMed

Gašparović, Mateo; Jurjević, Luka

2017-02-18

In this paper, results from the analysis of the gimbal impact on the determination of the camera exterior orientation parameters of an Unmanned Aerial Vehicle (UAV) are presented and interpreted. Additionally, a new approach and methodology for testing the influence of gimbals on the exterior orientation parameters of UAV acquired images is presented. The main motive of this study is to examine the possibility of obtaining better geometry and favorable spatial bundles of rays of images in UAV photogrammetric surveying. The subject is a 3-axis brushless gimbal based on a controller board (Storm32). Only two gimbal axes are taken into consideration: roll and pitch axes. Testing was done in a flight simulation, and in indoor and outdoor flight mode, to analyze the Inertial Measurement Unit (IMU) and photogrammetric data. Within these tests the change of the exterior orientation parameters without the use of a gimbal is determined, as well as the potential accuracy of the stabilization with the use of a gimbal. The results show that using a gimbal has huge potential. Significantly, smaller discrepancies between data are noticed when a gimbal is used in flight simulation mode, even four times smaller than in other test modes. In this test the potential accuracy of a low budget gimbal for application in real conditions is determined.

Modeling and simulation of dynamic ant colony's labor division for task allocation of UAV swarm

NASA Astrophysics Data System (ADS)

Wu, Husheng; Li, Hao; Xiao, Renbin; Liu, Jie

2018-02-01

The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of "problem centered + evolutionary solution" and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony's labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs' real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.

Implementation of AN Unmanned Aerial Vehicle System for Large Scale Mapping

NASA Astrophysics Data System (ADS)

Mah, S. B.; Cryderman, C. S.

2015-08-01

Unmanned Aerial Vehicles (UAVs), digital cameras, powerful personal computers, and software have made it possible for geomatics professionals to capture aerial photographs and generate digital terrain models and orthophotographs without using full scale aircraft or hiring mapping professionals. This has been made possible by the availability of miniaturized computers and sensors, and software which has been driven, in part, by the demand for this technology in consumer items such as smartphones. The other force that is in play is the increasing number of Do-It-Yourself (DIY) people who are building UAVs as a hobby or for professional use. Building a UAV system for mapping is an alternative to purchasing a turnkey system. This paper describes factors to be considered when building a UAV mapping system, the choices made, and the test results of a project using this completed system.

An improved dehazing algorithm of aerial high-definition image

NASA Astrophysics Data System (ADS)

Jiang, Wentao; Ji, Ming; Huang, Xiying; Wang, Chao; Yang, Yizhou; Li, Tao; Wang, Jiaoying; Zhang, Ying

2016-01-01

For unmanned aerial vehicle(UAV) images, the sensor can not get high quality images due to fog and haze weather. To solve this problem, An improved dehazing algorithm of aerial high-definition image is proposed. Based on the model of dark channel prior, the new algorithm firstly extracts the edges from crude estimated transmission map and expands the extracted edges. Then according to the expended edges, the algorithm sets a threshold value to divide the crude estimated transmission map into different areas and makes different guided filter on the different areas compute the optimized transmission map. The experimental results demonstrate that the performance of the proposed algorithm is substantially the same as the one based on dark channel prior and guided filter. The average computation time of the new algorithm is around 40% of the one as well as the detection ability of UAV image is improved effectively in fog and haze weather.

Autonomous collection of dynamically-cued multi-sensor imagery

NASA Astrophysics Data System (ADS)

Daniel, Brian; Wilson, Michael L.; Edelberg, Jason; Jensen, Mark; Johnson, Troy; Anderson, Scott

2011-05-01

The availability of imagery simultaneously collected from sensors of disparate modalities enhances an image analyst's situational awareness and expands the overall detection capability to a larger array of target classes. Dynamic cooperation between sensors is increasingly important for the collection of coincident data from multiple sensors either on the same or on different platforms suitable for UAV deployment. Of particular interest is autonomous collaboration between wide area survey detection, high-resolution inspection, and RF sensors that span large segments of the electromagnetic spectrum. The Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL) is building sensors with such networked communications capability and is conducting field tests to demonstrate the feasibility of collaborative sensor data collection and exploitation. Example survey / detection sensors include: NuSAR (NRL Unmanned SAR), a UAV compatible synthetic aperture radar system; microHSI, an NRL developed lightweight hyper-spectral imager; RASAR (Real-time Autonomous SAR), a lightweight podded synthetic aperture radar; and N-WAPSS-16 (Nighttime Wide-Area Persistent Surveillance Sensor-16Mpix), a MWIR large array gimbaled system. From these sensors, detected target cues are automatically sent to the NRL/SDL developed EyePod, a high-resolution, narrow FOV EO/IR sensor, for target inspection. In addition to this cooperative data collection, EyePod's real-time, autonomous target tracking capabilities will be demonstrated. Preliminary results and target analysis will be presented.

Land Surface Reflectance Retrieval from Hyperspectral Data Collected by an Unmanned Aerial Vehicle over the Baotou Test Site

PubMed Central

Duan, Si-Bo; Li, Zhao-Liang; Tang, Bo-Hui; Wu, Hua; Ma, Lingling; Zhao, Enyu; Li, Chuanrong

2013-01-01

To evaluate the in-flight performance of a new hyperspectral sensor onboard an unmanned aerial vehicle (UAV-HYPER), a comprehensive field campaign was conducted over the Baotou test site in China on 3 September 2011. Several portable reference reflectance targets were deployed across the test site. The radiometric performance of the UAV-HYPER sensor was assessed in terms of signal-to-noise ratio (SNR) and the calibration accuracy. The SNR of the different bands of the UAV-HYPER sensor was estimated to be between approximately 5 and 120 over the homogeneous targets, and the linear response of the apparent reflectance ranged from approximately 0.05 to 0.45. The uniform and non-uniform Lambertian land surface reflectance was retrieved and validated using in situ measurements, with root mean square error (RMSE) of approximately 0.01–0.07 and relative RMSE of approximately 5%–12%. There were small discrepancies between the retrieved uniform and non-uniform Lambertian land surface reflectance over the homogeneous targets and under low aerosol optical depth (AOD) conditions (AOD = 0.18). However, these discrepancies must be taken into account when adjacent pixels had large land surface reflectance contrast and under high AOD conditions (e.g. AOD = 1.0). PMID:23785513

Video change detection for fixed wing UAVs

NASA Astrophysics Data System (ADS)

Bartelsen, Jan; Müller, Thomas; Ring, Jochen; Mück, Klaus; Brüstle, Stefan; Erdnüß, Bastian; Lutz, Bastian; Herbst, Theresa

2017-10-01

In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the image processing and change detection, we use the approach of Muller.4 Although it was developed for unmanned ground vehicles (UGVs), it enables a near real time video change detection for aerial videos. Concluding, we discuss the demands on sensor systems in the matter of change detection.

UAV based mapping of variation in grassland yield for forage production in Arctic environments

NASA Astrophysics Data System (ADS)

Davids, C.; Karlsen, S. R.; Jørgensen, M.; Ancin Murguzur, F. J.

2017-12-01

Grassland cultivation for animal feed is the key agricultural activity in northern Norway. Even though the growing season has increased by at least a week in the last 30 years, grassland yields appear to have declined, probably due to more challenging winter conditions and changing agronomy practices. The ability for local and regional crop productivity forecasting would assist farmers with management decisions and would provide local and national authorities with a better overview over productivity and potential problems due to e.g. winter damage. Remote sensing technology has long been used to estimate and map the variability of various biophysical parameters, but calibration is important. In order to establish the relationship between spectral reflectance and grass yield in northern European environments we combine Sentinel-2 time series, UAV-based multispectral measurements, and ground-based spectroradiometry, with biomass analyses and observations of species composition. In this presentation we will focus on the results from the UAV data acquisition. We used a multirotor UAV with different sensors (a multispectral Rikola camera, and NDVI and RGB cameras) to image a number of cultivated grasslands of different age and productivity in northern Norway in June/July 2016 and 2017. Following UAV data acquisition, 10 to 20 in situ measurements were made per field using a FieldSpec3 (350-2500 nm). In addition, samples were taken to determine biomass and grass species composition. The imaging and sampling was done immediately prior to harvesting. The Rikola camera, when used as a stand-alone camera mounted on a UAV, can collect 15 bands with a spectral width of 10-15 nm in the range between 500-890 nm. In the initial analysis of the 2016 data we investigated how well different vegetation indices correlated with biomass and showed that vegetation indices that include red edge bands perform better than widely used indices such as NDVI. We will extend the analysis with partial least square regression once the 2017 data becomes available and in this presentation we will show the results of both the partial least square regression analysis and vegetation indices for the pooled data from the 2016 and 2017 acquisition.

Miniaturised Gravity Sensors for Remote Gravity Surveys.

NASA Astrophysics Data System (ADS)

Middlemiss, R. P.; Bramsiepe, S. G.; Hough, J.; Paul, D. J.; Rowan, S.; Samarelli, A.; Hammond, G.

2016-12-01

Gravimetry lets us see the world from a completely different perspective. The ability to measure tiny variations in gravitational acceleration (g), allows one to see not just the Earth's gravitational pull, but the influence of smaller objects. The more accurate the gravimeter, the smaller the objects one can see. Gravimetry has applications in many different fields: from tracking magma moving under volcanoes before eruptions; to locating hidden tunnels. The top commercial gravimeters weigh tens of kg and cost at least $100,000, limiting the situations in which they can be used. By contrast, smart phones use a MEMS (microelectromechanical system) accelerometer that can measure the orientation of the device. These are not nearly sensitive or stable enough to be used for the gravimetry but they are cheap, light-weight and mass-producible. At Glasgow University we have developed a MEMS device with both the stability and sensitivity for useful gravimetric measurements. This was demonstrated by a measurement of the Earth tides - the first time this has been achieved with a MEMS sensor. A gravimeter of this size opens up the possiblility for new gravity imaging modalities. Thousands of gravimeters could be networked over a survey site, storing data on an SD card or communicating wirelessly to a remote location. These devices could also be small enough to be carried by a UAVs: airborne gravity surveys could be carried out at low altitude by mulitple UAVs, or UAVs could be used to deliver ground based gravimeters to remote or inaccessible locations.

Development of a Micro-UAV Hyperspectral Imaging Platform for Assessing Hydrogeological Hazards

NASA Astrophysics Data System (ADS)

Chen, Z.; Alabsi, M.

2015-12-01

The exacerbating global weather changes have cast significant impacts upon the proportion of water supplied to agriculture. Therefore, one of the 21stCentury Grant Challenges faced by global population is securing water for food. However, the soil-water behavior in an agricultural environment is complex; among others, one of the key properties we recognize is water repellence or hydrophobicity, which affects many hydrogeological and hazardous conditions such as excessive water infiltration, runoff, and soil erosion. Under a US-Israel research program funded by USDA and BARD at Israel, we have proposed the development of a novel micro-unmanned aerial vehicle (micro-UAV or drone) based hyperspectral imaging platform for identifying and assessing soil repellence at low altitudes with enhanced flexibility, much reduced cost, and ultimately easy use. This aerial imaging system consists of a generic micro-UAV, hyperspectral sensor aided by GPS/IMU, on-board computing units, and a ground station. The target benefits of this system include: (1) programmable waypoint navigation and robotic control for multi-view imaging; (2) ability of two- or three-dimensional scene reconstruction for complex terrains; and (3) fusion with other sensors to realize real-time diagnosis (e.g., humidity and solar irradiation that may affect soil-water sensing). In this talk we present our methodology and processes in integration of hyperspectral imaging, on-board sensing and computing, hyperspectral data modeling, and preliminary field demonstration and verification of the developed prototype.

Gust alleviation of highly flexible UAVs with artificial hair sensors

NASA Astrophysics Data System (ADS)

Su, Weihua; Reich, Gregory W.

2015-04-01

Artificial hair sensors (AHS) have been recently developed in Air Force Research Laboratory (AFRL) using carbon nanotube (CNT). The deformation of CNT in air flow causes voltage and current changes in the circuit, which can be used to quantify the dynamic pressure and aerodynamic load along the wing surface. AFRL has done a lot of essential work in design, manufacturing, and measurement of AHSs. The work in this paper is to bridge the current AFRL's work on AHSs and their feasible applications in flight dynamics and control (e.g., the gust alleviation) of highly flexible aircraft. A highly flexible vehicle is modeled using a strain-based geometrically nonlinear beam formulation, coupled with finite-state inflow aerodynamics. A feedback control algorithm for the rejection of gust perturbations will be developed. A simplified Linear Quadratic Regulator (LQR) controller will be implemented based on the state-space representation of the linearized system. All AHS measurements will be used as the control input, i.e., wing sectional aerodynamic loads will be defined as the control output for designing the feedback gain. Once the controller is designed, closed-loop aeroelastic simulations will be performed to evaluate the performance of different controllers with the force feedback and be compared to traditional controller designs with the state feedback. From the study, the feasibility of AHSs in flight control will be assessed. The whole study will facilitate in building a fly-by-feel simulation environment for autonomous vehicles.

Auditory decision aiding in supervisory control of multiple unmanned aerial vehicles.

PubMed

Donmez, Birsen; Cummings, M L; Graham, Hudson D

2009-10-01

This article is an investigation of the effectiveness of sonifications, which are continuous auditory alerts mapped to the state of a monitored task, in supporting unmanned aerial vehicle (UAV) supervisory control. UAV supervisory control requires monitoring a UAV across multiple tasks (e.g., course maintenance) via a predominantly visual display, which currently is supported with discrete auditory alerts. Sonification has been shown to enhance monitoring performance in domains such as anesthesiology by allowing an operator to immediately determine an entity's (e.g., patient) current and projected states, and is a promising alternative to discrete alerts in UAV control. However, minimal research compares sonification to discrete alerts, and no research assesses the effectiveness of sonification for monitoring multiple entities (e.g., multiple UAVs). The authors conducted an experiment with 39 military personnel, using a simulated setup. Participants controlled single and multiple UAVs and received sonifications or discrete alerts based on UAV course deviations and late target arrivals. Regardless of the number of UAVs supervised, the course deviation sonification resulted in reactions to course deviations that were 1.9 s faster, a 19% enhancement, compared with discrete alerts. However, course deviation sonifications interfered with the effectiveness of discrete late arrival alerts in general and with operator responses to late arrivals when supervising multiple vehicles. Sonifications can outperform discrete alerts when designed to aid operators to predict future states of monitored tasks. However, sonifications may mask other auditory alerts and interfere with other monitoring tasks that require divided attention. This research has implications for supervisory control display design.

Modeling and Analysis of the Hurricane Imaging Radiometer (HIRAD)

NASA Technical Reports Server (NTRS)

Mauro, Stephanie

2013-01-01

The Hurricane Imaging Radiometer (HIRad) is a payload carried by an unmanned aerial vehicle (UAV) at altitudes up to 60,000 ft with the purpose of measuring ocean surface wind speeds and near ocean surface rain rates in hurricanes. The payload includes several components that must maintain steady temperatures throughout the flight. Minimizing the temperature drift of these components allows for accurate data collection and conclusions to be drawn concerning the behavior of hurricanes. HIRad has flown on several different UAVs over the past two years during the fall hurricane season. Based on the data from the 2011 flight, a Thermal Desktop model was created to simulate the payload and reproduce the temperatures. Using this model, recommendations were made to reduce the temperature drift through the use of heaters controlled by resistance temperature detector (RTD) sensors. The suggestions made were implemented for the 2012 hurricane season and further data was collected. The implementation of the heaters reduced the temperature drift for a portion of the flight, but after a period of time, the temperatures rose. With this new flight data, the thermal model was updated and correlated. Detailed analysis was conducted to determine a more effective way to reduce the temperature drift. The final recommendations made were to adjust the set temperatures of the heaters for 2013 flights and implement hardware changes for flights beyond 2013.

Thermal Modeling and Analysis of the Hurricane Imaging Radiometer (HIRad)

NASA Technical Reports Server (NTRS)

Mauro, Stephanie

2013-01-01

The Hurricane Imaging Radiometer (HIRad) is a payload carried by an unmanned aerial vehicle (UAV) at altitudes up to 60,000 ft with the purpose of measuring ocean surface wind speeds and near ocean surface rain rates in hurricanes. The payload includes several components that must maintain steady temperatures throughout the flight. Minimizing the temperature drift of these components allows for accurate data collection and conclusions to be drawn concerning the behavior of hurricanes. HIRad has flown on several different UAVs over the past two years during the fall hurricane season. Based on the data from the 2011 flight, a Thermal Desktop model was created to simulate the payload and reproduce the temperatures. Using this model, recommendations were made to reduce the temperature drift through the use of heaters controlled by resistance temperature detector (RTD) sensors. The suggestions made were implemented for the 2012 hurricane season and further data was collected. The implementation of the heaters reduced the temperature drift for a portion of the flight, but after a period of time, the temperatures rose. With this new flight data, the thermal model was updated and correlated. Detailed analysis was conducted to determine a more effective way to reduce the temperature drift. The final recommendations made were to adjust the set temperatures of the heaters for 2013 flights and implement hardware changes for flights beyond 2013.

Tactical Reconnaissance: UAVS Versus Manned Aircraft

DTIC Science & Technology

1997-03-01

level missions under the missile and radar detection envelope. It flew 1651 missions with a return rate of 87.2%. 3 The development of newer and more... level . The organizational structure of manned aircraft squadrons is therefore time tested and clearly defined. 12 By contrast UAV operations have had a ... a high level of information and sensor technology is available today it has been integrated to only a fraction of its combat potential following the

Achieving an Optimal Medium Altitude UAV Force Balance in Support of COIN Operations

DTIC Science & Technology

2009-02-02

and execute operations. UAS with common data links and remote video terminals (RVTs) provide input to the common operational picture (COP) and...full-motion video (FMV) is intuitive to many tactical warfighters who have used similar sensors in manned aircraft. Modern data links allow the video ...Document (AFDD) 2-9. Intelligence, Surveillance, and Reconnaissance Operations, 17 July 2007. Baldor, Lolita C. “Increased UAV reliance evident in

UAV remote sening for precision agriculture

NASA Astrophysics Data System (ADS)

Vigneau, Nathalie; Chéron, Corentin; Mainfroy, Florent; Faroux, Romain

2014-05-01

Airinov offers to farmers, scientists and experimenters (plant breeders, etc.) its technical skills about UAVs, cartography and agronomic remote sensing. The UAV is a 2-m-wingspan flying wing. It can carry away either a RGB camera or a multispectral sensor, which records reflectance in 4 spectral bands. The spectral characteristics of the sensor are modular. Each spectral band is comprised between 400 and 850 nm and the FWHM (Full Width at Half Maximum) is between 10 and 40 nm. The spatial resolution varies according to sensor, flying height and user needs from 15cm/px for multispectral sensor at 150m to 1.5cm/px for RGB camera at 50m. The flight is totally automatic thanks to on-board autopilot, IMU (Inertial Measurement Unit) and GPS. Data processing (unvignetting, mosaicking, correction in reflectance) leads to agronomic variables as LAI (Leaf Area Index) or chlorophyll content for barley, wheat, rape and maize as well as vegetation indices as NDVI (Normalized Difference Vegetation Index). Using these data, Airinov can product advices for farmers as nitrogen preconisation for rape. For scientists, Airinov offers trial plot monitoring by micro-plots vectorisation and numerical data exctraction micro-plot by micro-plot. This can lead to kinetic curve for LAI or NDVI to compare cover establishment for different genotypes for example. Airinov's system is a new way to monitor plots with a lot of data (biophysical or biochemical parameters) at high rate, high spatial resolution and high precision.

System Aware Cybersecurity: A Multi-Sentinel Scheme to Protect a Weapons Research Lab

DTIC Science & Technology

2015-12-07

In the simplified deployment scenario, some sensors report their output over a wireless link and other sensors are connected via CAT 5 (Ethernet...cable to reduce the chance of a wireless ‘jamming’ event impacting ALL sensors . In addition to this first sensor suite ( Sensor Suite “A”), the team...generating wind turbines , and video reconnaissance systems on unmanned aerial vehicles (UAVs). The most basic decision problem in designing a systems

Magnetic profiling of the San Andreas Fault using a dual magnetometer UAV aerial survey system.

NASA Astrophysics Data System (ADS)

Abbate, J. A.; Angelopoulos, V.; Masongsong, E. V.; Yang, J.; Medina, H. R.; Moon, S.; Davis, P. M.

2017-12-01

Aeromagnetic survey methods using planes are more time-effective than hand-held methods, but can be far more expensive per unit area unless large areas are covered. The availability of low cost UAVs and low cost, lightweight fluxgate magnetometers (FGMs) allows, with proper offset determination and stray fields correction, for low-cost magnetic surveys. Towards that end, we have developed a custom multicopter UAV for magnetic mapping using a dual 3-axis fluxgate magnetometer system: the GEOphysical Drone Enhanced Survey Instrument (GEODESI). A high precision sensor measures the UAV's position and attitude (roll, pitch, and yaw) and is recorded using a custom Arduino data processing system. The two FGMs (in-board and out-board) are placed on two ends of a vertical 1m boom attached to the base of the UAV. The in-board FGM is most sensitive to stray fields from the UAV and its signal is used, after scaling, to clean the signal of the out-board FGM from the vehicle noise. The FGMs record three orthogonal components of the magnetic field in the UAV body coordinates which are then transformed into a north-east-down coordinate system using a rotation matrix determined from the roll-pitch-yaw attitude data. This ensures knowledge of the direction of all three field components enabling us to perform inverse modeling of magnetic anomalies with greater accuracy than total or vertical field measurements used in the past. Field tests were performed at Dragon's Back Pressure Ridge in the Carrizo Plain of California, where there is a known crossing of the San Andreas Fault. Our data and models were compared to previously acquired LiDAR and hand-held magnetometer measurements. Further tests will be carried out to solidify our results and streamline our processing for educational use in the classroom and student field training.

Open area 2 × 2 MIMO channel model for 2 GHz low-elevation links with diversity and capacity applications

NASA Astrophysics Data System (ADS)

Zelený, J.; Pérez-Fontán, F.; Pechac, P.; Mariño-Espiñeira, P.

2017-05-01

In civil surveillance applications, unmanned aerial vehicles (UAV) are being increasingly used in floods, fires, and law enforcement scenarios. In order to transfer large amounts of information from UAV-mounted cameras, relays, or sensors, large bandwidths are needed in comparison to those required for remotely commanding the UAV. This demands the use of higher-frequency bands, in all probability in the vicinity of 2 or 5 GHz. Novel hardware developments need propagation channel models for the ample range of operational scenarios envisaged, including multiple-input, multiple-output (MIMO) deployments. These configurations may enable a more robust transmission by increasing either the carrier-to-noise ratio statistics or the achievable capacity. In this paper, a 2 × 2 MIMO propagation channel model for an open-field environment capable of synthesizing a narrowband time series at 2 GHz is described. Maximal ratio combining diversity and capacity improvements are also evaluated through synthetic series and compared with measurement results. A simple flat, open scenario was evaluated based on which other, more complex environments can be modeled.

Feasibility Study On Missile Launch Detection And Trajectory Tracking

DTIC Science & Technology

2016-09-01

Vehicles ( UAVs ) in military operations, their role in a missile defense operation is not well defined. The simulation program discussed in this thesis ...targeting information to an attacking UAV to reliably intercept the missile. B . FURTHER STUDIES The simulation program can be enhanced to improve the...intercept the threat. This thesis explores the challenges in creating a simulation program to process video footage from an unstable platform and the

Multi-objective control for cooperative payload transport with rotorcraft UAVs.

PubMed

Gimenez, Javier; Gandolfo, Daniel C; Salinas, Lucio R; Rosales, Claudio; Carelli, Ricardo

2018-06-01

A novel kinematic formation controller based on null-space theory is proposed to transport a cable-suspended payload with two rotorcraft UAVs considering collision avoidance, wind perturbations, and properly distribution of the load weight. An accurate 6-DoF nonlinear dynamic model of a helicopter and models for flexible cables and payload are included to test the proposal in a realistic scenario. System stability is demonstrated using Lyapunov theory and several simulation results show the good performance of the approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

NASA Astrophysics Data System (ADS)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

In situ Volcanic Plume Monitoring with small Unmanned Aerial Systems for Cal/Val of Satellite Remote Sensing Data: CARTA-UAV 2013 Mission (Invited)

NASA Astrophysics Data System (ADS)

Diaz, J. A.; Pieri, D. C.; Bland, G.; Fladeland, M. M.

2013-12-01

The development of small unmanned aerial systems (sUAS) with a variety of sensor packages, enables in situ and proximal remote sensing measurements of volcanic plumes. Using Costa Rican volcanoes as a Natural Laboratory, the University of Costa Rica as host institution, in collaboration with four NASA centers, have started an initiative to develop low-cost, field-deployable airborne platforms to perform volcanic gas & ash plume research, and in-situ volcanic monitoring in general, in conjunction with orbital assets and state-of-the-art models of plume transport and composition. Several gas sensors have been deployed into the active plume of Turrialba Volcano including a miniature mass spectrometer, and an electrochemical SO2 sensor system with temperature, pressure, relative humidity, and GPS sensors. Several different airborne platforms such as manned research aircraft, unmanned aerial vehicles, tethered balloons, as well as man-portable in-situ ground truth systems are being used for this research. Remote sensing data is also collected from the ASTER and OMI spaceborne instruments and compared with in situ data. The CARTA-UAV 2013 Mission deployment and follow up measurements successfully demonstrated a path to study and visualize gaseous volcanic emissions using mass spectrometer and gas sensor based instrumentation in harsh environment conditions to correlate in situ ground/airborne data with remote sensing satellite data for calibration and validation purposes. The deployment of such technology improves on our current capabilities to detect, analyze, monitor, model, and predict hazards presented to aircraft by volcanogenic ash clouds from active and impending volcanic eruptions.

Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes

NASA Astrophysics Data System (ADS)

Honkavaara, Eija; Rosnell, Tomi; Oliveira, Raquel; Tommaselli, Antonio

2017-12-01

A recent revolution in miniaturised sensor technology has provided markets with novel hyperspectral imagers operating in the frame format principle. In the case of unmanned aerial vehicle (UAV) based remote sensing, the frame format technology is highly attractive in comparison to the commonly utilised pushbroom scanning technology, because it offers better stability and the possibility to capture stereoscopic data sets, bringing an opportunity for 3D hyperspectral object reconstruction. Tuneable filters are one of the approaches for capturing multi- or hyperspectral frame images. The individual bands are not aligned when operating a sensor based on tuneable filters from a mobile platform, such as UAV, because the full spectrum recording is carried out in the time-sequential principle. The objective of this investigation was to study the aspects of band registration of an imager based on tuneable filters and to develop a rigorous and efficient approach for band registration in complex 3D scenes, such as forests. The method first determines the orientations of selected reference bands and reconstructs the 3D scene using structure-from-motion and dense image matching technologies. The bands, without orientation, are then matched to the oriented bands accounting the 3D scene to provide exterior orientations, and afterwards, hyperspectral orthomosaics, or hyperspectral point clouds, are calculated. The uncertainty aspects of the novel approach were studied. An empirical assessment was carried out in a forested environment using hyperspectral images captured with a hyperspectral 2D frame format camera, based on a tuneable Fabry-Pérot interferometer (FPI) on board a multicopter and supported by a high spatial resolution consumer colour camera. A theoretical assessment showed that the method was capable of providing band registration accuracy better than 0.5-pixel size. The empirical assessment proved the performance and showed that, with the novel method, most parts of the band misalignments were less than the pixel size. Furthermore, it was shown that the performance of the band alignment was dependent on the spatial distance from the reference band.

Advanced Doppler radar physiological sensing technique for drone detection

NASA Astrophysics Data System (ADS)

Yoon, Ji Hwan; Xu, Hao; Garcia Carrillo, Luis R.

2017-05-01

A 24 GHz medium-range human detecting sensor, using the Doppler Radar Physiological Sensing (DRPS) technique, which can also detect unmanned aerial vehicles (UAVs or drones), is currently under development for potential rescue and anti-drone applications. DRPS systems are specifically designed to remotely monitor small movements of non-metallic human tissues such as cardiopulmonary activity and respiration. Once optimized, the unique capabilities of DRPS could be used to detect UAVs. Initial measurements have shown that DRPS technology is able to detect moving and stationary humans, as well as largely non-metallic multi-rotor drone helicopters. Further data processing will incorporate pattern recognition to detect multiple signatures (motor vibration and hovering patterns) of UAVs.

Investigation of a robust tendon-sheath mechanism for flexible membrane wing application in mini-UAV

NASA Astrophysics Data System (ADS)

Lee, Shian; Tjahjowidodo, Tegoeh; Lee, Hsuchew; Lai, Benedict

2017-02-01

Two inherent issues manifest themselves in flying mini-unmanned aerial vehicles (mini-UAV) in the dense area at tropical climate regions, namely disturbances from gusty winds and limited space for deployment tasks. Flexible membrane wing (FMW) UAVs are seen to be potentials to mitigate these problems. FMWs are adaptable to gusty airflow as the wings are able to flex according to the gust load to reduce the effective angle-of-attack, thus, reducing the aerodynamic loads on the wing. On the other hand, the flexible structure is allowing the UAV to fold in a compact package, and later on, the mini-UAV can be deployed instantly from the storage tube, e.g. through a catapult mechanism. This paper discusses the development of an FMW UAV actuated by a tendon-sheath mechanism (TSM). This approach allows the wing to morph to generate a rolling moment, while still allowing the wing to fold. Dynamic characteristics of the mechanism that exhibits the strong nonlinear phenomenon of friction on TSM are modeled and compensated for. A feed-forward controller was implemented based on the identified nonlinear behavior to control the warping position of the wing. The proposed strategy is validated experimentally in a wind tunnel facility by creating a gusty environment that is imitating a realistic gusty condition based upon the results of computational fluid dynamics (CFD) simulation. The results demonstrate a stable and robust wing-warping actuation, even in gusty conditions. Accurate wing-warping can be achieved via the TSM, while also allowing the wings to fold.

New technologies for HWIL testing of WFOV, large-format FPA sensor systems

NASA Astrophysics Data System (ADS)

Fink, Christopher

2016-05-01

Advancements in FPA density and associated wide-field-of-view infrared sensors (>=4000x4000 detectors) have outpaced the current-art HWIL technology. Whether testing in optical projection or digital signal injection modes, current-art technologies for infrared scene projection, digital injection interfaces, and scene generation systems simply lack the required resolution and bandwidth. For example, the L3 Cincinnati Electronics ultra-high resolution MWIR Camera deployed in some UAV reconnaissance systems features 16MP resolution at 60Hz, while the current upper limit of IR emitter arrays is ~1MP, and single-channel dual-link DVI throughput of COTs graphics cards is limited to 2560x1580 pixels at 60Hz. Moreover, there are significant challenges in real-time, closed-loop, physics-based IR scene generation for large format FPAs, including the size and spatial detail required for very large area terrains, and multi - channel low-latency synchronization to achieve the required bandwidth. In this paper, the author's team presents some of their ongoing research and technical approaches toward HWIL testing of large-format FPAs with wide-FOV optics. One approach presented is a hybrid projection/injection design, where digital signal injection is used to augment the resolution of current-art IRSPs, utilizing a multi-channel, high-fidelity physics-based IR scene simulator in conjunction with a novel image composition hardware unit, to allow projection in the foveal region of the sensor, while non-foveal regions of the sensor array are simultaneously stimulated via direct injection into the post-detector electronics.

Medium Altitude Endurance Unmanned Air Vehicle

NASA Astrophysics Data System (ADS)

Ernst, Larry L.

1994-10-01

The medium altitude endurance unmanned air vehicle (MAE UAV) program (formerly the tactical endurance TE UAV) is a new effort initiated by the Department of Defense to develop a ground launched UAV that can fly out 500 miles, remain on station for 24 hours, and return. It will transmit high resolution optical, infrared, and synthetic aperture radar (SAR) images of well-defended target areas through satellite links. It will provide near-real-time, releasable, low cost/low risk surveillance, targeting and damage assessment complementary to that of satellites and manned aircraft. The paper describes specific objectives of the MAE UAV program (deliverables and schedule) and the program's unique position as one of several programs to streamline the acquisition process under the cognizance of the newly established Airborne Reconnaissance Office. I discuss the system requirements and operational concept and describe the technical capabilities and characteristics of the major subsystems (airframe, propulsion, navigation, sensors, communication links, ground station, etc.) in some detail.

GPS Remote Sensing Measurements Using Aerosonde UAV

NASA Technical Reports Server (NTRS)

Grant, Michael S.; Katzberg, Stephen J.; Lawrence, R. W.

2005-01-01

In February 2004, a NASA-Langley GPS Remote Sensor (GPSRS) unit was flown on an Aerosonde unmanned aerial vehicle (UAV) from the Wallops Flight Facility (WFF) in Virginia. Using direct and surface-reflected 1.575 GHz coarse acquisition (C/A) coded GPS signals, remote sensing measurements were obtained over land and portions of open water. The strength of the surface-reflected GPS signal is proportional to the amount of moisture in the surface, and is also influenced by surface roughness. Amplitude and other characteristics of the reflected signal allow an estimate of wind speed over open water. In this paper we provide a synopsis of the instrument accommodation requirements, installation procedures, and preliminary results from what is likely the first-ever flight of a GPS remote sensing instrument on a UAV. The correct operation of the GPSRS unit on this flight indicates that Aerosonde-like UAV's can serve as platforms for future GPS remote sensing science missions.

On parallel hybrid-electric propulsion system for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Hung, J. Y.; Gonzalez, L. F.

2012-05-01

This paper presents a review of existing and current developments and the analysis of Hybrid-Electric Propulsion Systems (HEPS) for small fixed-wing Unmanned Aerial Vehicles (UAVs). Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. One technology with potential in this area is with the use of HEPS. In this paper, information on the state-of-art technology in this field of research is provided. A description and simulation of a parallel HEPS for a small fixed-wing UAV by incorporating an Ideal Operating Line (IOL) control strategy is described. Simulation models of the components in a HEPS were designed in the MATLAB Simulink environment. An IOL analysis of an UAV piston engine was used to determine the most efficient points of operation for this engine. The results show that an UAV equipped with this HEPS configuration is capable of achieving a fuel saving of 6.5%, compared to the engine-only configuration.

Using unmanned aerial vehicle-borne magnetic sensors to detect and locate improvised explosive devices and unexploded ordnance

NASA Astrophysics Data System (ADS)

Trammell, Hoke S., III; Perry, Alexander R.; Kumar, Sankaran; Czipott, Peter V.; Whitecotton, Brian R.; McManus, Tobin J.; Walsh, David O.

2005-05-01

Magnetic sensors configured as a tensor magnetic gradiometer not only detect magnetic targets, but also determine their location and their magnetic moment. Magnetic moment information can be used to characterize and classify objects. Unexploded ordnance (UXO) and thus many types of improvised explosive device (IED) contain steel, and thus can be detected magnetically. Suitable unmanned aerial vehicle (UAV) platforms, both gliders and powered craft, can enable coverage of a search area much more rapidly than surveys using, for instance, total-field magnetometers. We present data from gradiometer passes over different shells using a gradiometer mounted on a moving cart. We also provide detection range and speed estimates for aerial detection by a UAV.

Comprehensive UAV agricultural remote-sensing research at Texas A M University

NASA Astrophysics Data System (ADS)

Thomasson, J. Alex; Shi, Yeyin; Olsenholler, Jeffrey; Valasek, John; Murray, Seth C.; Bishop, Michael P.

2016-05-01

Unmanned aerial vehicles (UAVs) have advantages over manned vehicles for agricultural remote sensing. Flying UAVs is less expensive, is more flexible in scheduling, enables lower altitudes, uses lower speeds, and provides better spatial resolution for imaging. The main disadvantage is that, at lower altitudes and speeds, only small areas can be imaged. However, on large farms with contiguous fields, high-quality images can be collected regularly by using UAVs with appropriate sensing technologies that enable high-quality image mosaics to be created with sufficient metadata and ground-control points. In the United States, rules governing the use of aircraft are promulgated and enforced by the Federal Aviation Administration (FAA), and rules governing UAVs are currently in flux. Operators must apply for appropriate permissions to fly UAVs. In the summer of 2015 Texas A&M University's agricultural research agency, Texas A&M AgriLife Research, embarked on a comprehensive program of remote sensing with UAVs at its 568-ha Brazos Bottom Research Farm. This farm is made up of numerous fields where various crops are grown in plots or complete fields. The crops include cotton, corn, sorghum, and wheat. After gaining FAA permission to fly at the farm, the research team used multiple fixed-wing and rotary-wing UAVs along with various sensors to collect images over all parts of the farm at least once per week. This article reports on details of flight operations and sensing and analysis protocols, and it includes some lessons learned in the process of developing a UAV remote-sensing effort of this sort.

System identification of a small low-cost unmanned aerial vehicle using flight data from low-cost sensors

NASA Astrophysics Data System (ADS)

Hoffer, Nathan Von

Remote sensing has traditionally been done with satellites and manned aircraft. While. these methods can yield useful scientificc data, satellites and manned aircraft have limitations in data frequency, process time, and real time re-tasking. Small low-cost unmanned aerial vehicles (UAVs) provide greater possibilities for personal scientic research than traditional remote sensing platforms. Precision aerial data requires an accurate vehicle dynamics model for controller development, robust flight characteristics, and fault tolerance. One method of developing a model is system identification (system ID). In this thesis system ID of a small low-cost fixed-wing T-tail UAV is conducted. The linerized longitudinal equations of motion are derived from first principles. Foundations of Recursive Least Squares (RLS) are presented along with RLS with an Error Filtering Online Learning scheme (EFOL). Sensors, data collection, data consistency checking, and data processing are described. Batch least squares (BLS) and BLS with EFOL are used to identify aerodynamic coecoefficients of the UAV. Results of these two methods with flight data are discussed.

The acoustic vector sensor: a versatile battlefield acoustics sensor

NASA Astrophysics Data System (ADS)

de Bree, Hans-Elias; Wind, Jelmer W.

2011-06-01

The invention of the Microflown sensor has made it possible to measure acoustic particle velocity directly. An acoustic vector sensor (AVS) measures the particle velocity in three directions (the source direction) and the pressure. The sensor is a uniquely versatile battlefield sensor because its size is a few millimeters and it is sensitive to sound from 10Hz to 10kHz. This article shows field tests results of acoustic vector sensors, measuring rifles, heavy artillery, fixed wing aircraft and helicopters. Experimental data shows that the sensor is suitable as a ground sensor, mounted on a vehicle and on a UAV.

C2 System Engineering for the Adversities of the Amazon Environment

DTIC Science & Technology

2013-06-01

within Brazil (60%), followed by Peru (13%), Colombia (10%), and with minor amounts in Venezuela, Ecuador , Bolivia, Guyana, Suriname and French Guiana...connection is permanently maintained. The UAVs fly according to a given movement pattern, such as a random movement for instance. In the case of a...with the Aerostat or the island of sensor nodes in the case of the UAVs that are in the extremities of this network) is about to break. This

Airborne geophysics for mesoscale observations of polar sea ice in a changing climate

NASA Astrophysics Data System (ADS)

Hendricks, S.; Haas, C.; Krumpen, T.; Eicken, H.; Mahoney, A. R.

2016-12-01

Sea ice thickness is an important geophysical parameter with a significant impact on various processes of the polar energy balance. It is classified as Essential Climate Variable (ECV), however the direct observations of the large ice-covered oceans are limited due to the harsh environmental conditions and logistical constraints. Sea-ice thickness retrieval by the means of satellite remote sensing is an active field of research, but current observational capabilities are not able to capture the small scale variability of sea ice thickness and its evolution in the presence of surface melt. We present an airborne observation system based on a towed electromagnetic induction sensor that delivers long range measurements of sea ice thickness for a wide range of sea ice conditions. The purpose-built sensor equipment can be utilized from helicopters and polar research aircraft in multi-role science missions. While airborne EM induction sounding is used in sea ice research for decades, the future challenge is the development of unmanned aerial vehicle (UAV) platform that meet the requirements for low-level EM sea ice surveys in terms of range and altitude of operations. The use of UAV's could enable repeated sea ice surveys during the the polar night, when manned operations are too dangerous and the observational data base is presently very sparse.

Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest

NASA Astrophysics Data System (ADS)

Tian, Jinyan; Wang, Le; Li, Xiaojuan; Gong, Huili; Shi, Chen; Zhong, Ruofei; Liu, Xiaomeng

2017-09-01

Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2). In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R2 = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R2 = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation.

Vision-based sensing for autonomous in-flight refueling

NASA Astrophysics Data System (ADS)

Scott, D.; Toal, M.; Dale, J.

2007-04-01

A significant capability of unmanned airborne vehicles (UAV's) is that they can operate tirelessly and at maximum efficiency in comparison to their human pilot counterparts. However a major limiting factor preventing ultra-long endurance missions is that they require landing to refuel. Development effort has been directed to allow UAV's to automatically refuel in the air using current refueling systems and procedures. The 'hose & drogue' refueling system was targeted as it is considered the more difficult case. Recent flight trials resulted in the first-ever fully autonomous airborne refueling operation. Development has gone into precision GPS-based navigation sensors to maneuver the aircraft into the station-keeping position and onwards to dock with the refueling drogue. However in the terminal phases of docking, the accuracy of the GPS is operating at its performance limit and also disturbance factors on the flexible hose and basket are not predictable using an open-loop model. Hence there is significant uncertainty on the position of the refueling drogue relative to the aircraft, and is insufficient in practical operation to achieve a successful and safe docking. A solution is to augment the GPS based system with a vision-based sensor component through the terminal phase to visually acquire and track the drogue in 3D space. The higher bandwidth and resolution of camera sensors gives significantly better estimates on the state of the drogue position. Disturbances in the actual drogue position caused by subtle aircraft maneuvers and wind gusting can be visually tracked and compensated for, providing an accurate estimate. This paper discusses the issues involved in visually detecting a refueling drogue, selecting an optimum camera viewpoint, and acquiring and tracking the drogue throughout a widely varying operating range and conditions.

Comparison of Unmanned Aerial Vehicle Technology Versus Standard Practice in Identification of Hazards at a Mass Casualty Incident Scenario by Primary Care Paramedic Students.

PubMed

Jain, Trevor; Sibley, Aaron; Stryhn, Henrik; Hubloue, Ives

2018-01-31

Introduction The proliferation of unmanned aerial vehicles (UAV) has the potential to change the situational awareness of incident commanders allowing greater scene safety. The aim of this study was to compare UAV technology to standard practice (SP) in hazard identification during a simulated multi-vehicle motor collision (MVC) in terms of time to identification, accuracy and the order of hazard identification. A prospective observational cohort study was conducted with 21 students randomized into UAV or SP group, based on a MVC with 7 hazards. The UAV group remained at the UAV ground station while the SP group approached the scene. After identifying hazards the time and order was recorded. The mean time (SD, range) to identify the hazards were 3 minutes 41 seconds (1 minute 37 seconds, 1 minute 48 seconds-6 minutes 51 seconds) and 2 minutes 43 seconds (55 seconds, 1 minute 43 seconds-4 minutes 38 seconds) in UAV and SP groups corresponding to a mean difference of 58 seconds (P=0.11). A non-parametric permutation test showed a significant (P=0.04) difference in identification order. Both groups had 100% accuracy in hazard identification with no statistical difference in time for hazard identification. A difference was found in the identification order of hazards. (Disaster Med Public Health Preparedness. 2018;page 1 of 4).

High resolution and low altitude magnetic surveys for structural geology mapping in the Seabee mine, Saskatchewan, Canada, using UAV-MAG™ technology.

NASA Astrophysics Data System (ADS)

Braun, A.; Parvar, K.; Burns, M.

2017-12-01

Uninhabited Aerial Vehicles (UAV) provide the operational flexibility and ease of use which makes them ideal tools for low altitude and high resolution magnetic surveys. Being able to fly at lower altitudes compared to manned aircrafts provides the proximity to the target needed to increase the sensitivity to detect smaller and less magnetic targets. Considering the same sensor specifications, this further increases the signal to noise ratio. However, to increase spatial resolution, a tighter line spacing is needed which increases the survey time. We describe a case study in the Seabee mine in Saskatchewan, Canada. Using Pioneer Exploration Ltd. UAV-MAG™ technology, we emphasize the importance of altitude and line spacing in magnetic surveys with UAVs in order to resolve smaller and less magnetic targets compared to conventional manned airborne magnetic surveys. Mapping lithological or stratigraphic changes along the target structure requires an existing gradient in magnetic susceptibility. Mostly, this criterium is either not presented or the is weaker than the sensor's signal to noise ratio at a certain flying altitude. However, the folded structure in the study region shows high susceptibility changes in rock formations in high altitude regional magnetic surveys. In order to confirm that there are no missed structural elements in the target region, a UAV magnetic survey using a GEM Systems GSMP-35A potassium vapor magnetometer on Pioneer Exploration's UAV-MAG™ platform was conducted to exploit the structure in detail and compare the gain in spatial resolution from flying at lower altitude and with denser flight lines. The survey was conducted at 25 meters above ground level (AGL). Line spacing was set to 15 meters and a total of 550 kilometers was covered using an autonomous UAV. The collected data were compared to the regional airborne data which were collected at 150 meters AGL with a line spacing of 100 meters. Comparison revealed an anticline with plunge in the northeastern side of the gird. The analysis of the magnetic data, both total magnetic intensity and gradients, reveals that the UAV survey is able to resolve much smaller structures than the manned airborne survey. These details also match observations made in previous geological mapping missions.

The study of aerosol and ozone measurements in lower boundary layer with UAV helicopter platform

NASA Astrophysics Data System (ADS)

Lin, Po-hsiung; Chen, Wen-nai

2013-04-01

This study describes the aerosol and ozone measurement in the lower atmospheric boundary layer of highly polluted region at Kao-hsiung, Taiwan with a small unmanned aerial vehicle (UAV) helicopter platform. This UAV helicopter, modified from Gaui-X7 electronic-power model helicopter with autopilot AHRS (Altitude-Head-Reference System) kit, has fast climb speed up to 700 m height and keeps stable status for atmospheric measurements in five-minute fly leg. Several quick-replaced battery packages are ready on ground for field intensive observation. The payload rack under this UAV helicopter carries a micro-Aethalometer (black carbon concentration), ozone meter, temperature-humidity sensor, barometer and a time-lapse digital camera. The field measurement site closes to Linyuan Petrochemical Industrial Park, where is one of the heavy polluted regions in Taiwan. Balloon-borne Vaisala RS-92 radiosonde and CL31 Lidar Ceilometer are used to provide the background of the atmosphere at the same time. More data analysis measured by UAV helicopter and its potential application will be discussed.

Assessing the Accuracy of Ortho-image using Photogrammetric Unmanned Aerial System

NASA Astrophysics Data System (ADS)

Jeong, H. H.; Park, J. W.; Kim, J. S.; Choi, C. U.

2016-06-01

Smart-camera can not only be operated under network environment anytime and any place but also cost less than the existing photogrammetric UAV since it provides high-resolution image, 3D location and attitude data on a real-time basis from a variety of built-in sensors. This study's proposed UAV photogrammetric method, low-cost UAV and smart camera were used. The elements of interior orientation were acquired through camera calibration. The image triangulation was conducted in accordance with presence or absence of consideration of the interior orientation (IO) parameters determined by camera calibration, The Digital Elevation Model (DEM) was constructed using the image data photographed at the target area and the results of the ground control point survey. This study also analyzes the proposed method's application possibility by comparing a Ortho-image the results of the ground control point survey. Considering these study findings, it is suggested that smartphone is very feasible as a payload for UAV system. It is also expected that smartphone may be loaded onto existing UAV playing direct or indirect roles significantly.

The View from a Few Hundred Feet : A New Transparent and Integrated Workflow for UAV-collected Data

NASA Astrophysics Data System (ADS)

Peterson, F. S.; Barbieri, L.; Wyngaard, J.

2015-12-01

Unmanned Aerial Vehicles (UAVs) allow scientists and civilians to monitor earth and atmospheric conditions in remote locations. To keep up with the rapid evolution of UAV technology, data workflows must also be flexible, integrated, and introspective. Here, we present our data workflow for a project to assess the feasibility of detecting threshold levels of methane, carbon-dioxide, and other aerosols by mounting consumer-grade gas analysis sensors on UAV's. Particularly, we highlight our use of Project Jupyter, a set of open-source software tools and documentation designed for developing "collaborative narratives" around scientific workflows. By embracing the GitHub-backed, multi-language systems available in Project Jupyter, we enable interaction and exploratory computation while simultaneously embracing distributed version control. Additionally, the transparency of this method builds trust with civilians and decision-makers and leverages collaboration and communication to resolve problems. The goal of this presentation is to provide a generic data workflow for scientific inquiries involving UAVs and to invite the participation of the AGU community in its improvement and curation.

a Uav Based 3-D Positioning Framework for Detecting Locations of Buried Persons in Collapsed Disaster Area

NASA Astrophysics Data System (ADS)

Moon, H.; Kim, C.; Lee, W.

2016-06-01

Regarding spatial location positioning, indoor location positioning theories based on wireless communication techniques such as Wi-Fi, beacon, UWB and Bluetooth has widely been developing across the world. These techniques are mainly focusing on spatial location detection of customers using fixed wireless APs and unique Tags in the indoor environment. Besides, since existing detection equipment and techniques using ultrasound or sound etc. to detect buried persons and identify survival status for them cause 2nd damages on the collapsed debris for rescuers. In addition, it might take time to check the buried persons. However, the collapsed disaster sites should consider both outdoor and indoor environments because empty spaces under collapsed debris exists. In order to detect buried persons from the empty spaces, we should collect wireless signals with Wi-Fi from their mobile phone. Basically, the Wi-Fi signal measure 2-D location. However, since the buried persons have Z value with burial depth, we also should collect barometer sensor data from their mobile phones in order to measure Z values according to weather conditions. Specially, for quick accessibility to the disaster area, a drone (UAV; Unmanned Arial Vehicle) system, which is equipped with a wireless detection module, was introduced. Using these framework, this study aims to provide the rescuers with effective rescue information by calculating 3-D location for buried persons based on the wireless and barometer sensor fusion.

Gimbal Influence on the Stability of Exterior Orientation Parameters of UAV Acquired Images

PubMed Central

Gašparović, Mateo; Jurjević, Luka

2017-01-01

In this paper, results from the analysis of the gimbal impact on the determination of the camera exterior orientation parameters of an Unmanned Aerial Vehicle (UAV) are presented and interpreted. Additionally, a new approach and methodology for testing the influence of gimbals on the exterior orientation parameters of UAV acquired images is presented. The main motive of this study is to examine the possibility of obtaining better geometry and favorable spatial bundles of rays of images in UAV photogrammetric surveying. The subject is a 3-axis brushless gimbal based on a controller board (Storm32). Only two gimbal axes are taken into consideration: roll and pitch axes. Testing was done in a flight simulation, and in indoor and outdoor flight mode, to analyze the Inertial Measurement Unit (IMU) and photogrammetric data. Within these tests the change of the exterior orientation parameters without the use of a gimbal is determined, as well as the potential accuracy of the stabilization with the use of a gimbal. The results show that using a gimbal has huge potential. Significantly, smaller discrepancies between data are noticed when a gimbal is used in flight simulation mode, even four times smaller than in other test modes. In this test the potential accuracy of a low budget gimbal for application in real conditions is determined. PMID:28218699

Spinoff 2010

NASA Technical Reports Server (NTRS)

2010-01-01

Topics covered include: Burnishing Techniques Strengthen Hip Implants; Signal Processing Methods Monitor Cranial Pressure; Ultraviolet-Blocking Lenses Protect, Enhance Vision; Hyperspectral Systems Increase Imaging Capabilities; Programs Model the Future of Air Traffic Management; Tail Rotor Airfoils Stabilize Helicopters, Reduce Noise; Personal Aircraft Point to the Future of Transportation; Ducted Fan Designs Lead to Potential New Vehicles; Winglets Save Billions of Dollars in Fuel Costs; Sensor Systems Collect Critical Aerodynamics Data; Coatings Extend Life of Engines and Infrastructure; Radiometers Optimize Local Weather Prediction; Energy-Efficient Systems Eliminate Icing Danger for UAVs; Rocket-Powered Parachutes Rescue Entire Planes; Technologies Advance UAVs for Science, Military; Inflatable Antennas Support Emergency Communication; Smart Sensors Assess Structural Health; Hand-Held Devices Detect Explosives and Chemical Agents; Terahertz Tools Advance Imaging for Security, Industry; LED Systems Target Plant Growth; Aerogels Insulate Against Extreme Temperatures; Image Sensors Enhance Camera Technologies; Lightweight Material Patches Allow for Quick Repairs; Nanomaterials Transform Hairstyling Tools; Do-It-Yourself Additives Recharge Auto Air Conditioning; Systems Analyze Water Quality in Real Time; Compact Radiometers Expand Climate Knowledge; Energy Servers Deliver Clean, Affordable Power; Solutions Remediate Contaminated Groundwater; Bacteria Provide Cleanup of Oil Spills, Wastewater; Reflective Coatings Protect People and Animals; Innovative Techniques Simplify Vibration Analysis; Modeling Tools Predict Flow in Fluid Dynamics; Verification Tools Secure Online Shopping, Banking; Toolsets Maintain Health of Complex Systems; Framework Resources Multiply Computing Power; Tools Automate Spacecraft Testing, Operation; GPS Software Packages Deliver Positioning Solutions; Solid-State Recorders Enhance Scientific Data Collection; Computer Models Simulate Fine Particle Dispersion; Composite Sandwich Technologies Lighten Components; Cameras Reveal Elements in the Short Wave Infrared; Deformable Mirrors Correct Optical Distortions; Stitching Techniques Advance Optics Manufacturing; Compact, Robust Chips Integrate Optical Functions; Fuel Cell Stations Automate Processes, Catalyst Testing; Onboard Systems Record Unique Videos of Space Missions; Space Research Results Purify Semiconductor Materials; and Toolkits Control Motion of Complex Robotics.

Ubiquitous UAVs: a cloud based framework for storing, accessing and processing huge amount of video footage in an efficient way

NASA Astrophysics Data System (ADS)

Efstathiou, Nectarios; Skitsas, Michael; Psaroudakis, Chrysostomos; Koutras, Nikolaos

2017-09-01

Nowadays, video surveillance cameras are used for the protection and monitoring of a huge number of facilities worldwide. An important element in such surveillance systems is the use of aerial video streams originating from onboard sensors located on Unmanned Aerial Vehicles (UAVs). Video surveillance using UAVs represent a vast amount of video to be transmitted, stored, analyzed and visualized in a real-time way. As a result, the introduction and development of systems able to handle huge amount of data become a necessity. In this paper, a new approach for the collection, transmission and storage of aerial videos and metadata is introduced. The objective of this work is twofold. First, the integration of the appropriate equipment in order to capture and transmit real-time video including metadata (i.e. position coordinates, target) from the UAV to the ground and, second, the utilization of the ADITESS Versatile Media Content Management System (VMCMS-GE) for storing of the video stream and the appropriate metadata. Beyond the storage, VMCMS-GE provides other efficient management capabilities such as searching and processing of videos, along with video transcoding. For the evaluation and demonstration of the proposed framework we execute a use case where the surveillance of critical infrastructure and the detection of suspicious activities is performed. Collected video Transcodingis subject of this evaluation as well.

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images

PubMed Central

Ortega-Terol, Damian; Ballesteros, Rocio

2017-01-01

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology. PMID:29036930

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

PubMed

Ortega-Terol, Damian; Hernandez-Lopez, David; Ballesteros, Rocio; Gonzalez-Aguilera, Diego

2017-10-15

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

Human-Robot Interaction Literature Review

DTIC Science & Technology

2012-03-01

coordination, operation of non-simulated UVs, and mixed UAV and UGV systems . Recommendations THRIL should focus on studies that look at operating non...Security, 2012). UAVs were continued to be developed in operations such as Desert Storm, where the Pioneer UAV system provided intelligence and fire... systems . One of the programs that was part of the initial fielding of UGVs in military operations was the Vehicle Teleoperation Capability (VTC). The

Mission-based guidance system design for autonomous UAVs

NASA Astrophysics Data System (ADS)

Moon, Jongki

The advantages of UAVs in the aviation arena have led to extensive research activities on autonomous technology of UAVs to achieve specific mission objectives. This thesis mainly focuses on the development of a mission-based guidance system. Among various missions expected for future needs, autonomous formation flight (AFF) and obstacle avoidance within safe operation limits are investigated. In the design of an adaptive guidance system for AFF, the leader information except position is assumed to be unknown to a follower. Thus, the only measured information related to the leader is the line-of-sight (LOS) range and angle. Adding an adaptive element with neural networks into the guidance system provides a capability to effectively handle leader's velocity changes. Therefore, this method can be applied to the AFF control systems that use a passive sensing method. In this thesis, an adaptive velocity command guidance system and an adaptive acceleration command guidance system are developed and presented. Since relative degrees of the LOS range and angle are different depending on the outputs from the guidance system, the architecture of the guidance system changes accordingly. Simulations and flight tests are performed using the Georgia Tech UAV helicopter, the GTMax, to evaluate the proposed guidance systems. The simulation results show that the neural network (NN) based adaptive element can improve the tracking performance by effectively compensating for the effect of unknown dynamics. It has also been shown that the combination of an adaptive velocity command guidance system and the existing GTMax autopilot controller performs better than the combination of an adaptive acceleration command guidance system and the GTMax autopilot controller. The successful flight evaluation using an adaptive velocity command guidance system clearly shows that the adaptive guidance control system is a promising solution for autonomous formation flight of UAVs. In addition, an integrated approach is proposed to resolve the conflict between aggressive maneuvering needed for obstacle avoidance and the constrained maneuvering needed for envelope protection. A time-optimal problem with obstacle and envelope constraints is used for an integrated approach for obstacle avoidance and envelope protection. The Nonlinear trajectory generator (NTG) is used as a real-time optimization solver. The computational complexity arising from the obstacle constraints is reduced by converting the obstacle constraints into a safe waypoint constraint along with an implicit requirement that the horizontal velocity during the avoidance maneuver must be nonnegative. The issue of when to initiate a time-optimal avoidance maneuver is addressed by including a requirement that the vehicle must maintain its original flight path to the maximum extent possible. The simulation evaluations are preformed for the nominal case, the unsafe avoidance solution case, the multiple safe waypoint case, and the unidentified obstacle size case. Artificial values for the load factor limit and the longitudinal flap angle limit are imposed as safe operational boundaries. Also, simulation results for different limit values and different initial flight speed are compared. Simulation results using a nonlinear model of a rotary wing UAV demonstrate the feasibility of the proposed approach for obstacle avoidance with envelope protection.

Strategies for soil-based precision agriculture in cotton

NASA Astrophysics Data System (ADS)

Neely, Haly L.; Morgan, Cristine L. S.; Stanislav, Scott; Rouze, Gregory; Shi, Yeyin; Thomasson, J. Alex; Valasek, John; Olsenholler, Jeff

2016-05-01

The goal of precision agriculture is to increase crop yield while maximizing the use efficiency of farm resources. In this application, UAV-based systems are presenting agricultural researchers with an opportunity to study crop response to environmental and management factors in real-time without disturbing the crop. The spatial variability soil properties, which drive crop yield and quality, cannot be changed and thus keen agronomic choices with soil variability in mind have the potential to increase profits. Additionally, measuring crop stress over time and in response to management and environmental conditions may enable agronomists and plant breeders to make more informed decisions about variety selection than the traditional end-of-season yield and quality measurements. In a previous study, seed-cotton yield was measured over 4 years and compared with soil variability as mapped by a proximal soil sensor. It was found that soil properties had a significant effect on seed-cotton yield and the effect was not consistent across years due to different precipitation conditions. However, when seed-cotton yield was compared to the normalized difference vegetation index (NDVI), as measured using a multispectral camera from a UAV, predictions improved. Further improvement was seen when soil-only pixels were removed from the analysis. On-going studies are using UAV-based data to uncover the thresholds for stress and yield potential. Long-term goals of this research include detecting stress before yield is reduced and selecting better adapted varieties.

Autonomous Control of a Quadrotor UAV Using Fuzzy Logic

NASA Astrophysics Data System (ADS)

Sureshkumar, Vijaykumar

UAVs are being increasingly used today than ever before in both military and civil applications. They are heavily preferred in "dull, dirty or dangerous" mission scenarios. Increasingly, UAVs of all kinds are being used in policing, fire-fighting, inspection of structures, pipelines etc. Recently, the FAA gave its permission for UAVs to be used on film sets for motion capture and high definition video recording. The rapid development in MEMS and actuator technology has made possible a plethora of UAVs that are suited for commercial applications in an increasingly cost effective manner. An emerging popular rotary wing UAV platform is the Quadrotor A Quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAVs are VTOL and hovering capabilities as well as a greater maneuverability. It is also simple in construction and design compared to a scaled single rotorcraft. Flying such UAVs using a traditional radio Transmitter-Receiver setup can be a daunting task especially in high stress situations. In order to make such platforms widely applicable, a certain level of autonomy is imperative to the future of such UAVs. This thesis paper presents a methodology for the autonomous control of a Quadrotor UAV using Fuzzy Logic. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. Modularity and adaptability are the key cornerstones of FLC. The objective of this thesis is to present the steps of designing, building and simulating an intelligent flight control module for a Quadrotor UAV. In the course of this research effort, a Quadrotor UAV is indigenously developed utilizing the resources of an online open source project called Aeroquad. System design is comprehensively dealt with. A math model for the Quadrotor is developed and a simulation environment is built in the MATLAB/Simulink framework. The Fuzzy flight controller development is discussed intensively. Validation of the math model developed is presented using actual flight data. Excellent attitude tracking is demonstrated for near hover flight regimes. The responses are analyzed and future work involving implementation is discussed.

Vision based control of unmanned aerial vehicles with applications to an autonomous four-rotor helicopter, quadrotor

NASA Astrophysics Data System (ADS)

Altug, Erdinc

Our work proposes a vision-based stabilization and output tracking control method for a model helicopter. This is a part of our effort to produce a rotorcraft based autonomous Unmanned Aerial Vehicle (UAV). Due to the desired maneuvering ability, a four-rotor helicopter has been chosen as the testbed. On previous research on flying vehicles, vision is usually used as a secondary sensor. Unlike previous research, our goal is to use visual feedback as the main sensor, which is not only responsible for detecting where the ground objects are but also for helicopter localization. A novel two-camera method has been introduced for estimating the full six degrees of freedom (DOF) pose of the helicopter. This two-camera system consists of a pan-tilt ground camera and an onboard camera. The pose estimation algorithm is compared through simulation to other methods, such as four-point, and stereo method and is shown to be less sensitive to feature detection errors. Helicopters are highly unstable flying vehicles; although this is good for agility, it makes the control harder. To build an autonomous helicopter, two methods of control are studied---one using a series of mode-based, feedback linearizing controllers and the other using a back-stepping control law. Various simulations with 2D and 3D models demonstrate the implementation of these controllers. We also show global convergence of the 3D quadrotor controller even with large calibration errors or presence of large errors on the image plane. Finally, we present initial flight experiments where the proposed pose estimation algorithm and non-linear control techniques have been implemented on a remote-controlled helicopter. The helicopter was restricted with a tether to vertical, yaw motions and limited x and y translations.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

PubMed

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-05-09

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

The Development of an Open Hardware and Software System Onboard Unmanned Aerial Vehicles to Monitor Concentrated Solar Power Plants

PubMed Central

Mesas-Carrascosa, Francisco Javier; Verdú Santano, Daniel; Pérez Porras, Fernando; Meroño-Larriva, José Emilio; García-Ferrer, Alfonso

2017-01-01

Concentrated solar power (CSP) plants are increasingly gaining interest as a source of renewable energy. These plants face several technical problems and the inspection of components such as absorber tubes in parabolic trough concentrators (PTC), which are widely deployed, is necessary to guarantee plant efficiency. This article presents a system for real-time industrial inspection of CSP plants using low-cost, open-source components in conjunction with a thermographic sensor and an unmanned aerial vehicle (UAV). The system, available in open-source hardware and software, is designed to be employed independently of the type of device used for inspection (laptop, smartphone, tablet or smartglasses) and its operating system. Several UAV flight missions were programmed as follows: flight altitudes at 20, 40, 60, 80, 100 and 120 m above ground level; and three cruising speeds: 5, 7 and 10 m/s. These settings were chosen and analyzed in order to optimize inspection time. The results indicate that it is possible to perform inspections by an UAV in real time at CSP plants as a means of detecting anomalous absorber tubes and improving the effectiveness of methodologies currently being utilized. Moreover, aside from thermographic sensors, this contribution can be applied to other sensors and can be used in a broad range of applications where real-time georeferenced data visualization is necessary. PMID:28594353

The Development of an Open Hardware and Software System Onboard Unmanned Aerial Vehicles to Monitor Concentrated Solar Power Plants.

PubMed

Mesas-Carrascosa, Francisco Javier; Verdú Santano, Daniel; Pérez Porras, Fernando; Meroño-Larriva, José Emilio; García-Ferrer, Alfonso

2017-06-08

Concentrated solar power (CSP) plants are increasingly gaining interest as a source of renewable energy. These plants face several technical problems and the inspection of components such as absorber tubes in parabolic trough concentrators (PTC), which are widely deployed, is necessary to guarantee plant efficiency. This article presents a system for real-time industrial inspection of CSP plants using low-cost, open-source components in conjunction with a thermographic sensor and an unmanned aerial vehicle (UAV). The system, available in open-source hardware and software, is designed to be employed independently of the type of device used for inspection (laptop, smartphone, tablet or smartglasses) and its operating system. Several UAV flight missions were programmed as follows: flight altitudes at 20, 40, 60, 80, 100 and 120 m above ground level; and three cruising speeds: 5, 7 and 10 m/s. These settings were chosen and analyzed in order to optimize inspection time. The results indicate that it is possible to perform inspections by an UAV in real time at CSP plants as a means of detecting anomalous absorber tubes and improving the effectiveness of methodologies currently being utilized. Moreover, aside from thermographic sensors, this contribution can be applied to other sensors and can be used in a broad range of applications where real-time georeferenced data visualization is necessary.

Networking Multiple Autonomous Air and Ocean Vehicles for Oceanographic Research and Monitoring

NASA Astrophysics Data System (ADS)

McGillivary, P. A.; Borges de Sousa, J.; Rajan, K.

2013-12-01

Autonomous underwater and surface vessels (AUVs and ASVs) are coming into wider use as components of oceanographic research, including ocean observing systems. Unmanned airborne vehicles (UAVs) are now available at modest cost, allowing multiple UAVs to be deployed with multiple AUVs and ASVs. For optimal use good communication and coordination among vehicles is essential. We report on the use of multiple AUVs networked in communication with multiple UAVs. The UAVs are augmented by inferential reasoning software developed at MBARI that allows UAVs to recognize oceanographic fronts and change their navigation and control. This in turn allows UAVs to automatically to map frontal features, as well as to direct AUVs and ASVs to proceed to such features and conduct sampling via onboard sensors to provide validation for airborne mapping. ASVs can also act as data nodes for communication between UAVs and AUVs, as well as collecting data from onboard sensors, while AUVs can sample the water column vertically. This allows more accurate estimation of phytoplankton biomass and productivity, and can be used in conjunction with UAV sampling to determine air-sea flux of gases (e.g. CO2, CH4, DMS) affecting carbon budgets and atmospheric composition. In particular we describe tests in July 2013 conducted off Sesimbra, Portugal in conjunction with the Portuguese Navy by the University of Porto and MBARI with the goal of tracking large fish in the upper water column with coordinated air/surface/underwater measurements. A thermal gradient was observed in the infrared by a low flying UAV, which was used to dispatch an AUV to obtain ground truth to demonstrate the event-response capabilities using such autonomous platforms. Additional field studies in the future will facilitate integration of multiple unmanned systems into research vessel operations. The strength of hardware and software tools described in this study is to permit fundamental oceanographic measurements of both ocean and atmosphere over temporal and spatial scales that have previously been problematic. The methods demonstrated are particularly suited to the study of oceanographic fronts and for tracking and mapping oil spills or plankton blooms. With the networked coordination of multiple autonomous systems, individual components may be changed out while ocean observations continue, allowing coarse to fine spatial studies of hydrographic features over temporal dimensions that would otherwise be difficult, including diurnal and tidal periods. Constraints on these methods currently involve coordination of data archiving systems into shipboard operating systems, familiarization of oceanographers with these methods, and existing nearshore airspace use constraints on UAVs. An important outcome of these efforts is to understand the methodology for using multiple heterogeneous autonomous vehicles for targeted science exploration.

Fugitive greenhouse gas emissions from shale gas activities - a case study of Dish, TX

NASA Astrophysics Data System (ADS)

Khan, A.; Roscoe, B.; Lary, D.; Schaefer, D.; Tao, L.; Sun, K.; Brian, A.; DiGangi, J.; Miller, D. J.; Zondlo, M. A.

2012-12-01

We evaluate new findings on aerial (horizontal and vertical) mapping of methane emissions in the atmospheric boundary layer region to help study fugitive methane emissions from extraction, transmission, and storage of natural gas and oil in Dish, Texas. Dish is located in the Barnett Shale which has seen explosive development of hydraulic fracking activities in recent years. The aerial measurements were performed with a new laser-based methane sensor developed specifically for an unmanned aerial vehicle (UAV). The vertical cavity surface emitting laser (VCSEL) methane sensor, with a mass of 2.5 kg and a precision of < 20 ppbv methane at 1 Hz, was flown on the UT-Dallas ARC Payload Master electronic aircraft at two sites in Texas: one representative of urban emissions of the Dallas-Fort Worth area in Richardson, Texas and another in Dish, Texas, closer to gas and oil activities. Methane mixing ratios at Dish were ubiquitously in the 3.5 - 4 ppmv range which was 1.5 - 2 ppmv higher than methane levels immediately downwind of Dallas. During the flight measurements at Dish, narrow methane plumes exceeding 20 ppmv were frequently observed at altitudes from the surface to 130 m above the ground. Based on the wind speed at the sampling location, the horizontal widths of large methane plumes were of the order of 100 m. The locations of the large methane plumes were variable in space and time over a ~ 1 km2 area sampled from the UAV. Spatial mapping over larger scales (10 km) by ground-based measurements showed similar methane levels as the UAV measurements. To corroborate our measurements, alkane and other hydrocarbon mixing ratios from an on-site TCEQ environmental monitoring station were analyzed and correlated with methane measurements to fingerprint the methane source. We show that fugitive methane emissions at Dish are a significant cause of the large and ubiquitous methane levels on the 1-10 km scale.

Toward a generic UGV autopilot

NASA Astrophysics Data System (ADS)

Moore, Kevin L.; Whitehorn, Mark; Weinstein, Alejandro J.; Xia, Junjun

2009-05-01

Much of the success of small unmanned air vehicles (UAVs) has arguably been due to the widespread availability of low-cost, portable autopilots. While the development of unmanned ground vehicles (UGVs) has led to significant achievements, as typified by recent grand challenge events, to date the UGV equivalent of the UAV autopilot is not available. In this paper we describe our recent research aimed at the development of a generic UGV autopilot. Assuming we are given a drive-by-wire vehicle that accepts as inputs steering, brake, and throttle commands, we present a system that adds sonar ranging sensors, GPS/IMU/odometry, stereo camera, and scanning laser sensors, together with a variety of interfacing and communication hardware. The system also includes a finite state machine-based software architecture as well as a graphical user interface for the operator control unit (OCU). Algorithms are presented that enable an end-to-end scenario whereby an operator can view stereo images as seen by the vehicle and can input GPS waypoints either from a map or in the vehicle's scene-view image, at which point the system uses the environmental sensors as inputs to a Kalman filter for pose estimation and then computes control actions to move through the waypoint list, while avoiding obstacles. The long-term goal of the research is a system that is generically applicable to any drive-by-wire unmanned ground vehicle.

Quality Analysis on 3d Buidling Models Reconstructed from Uav Imagery

NASA Astrophysics Data System (ADS)

Jarzabek-Rychard, M.; Karpina, M.

2016-06-01

Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component.

Sensor data fusion for textured reconstruction and virtual representation of alpine scenes

NASA Astrophysics Data System (ADS)

Häufel, Gisela; Bulatov, Dimitri; Solbrig, Peter

2017-10-01

The concept of remote sensing is to provide information about a wide-range area without making physical contact with this area. If, additionally to satellite imagery, images and videos taken by drones provide a more up-to-date data at a higher resolution, or accurate vector data is downloadable from the Internet, one speaks of sensor data fusion. The concept of sensor data fusion is relevant for many applications, such as virtual tourism, automatic navigation, hazard assessment, etc. In this work, we describe sensor data fusion aiming to create a semantic 3D model of an extremely interesting yet challenging dataset: An alpine region in Southern Germany. A particular challenge of this work is that rock faces including overhangs are present in the input airborne laser point cloud. The proposed procedure for identification and reconstruction of overhangs from point clouds comprises four steps: Point cloud preparation, filtering out vegetation, mesh generation and texturing. Further object types are extracted in several interesting subsections of the dataset: Building models with textures from UAV (Unmanned Aerial Vehicle) videos, hills reconstructed as generic surfaces and textured by the orthophoto, individual trees detected by the watershed algorithm, as well as the vector data for roads retrieved from openly available shapefiles and GPS-device tracks. We pursue geo-specific reconstruction by assigning texture and width to roads of several pre-determined types and modeling isolated trees and rocks using commercial software. For visualization and simulation of the area, we have chosen the simulation system Virtual Battlespace 3 (VBS3). It becomes clear that the proposed concept of sensor data fusion allows a coarse reconstruction of a large scene and, at the same time, an accurate and up-to-date representation of its relevant subsections, in which simulation can take place.

Comparison of a UAV-derived point-cloud to Lidar data at Haig Glacier, Alberta, Canada

NASA Astrophysics Data System (ADS)

Bash, E. A.; Moorman, B.; Montaghi, A.; Menounos, B.; Marshall, S. J.

2016-12-01

The use of unmanned aerial vehicles (UAVs) is expanding rapidly in glaciological research as a result of technological improvements that make UAVs a cost-effective solution for collecting high resolution datasets with relative ease. The cost and difficult access traditionally associated with performing fieldwork in glacial environments makes UAVs a particularly attractive tool. In the small, but growing, body of literature using UAVs in glaciology the accuracy of UAV data is tested through the comparison of a UAV-derived DEM to measured control points. A field campaign combining simultaneous lidar and UAV flights over Haig Glacier in April 2015, provided the unique opportunity to directly compare UAV data to lidar. The UAV was a six-propeller Mikrokopter carrying a Panasonic Lumix DMC-GF1 camera with a 12 Megapixel Live MOS sensor and Lumix G 20 mm lens flown at a height of 90 m, resulting in sub-centimetre ground resolution per image pixel. Lidar data collection took place April 20, while UAV flights were conducted April 20-21. A set of 65 control points were laid out and surveyed on the glacier surface on April 19 and 21 using a RTK GPS with a vertical uncertainty of 5 cm. A direct comparison of lidar points to these control points revealed a 9 cm offset between the control points and the lidar points on average, but the difference changed distinctly from points collected on April 19 versus those collected April 21 (7 cm and 12 cm). Agisoft Photoscan was used to create a point-cloud from imagery collected with the UAV and CloudCompare was used to calculate the difference between this and the lidar point cloud, revealing an average difference of less than 17 cm. This field campaign also highlighted some of the benefits and drawbacks of using a rotary UAV for glaciological research. The vertical takeoff and landing capabilities, combined with quick responsiveness and higher carrying capacity, make the rotary vehicle favourable for high-resolution photos when working in mountainous terrain. Battery life is limited, however, compared to fixed-wing vehicles, making it more difficult to cover large areas in a short time. This analysis shows that UAVs are able to fill an important role in the future of glaciological research, when research goals are balanced with instrument accuracy and UAV platform selection.

Electric Power System for High Altitude UAV Technology Survey

NASA Technical Reports Server (NTRS)

1997-01-01

Electric powertrain technologies with application to high altitude Unmanned Aerial Vehicles (UAV) are assessed. One hundred twenty five solar electric UAV configurations and missions were simulated. Synergistic design opportunities were investigated with the premise that specific benefits may be realized, for example, if a single component can serve multiple functions, such as a battery being used for energy storage as well as for a structural component of the aircraft. For each UAV mission simulation, the airframe structure, powertrain configuration (type of solar cells, energy storage options) and performance baseline (1997 or 2001) were specified. It has been found that the use of the high efficiency (multijunction) solar cells or the use of the synergistic amorphous silicon solar cell configuration yields aircraft that can accomplish the majority of the missions of interest for any latitude between 0 deg and 55 deg, hence, a single versatile aircraft can be constructed and implemented to accomplish these missions.

Imaging of earthquake faults using small UAVs as a pathfinder for air and space observations

USGS Publications Warehouse

Donnellan, Andrea; Green, Joseph; Ansar, Adnan; Aletky, Joseph; Glasscoe, Margaret; Ben-Zion, Yehuda; Arrowsmith, J. Ramón; DeLong, Stephen B.

2017-01-01

Large earthquakes cause billions of dollars in damage and extensive loss of life and property. Geodetic and topographic imaging provide measurements of transient and long-term crustal deformation needed to monitor fault zones and understand earthquakes. Earthquake-induced strain and rupture characteristics are expressed in topographic features imprinted on the landscapes of fault zones. Small UAVs provide an efficient and flexible means to collect multi-angle imagery to reconstruct fine scale fault zone topography and provide surrogate data to determine requirements for and to simulate future platforms for air- and space-based multi-angle imaging.

Wireless Sensor Buoys for Perimeter Security of Military Vessels and Seabases

DTIC Science & Technology

2015-12-01

however, additional sensors utilizing modern technology and a self -forming, as well as self - healing , network could further diminish attacks against...Space SSDF Ship’s Self -Defense Force UAV Unmanned Aerial Vehicle UGS Unattended Ground Sensor USB Universal Serial Bus USMC United States Marine...conditions may delay their reaction resulting in the attacking surface craft entering into a critically close position. The Ship’s Self -Defense

A Challenge for Micro and Mini UAV: The Sensor Problem

DTIC Science & Technology

2005-05-01

pressure airspeed sensors on one single circuit board (Figure 8). Figure 8: Autopilot. The Quadcopter The fourth and final MAV is a quad-copter with...UNCLASSIFIED/UNLIMITED Figure 9: Quadcopter MAV. Figure 10: Loopshaping Diagram. The IMU contains 3 MEMS gyros. These form the rotational sensors Gx...flapping wings) and even by insects (vibrating wings). Once in operation, they will be extremely discrete, making it very difficult to distinct

Uav-Based Detection of Unknown Radioactive Biomass Deposits in Chernobyl's Exclusion Zone

NASA Astrophysics Data System (ADS)

Briechle, S.; Sizov, A.; Tretyak, O.; Antropov, V.; Molitor, N.; Krzystek, P.

2018-05-01

Shortly after the explosion of the Chernobyl nuclear power plant (ChNPP) in 1986, radioactive fall-out and contaminated trees (socalled Red Forest) were buried in the Chernobyl Exclusion Zone (ChEZ). These days, exact locations of the buried contaminated material are needed. Moreover, 3D vegetation maps are necessary to simulate the impact of tornados and forest fire. After 30 years, some of the so-called trenches and clamps are visible. However, some of them are overgrown and have slightly settled in the centimeter and decimeter range. This paper presents a pipeline that comprises 3D vegetation mapping and machine learning methods to precisely map trenches and clamps from remote sensing data. The dataset for our experiments consists of UAV-based LiDAR data, multi-spectral data, and aerial gamma-spectrometry data. Depending on the study areas overall accuracies ranging from 95.6 % to 99.0 % were reached for the classification of radioactive deposits. Our first results demonstrate an accurate and reliable UAV-based detection of unknown radioactive biomass deposits in the ChEZ.

Benchmarking real-time RGBD odometry for light-duty UAVs

NASA Astrophysics Data System (ADS)

Willis, Andrew R.; Sahawneh, Laith R.; Brink, Kevin M.

2016-06-01

This article describes the theoretical and implementation challenges associated with generating 3D odometry estimates (delta-pose) from RGBD sensor data in real-time to facilitate navigation in cluttered indoor environments. The underlying odometry algorithm applies to general 6DoF motion; however, the computational platforms, trajectories, and scene content are motivated by their intended use on indoor, light-duty UAVs. Discussion outlines the overall software pipeline for sensor processing and details how algorithm choices for the underlying feature detection and correspondence computation impact the real-time performance and accuracy of the estimated odometry and associated covariance. This article also explores the consistency of odometry covariance estimates and the correlation between successive odometry estimates. The analysis is intended to provide users information needed to better leverage RGBD odometry within the constraints of their systems.

Calibration and Extension of a Discrete Event Operations Simulation Modeling Multiple Un-Manned Aerial Vehicles Controlled by a Single Operator

DTIC Science & Technology

2013-03-01

within systems of UAVs and between UAVs and the operators that use them. The next step for small UAVs in this direction is for one operator to be able...Team’s testing efforts, both in the planning and execution stages. The flight tests would never have taken place without the tremendous assistance...1 1.2 Unmanned Aerial Systems

UAV-based landslide deformation monitoring - first results from Corvara landslide

NASA Astrophysics Data System (ADS)

Thiebes, Benni; Tomelleri, Enrico; Mejia-Aguilar, Abraham; Schlögel, Romy; Darvishi, Mehdi; Remondino, Fabio; Toschi, Isabella; Rutzinger, Martin; Zieher, Thomas

2016-04-01

In recent years, unmanned aerial vehicles (UAVs) have been more frequently utilised to study geomorphological and natural hazard processes, including gravitational mass movements such as landslides. UAVs can be equipped with different sensors, e.g. photo cameras and laser scanners, and the data that can be achieved can substantially improve the monitoring and understanding of the involved natural processes. One of the main advantages of UAVs is their flexibility that allows for carrying out assessments of large areas in short periods of time and at much lower costs than other platforms, e.g. airplanes or helicopters. Thereby, UAVs represent an interesting technique to complement more traditional monitoring methods. Here we present some first results of the EUREGIO-funded LEMONADE project that is concerned with the combination and integration of novel and traditional landslide monitoring techniques. We carried out a series of UAV flights over a particularly active part of the Corvara landslide and acquired aerial imagery for quantitative assessments of the retrogressive enlargement of the landslide over recent years. Additional field surveys including terrestrial laser scanning, and UAV-based photogrammetry and laser scanning are scheduled for summer 2016. The Corvara landslide is a large complex earthflow in the Italian Dolomites that has been investigated by a wide range of methodologies over the past years. The landslide is characterised by movement patterns of greatly varying magnitude, ranging from annual rates of a few cm to more than 20 m. The current and past monitoring activities concentrated on GPS measurements as well as multi-temporal differential radar interferometry utilising artificial corner reflectors. Thereby, primarily punctual displacement data were achieved and spatial information on topographic and geomorphic changes were consequently sparse. For our photogrammetry study, we utilised a SoLeon octocopter equipped with a Ricoh GR 16.2 Megapixels camera. Three photos were taken with different exposure settings every 2 seconds while the UAV followed a pre-programmed flight track in an elevation of 70 m above ground and a flight speed of 1 m/s. Ground-control points were distributed to allow for reliable merging as well as for georeferencing of the resulting imagery. Within one day, approximately 13 ha were covered with an orthophoto and point cloud of extremely high spatial resolution. The point cloud consisting of more than 200 million points was transferred to a digital surface model (DSM) with 1.5 cm resolution, and was subsequently compared to the 2006 LiDAR based DSM. The comparison of the results highlights areas of vertical topographic changes which reached up to 12 m. Moreover, the retrogressive enlargement of the landslide could be quantified and partly exceeds 30 m within the past 4 years only.

Critical Zone Co-dynamics: Quantifying Interactions between Subsurface, Land Surface, and Vegetation Properties Using UAV and Geophysical Approaches

NASA Astrophysics Data System (ADS)

Dafflon, B.; Leger, E.; Peterson, J.; Falco, N.; Wainwright, H. M.; Wu, Y.; Tran, A. P.; Brodie, E.; Williams, K. H.; Versteeg, R.; Hubbard, S. S.

2017-12-01

Improving understanding and modelling of terrestrial systems requires advances in measuring and quantifying interactions among subsurface, land surface and vegetation processes over relevant spatiotemporal scales. Such advances are important to quantify natural and managed ecosystem behaviors, as well as to predict how watershed systems respond to increasingly frequent hydrological perturbations, such as droughts, floods and early snowmelt. Our study focuses on the joint use of UAV-based multi-spectral aerial imaging, ground-based geophysical tomographic monitoring (incl., electrical and electromagnetic imaging) and point-scale sensing (soil moisture sensors and soil sampling) to quantify interactions between above and below ground compartments of the East River Watershed in the Upper Colorado River Basin. We evaluate linkages between physical properties (incl. soil composition, soil electrical conductivity, soil water content), metrics extracted from digital surface and terrain elevation models (incl., slope, wetness index) and vegetation properties (incl., greenness, plant type) in a 500 x 500 m hillslope-floodplain subsystem of the watershed. Data integration and analysis is supported by numerical approaches that simulate the control of soil and geomorphic characteristic on hydrological processes. Results provide an unprecedented window into critical zone interactions, revealing significant below- and above-ground co-dynamics. Baseline geophysical datasets provide lithological structure along the hillslope, which includes a surface soil horizon, underlain by a saprolite layer and the fractured Mancos shale. Time-lapse geophysical data show very different moisture dynamics in various compartments and locations during the winter and growing season. Integration with aerial imaging reveals a significant linkage between plant growth and the subsurface wetness, soil characteristics and the topographic gradient. The obtained information about the organization and connectivity of the landscape is being transferred to larger regions using aerial imaging and will be used to constrain multi-scale, multi-physics hydro-biogeochemical simulations of the East River watershed response to hydrological perturbations.

UAV Swarm Operational Risk Assessment System

DTIC Science & Technology

2015-09-01

a SIPRNET connection. For practicality in development of this prototype, the interface was created using the MATLAB GUI language . By design, the use ...and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE September 2015 3...discrete-event simulation of UAV swarm attacks using ExtendSim, statistical analysis of the simulation data using Minitab, and a graphical user interface

Nonlinear automatic landing control of unmanned aerial vehicles on moving platforms via a 3D laser radar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hervas, Jaime Rubio; Tang, Hui; Reyhanoglu, Mahmut

2014-12-10

This paper presents a motion tracking and control system for automatically landing Unmanned Aerial Vehicles (UAVs) on an oscillating platform using Laser Radar (LADAR) observations. The system itself is assumed to be mounted on a ship deck. A full nonlinear mathematical model is first introduced for the UAV. The ship motion is characterized by a Fourier transform based method which includes a realistic characterization of the sea waves. LADAR observation models are introduced and an algorithm to process those observations for yielding the relative state between the vessel and the UAV is presented, from which the UAV's state relative tomore » an inertial frame can be obtained and used for feedback purposes. A sliding mode control algorithm is derived for tracking a landing trajectory defined by a set of desired waypoints. An extended Kalman filter (EKF) is proposed to account for process and observation noises in the design of a state estimator. The effectiveness of the control algorithm is illustrated through a simulation example.« less

Applications of UAVs for Remote Sensing of Critical Infrastructure

NASA Technical Reports Server (NTRS)

Wegener, Steve; Brass, James; Schoenung, Susan

2003-01-01

The surveillance of critical facilities and national infrastructure such as waterways, roadways, pipelines and utilities requires advanced technological tools to provide timely, up to date information on structure status and integrity. Unmanned Aerial Vehicles (UAVs) are uniquely suited for these tasks, having large payload and long duration capabilities. UAVs also have the capability to fly dangerous and dull missions, orbiting for 24 hours over a particular area or facility providing around the clock surveillance with no personnel onboard. New UAV platforms and systems are becoming available for commercial use. High altitude platforms are being tested for use in communications, remote sensing, agriculture, forestry and disaster management. New payloads are being built and demonstrated onboard the UAVs in support of these applications. Smaller, lighter, lower power consumption imaging systems are currently being tested over coffee fields to determine yield and over fires to detect fire fronts and hotspots. Communication systems that relay video, meteorological and chemical data via satellite to users on the ground in real-time have also been demonstrated. Interest in this technology for infrastructure characterization and mapping has increased dramatically in the past year. Many of the UAV technological developments required for resource and disaster monitoring are being used for the infrastructure and facility mapping activity. This paper documents the unique contributions from NASA;s Environmental Research Aircraft and Sensor Technology (ERAST) program to these applications. ERAST is a UAV technology development effort by a consortium of private aeronautical companies and NASA. Details of demonstrations of UAV capabilities currently underway are also presented.

Multi-temporal UAV-borne LiDAR point clouds for vegetation analysis - a case study

NASA Astrophysics Data System (ADS)

Mandlburger, Gottfried; Wieser, Martin; Hollaus, Markus; Pfennigbauer, Martin; Riegl, Ursula

2016-04-01

In the recent past the introduction of compact and lightweight LiDAR (Light Detection And Ranging) sensors together with progress in UAV (Unmanned Aerial Vehicle) technology allowed the integration of laser scanners on remotely piloted multicopter, helicopter-type and even fixed-wing platforms. The multi-target capabilities of state-of-the-art time-of-flight full-waveform laser sensors operated from low flying UAV-platforms has enabled capturing of the entire 3D structure of semi-transparent objects like deciduous forests under leaf-off conditions in unprecedented density and completeness. For such environments it has already been demonstrated that UAV-borne laser scanning combines the advantages of terrestrial laser scanning (high point density, short range) and airborne laser scanning (bird's eye perspective, homogeneous point distribution). Especially the oblique looking capabilities of scanners with a large field of view (>180°) enable capturing of vegetation from different sides resulting in a constantly high point density also in the sub canopy domain. Whereas the findings stated above were drawn based on a case study carried out in February 2015 with the Riegl VUX-1UAV laser scanner system mounted on a Riegl RiCopter octocopter UAV-platform over an alluvial forest at the Pielach River (Lower Austria), the site was captured a second time with the same sensor system and mission parameters at the end of the vegetation period on October 28th, 2015. The main goal of this experiment was to assess the impact of the late autumn foliage on the achievable 3D point density. Especially the entire understory vegetation and certain tree species (e.g. willow) were still in full leaf whereas the bigger trees (poplar) where already partly defoliated. The comparison revealed that, although both campaigns featured virtually the same laser shot count, the ground point density dropped from 517 points/m2 in February (leaf-off) to 267 points/m2 end of October (leaf-on). The decrease of ca. 50% is compensated by an increase in the upper canopy area (>20 m a.g.l.; Feb: 348 points/m2, Oct: 757 points/m2, increase rate: 118%). The greater leaf area in October results in more laser echoes from the canopy but the density decrease on the ground is not entirely attributed to shadowing from the upper canopy as the point distribution is nearly constant in the medium (10-20 m) and lower (0-10 m) sub-canopy area. The lower density on the ground is rather caused by a densely foliated shrub layer (0.15-3 m; Feb: 178 points/m2, Oct: 259 points/m2, increase rate: 46%). A sharp ground point density drop could be observed in areas covered by an invasive weed species (Fallopia japonica) which keeps its extremely dense foliage till late in the year. In summary, the preliminary point density study has shown the potential of UAV-borne, multi-temporal LiDAR for characterization of seasonal vegetation changes in deciduous environments. It is remarkable that even under leaf-on conditions a very high terrain point density is achievable. Except for the dense shrub layer, the case study has shown a similar 3D point distribution in the sub-canopy area for leaf-off and leaf-on data acquisition.

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers

PubMed Central

Olivares-Mendez, Miguel A.; Fu, Changhong; Ludivig, Philippe; Bissyandé, Tegawendé F.; Kannan, Somasundar; Zurad, Maciej; Annaiyan, Arun; Voos, Holger; Campoy, Pascual

2015-01-01

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing. PMID:26703597

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers.

PubMed

Olivares-Mendez, Miguel A; Fu, Changhong; Ludivig, Philippe; Bissyandé, Tegawendé F; Kannan, Somasundar; Zurad, Maciej; Annaiyan, Arun; Voos, Holger; Campoy, Pascual

2015-12-12

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.

Towards the Development of a Smart Flying Sensor: Illustration in the Field of Precision Agriculture.

PubMed

Hernandez, Andres; Murcia, Harold; Copot, Cosmin; De Keyser, Robin

2015-07-10

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology.

Towards the Development of a Smart Flying Sensor: Illustration in the Field of Precision Agriculture

PubMed Central

Hernandez, Andres; Murcia, Harold; Copot, Cosmin; De Keyser, Robin

2015-01-01

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology. PMID:26184205

Egnos-Based Multi-Sensor Accurate and Reliable Navigation in Search-And Missions with Uavs

NASA Astrophysics Data System (ADS)

Molina, P.; Colomina, I.; Vitoria, T.; Silva, P. F.; Stebler, Y.; Skaloud, J.; Kornus, W.; Prades, R.

2011-09-01

This paper will introduce and describe the goals, concept and overall approach of the European 7th Framework Programme's project named CLOSE-SEARCH, which stands for 'Accurate and safe EGNOS-SoL Navigation for UAV-based low-cost SAR operations'. The goal of CLOSE-SEARCH is to integrate in a helicopter-type unmanned aerial vehicle, a thermal imaging sensor and a multi-sensor navigation system (based on the use of a Barometric Altimeter (BA), a Magnetometer (MAGN), a Redundant Inertial Navigation System (RINS) and an EGNOS-enabled GNSS receiver) with an Autonomous Integrity Monitoring (AIM) capability, to support the search component of Search-And-Rescue operations in remote, difficult-to-access areas and/or in time critical situations. The proposed integration will result in a hardware and software prototype that will demonstrate an end-to-end functionality, that is to fly in patterns over a region of interest (possibly inaccessible) during day or night and also under adverse weather conditions and locate there disaster survivors or lost people through the detection of the body heat. This paper will identify the technical challenges of the proposed approach, from navigating with a BA/MAGN/RINS/GNSS-EGNOSbased integrated system to the interpretation of thermal images for person identification. Moreover, the AIM approach will be described together with the proposed integrity requirements. Finally, this paper will show some results obtained in the project during the first test campaign performed on November 2010. On that day, a prototype was flown in three different missions to assess its high-level performance and to observe some fundamental mission parameters as the optimal flying height and flying speed to enable body recognition. The second test campaign is scheduled for the end of 2011.

Exploratory use of a UAV platform for variety selection in peanut

NASA Astrophysics Data System (ADS)

Balota, Maria; Oakes, Joseph

2016-05-01

Variety choice is the most important production decision farmers make because high yielding varieties can increase profit with no additional production costs. Therefore, yield improvement has been the major objective for peanut (Arachis hypogaea L.) breeding programs worldwide, but the current breeding approach (selecting for yield under optimal production conditions) is slow and inconsistent with the needs derived from population demand and climate change. To improve the rate of genetic gain, breeders have used target physiological traits such as leaf chlorophyll content using SPAD chlorophyll meter, Normalized Difference Vegetation Index (NDVI) from canopy reflectance in visible and near infra-red (NIR) wavelength bands, and canopy temperature (CT) manually measured with infra-red (IR) thermometers at the canopy level; but its use for routine selection was hampered by the time required to walk hundreds of plots. Recent developments in remote sensing-based high throughput phenotyping platforms using unmanned aerial vehicles (UAV) have shown good potential for future breeding advancements. Recently, we initiated a study for the evaluation of suitability of digital imagery, NDVI, and CT taken from an UAV platform for peanut variety differentiation. Peanut is unique for setting its yield underground and resilience to drought and heat, for which yield is difficult to pre-harvest estimate; although the need for early yield estimation within the breeding programs exists. Twenty-six peanut cultivars and breeding lines were grown in replicated plots either optimally or deficiently irrigated under rain exclusion shelters at Suffolk, Virginia. At the beginning maturity growth stage, approximately a month before digging, NDVI and CT were taken with ground-based sensors at the same time with red, blue, green (RGB) images from a Sony camera mounted on an UAV platform. Disease ratings were also taken pre-harvest. Ground and UAV derived vegetation indices were analyzed for disease and yield prediction and further presented in this paper.

Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence.

PubMed

Sandino, Juan; Pegg, Geoff; Gonzalez, Felipe; Smith, Grant

2018-03-22

The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust ( Austropuccinia psidii ) on paperbark tea trees ( Melaleuca quinquenervia ) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools.

Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence

PubMed Central

2018-01-01

The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec® camera, orthorectified in Headwall SpectralView®, and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools. PMID:29565822

Generation and Assessment of High Resolution Digital Surface Model by Using Unmanned Air Vehicle Based Multicopter

NASA Astrophysics Data System (ADS)

Gülci, S.; Dindaroğlu, T.; Gündoğan, R.

2017-11-01

Unmanned air vehicle systems (UAVSs), which are presently defined as effective measuring instruments, can be used for measurements and evaluation studies in fields. Furthermore, UAVs are effective tools that can produce high-precision and resolution data for use in geographic information system-based work. This study examined a multicopter (hexacopter) as an air platform to seek opportunity in generating DSM with high resolution. Flights were performed in Kahramanmaras Sutcu Imam University Campus area in Turkey. Pre-assessment of field works, mission, tests and installation were prepared by using a Laptop with an adaptive ground control station. Hand remote controller unit was also linked and activated during flight to interfere with emergency situations. Canon model IXSUS 160 was preferred as sensor. As a result of this study, as mentioned previous studies, .The orthophotos can be produced by RGB (Red-green-blue) images obtained with UAV, herewith information on terrain topography, land cover and soil erosion can be evaluated.

Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensors

NASA Astrophysics Data System (ADS)

van der Meij, Bob; Kooistra, Lammert; Suomalainen, Juha; Barel, Janna M.; De Deyn, Gerlinde B.

2017-02-01

Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m-2, R2 = 0.80), N-content (RMSE = 1.94 g m-2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m-2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m-2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m-2, respectively), while the lowest values (76 cm, 0.41 g m-2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley.

PubMed

Kefauver, Shawn C; Vicente, Rubén; Vergara-Díaz, Omar; Fernandez-Gallego, Jose A; Kerfal, Samir; Lopez, Antonio; Melichar, James P E; Serret Molins, María D; Araus, José L

2017-01-01

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.

The payload bay in the nose of NASA's Altair unmanned aerial vehicle (UAV) will be able to carry up

NASA Technical Reports Server (NTRS)

2002-01-01

The payload bay in the nose of NASA's Altair unmanned aerial vehicle (UAV), shown here during final construction at General Atomics Aeronautical Systems, Inc., (GA-ASI) facility at Adelanto, Calif., will be able to carry up to 700 lbs. of sensors, imaging equipment and other instruments for Earth science missions. General Atomics Aeronautical Systems, Inc., is developing the Altair version of its Predator B unmanned reconnaissance aircraft under NASA's Environmental Research Aircraft and Sensor Technology (ERAST) project. NASA plans to use the Altair as a technology demonstrator to validate a variety of command and control technologies for UAVs, as well as demonstrate the capability to perform a variety of Earth science missions. The Altair is designed to carry an 700-lb. payload of scientific instruments and imaging equipment for as long as 32 hours at up to 52,000 feet altitude. Eleven-foot extensions have been added to each wing, giving the Altair an overall wingspan of 86 feet with an aspect ratio of 23. It is powered by a 700-hp. rear-mounted TPE-331-10 turboprop engine, driving a three-blade propeller. Altair is scheduled to begin flight tests in the fourth quarter of 2002, and be acquired by NASA following successful completion of basic airworthiness tests in early 2003 for evaluation of over-the-horizon control, detect, see and avoid and other technologies required to allow UAVs to operate safely with other aircraft in the national airspace.

a Redundant Gnss-Ins Low-Cost Uav Navigation Solution for Professional Applications

NASA Astrophysics Data System (ADS)

Navarro, J.; Parés, M. E.; Colomina, I.; Bianchi, G.; Pluchino, S.; Baddour, R.; Consoli, A.; Ayadi, J.; Gameiro, A.; Sekkas, O.; Tsetsos, V.; Gatsos, T.; Navoni, R.

2015-08-01

This paper presents the current results for the FP7 GINSEC project. Its goal is to build a pre-commercial prototype of a low-cost, accurate and reliable system for the professional UAV market. Low-cost, in this context, stands for the use of sensors in the most affordable segment of the market, especially MEMS IMUs and GNSS receivers. Reliability applies to the ability of the autopilot to cope with situations where unfavourable GNSS reception conditions or strong electromagnetic fields make the computation of the position and / or attitude of the UAV difficult. Professional and accurate mean that, at least using post-processing techniques as PPP, it will be possible to reach cm-level precisions that open the door to a range of applications demanding high levels of quality in positioning, as precision agriculture or mapping. To achieve such goal, a rigorous sensor error modelling approach, the use of redundant IMUs and a dual-GNSS receiver setup, together with close-coupling techniques and an extended Kalman filter with self-analysis capabilities have been used. Although the project is not yet complete, the results obtained up to now prove the feasibility of the aforementioned goal, especially in those aspects related to position determination. Research work is still undergoing to estimate the heading using a dual-GNNS receiver setup; preliminary results prove the validity of this approach for relatively long baselines, although positive results are expected when these are shorter than 1 m - which is a necessary requisite for small-sized UAVs.

Monitoring height and greenness of non-woody floodplain vegetation with UAV time series

NASA Astrophysics Data System (ADS)

van Iersel, Wimala; Straatsma, Menno; Addink, Elisabeth; Middelkoop, Hans

2018-07-01

Vegetation in river floodplains has important functions for biodiversity, but can also have a negative influence on flood safety. Floodplain vegetation is becoming increasingly heterogeneous in space and time as a result of river restoration projects. To document the spatio-temporal patterns of the floodplain vegetation, the need arises for efficient monitoring techniques. Monitoring is commonly performed by mapping floodplains based on single-epoch remote sensing data, thereby not considering seasonal dynamics of vegetation. The rising availability of unmanned airborne vehicles (UAV) increases monitoring frequency potential. Therefore, we aimed to evaluate the performance of multi-temporal high-spatial-resolution imagery, collected with a UAV, to record the dynamics in floodplain vegetation height and greenness over a growing season. Since the classification accuracy of current airborne surveys remains insufficient for low vegetation types, we focussed on seasonal variation of herbaceous and grassy vegetation with a height up to 3 m. Field reference data on vegetation height were collected six times during one year in 28 field plots within a single floodplain along the Waal River, the main distributary of the Rhine River in the Netherlands. Simultaneously with each field survey, we recorded UAV true-colour and false-colour imagery from which normalized digital surface models (nDSMs) and a consumer-grade camera vegetation index (CGCVI) were calculated. We observed that: (1) the accuracy of a UAV-derived digital terrain model (DTM) varies over the growing season and is most accurate during winter when the vegetation is dormant, (2) vegetation height can be determined from the nDSMs in leaf-on conditions via linear regression (RSME = 0.17-0.33 m), (3) the multitemporal nDSMs yielded meaningful temporal profiles of greenness and vegetation height and (4) herbaceous vegetation shows hysteresis for greenness and vegetation height, but no clear hysteresis was observed for grassland vegetation. These results show the high potential of using UAV-borne sensors for increasing the classification accuracy of low floodplain vegetation within the framework of floodplain monitoring.

NASA Earth Science Research and Applications Using UAVs

NASA Technical Reports Server (NTRS)

Guillory, Anthony R.

2003-01-01

The NASA Earth Science Enterprise sponsored the UAV Science Demonstration Project, which funded two projects: the Altus Cumulus Electrification Study (ACES) and the UAV Coffee Harvest Optimization experiment. These projects were intended to begin a process of integrating UAVs into the mainstream of NASA s airborne Earth Science Research and Applications programs. The Earth Science Enterprise is moving forward given the positive science results of these demonstration projects to incorporate more platforms with additional scientific utility into the program and to look toward a horizon where the current piloted aircraft may not be able to carry out the science objectives of a mission. Longer duration, extended range, slower aircraft speed, etc. all have scientific advantages in many of the disciplines within Earth Science. The challenge we now face are identifying those capabilities that exist and exploiting them while identifying the gaps. This challenge has two facets: the engineering aspects of redesigning or modifying sensors and a paradigm shift by the scientists.

Uncooled microbolometer sensors for unattended applications

NASA Astrophysics Data System (ADS)

Kohin, Margaret; Miller, James E.; Leary, Arthur R.; Backer, Brian S.; Swift, William; Aston, Peter

2003-09-01

BAE SYSTEMS has been developing and producing uncooled microbolometer sensors since 1995. Recently, uncooled sensors have been used on Pointer Unattended Aerial Vehicles and considered for several unattended sensor applications including DARPA Micro-Internetted Unattended Ground Sensors (MIUGS), Army Modular Acoustic Imaging Sensors (MAIS), and Redeployable Unattended Ground Sensors (R-UGS). This paper describes recent breakthrough uncooled sensor performance at BAE SYSTEMS and how this improved performance has been applied to a new Standard Camera Core (SCC) that is ideal for these unattended applications. Video imagery from a BAE SYSTEMS 640x480 imaging camera flown in a Pointer UAV is provided. Recent performance results are also provided.

Assessing UAVs in Monitoring Crop Evapotranspiration within a Heterogeneous Soil

NASA Astrophysics Data System (ADS)

Rouze, G.; Neely, H.; Morgan, C.; Kustas, W. P.; McKee, L.; Prueger, J. H.; Cope, D.; Yang, C.; Thomasson, A.; Jung, J.

2017-12-01

Airborne and satellite remote sensing methods have been developed to provide ET estimates across entire management fields. However, airborne-based ET is not particularly cost-effective and satellite-based ET provides insufficient spatial/temporal information. ET estimations through remote sensing are also problematic where soils are highly variable within a given management field. Unlike airborne/satellite-based ET, Unmanned Aerial Vehicle (UAV)-based ET has the potential to increase the spatial and temporal detail of these measurements, particularly within a heterogeneous soil landscape. However, it is unclear to what extent UAVs can model ET. The overall goal of this project was to assess the capability of UAVs in modeling ET across a heterogeneous landscape. Within a 20-ha irrigated cotton field in Central Texas, low-altitude UAV surveys were conducted throughout the growing season over two soil types. UAVs were equipped with thermal and multispectral cameras to obtain canopy temperature and NDVI, respectively. UAV data were supplemented simultaneously with ground-truth measurements such as Leaf Area Index (LAI) and plant height. Both remote sensing and ground-truth parameters were used to model ET using a Two-Source Energy Balance (TSEB) model. UAV-based estimations of ET and other energy balance components were validated against energy balance measurements obtained from nearby eddy covariance towers that were installed within each soil type. UAV-based ET fluxes were also compared with airborne and satellite (Landsat 8)-based ET fluxes collected near the time of the UAV survey.

Millimeter-wave micro-Doppler measurements of small UAVs

NASA Astrophysics Data System (ADS)

Rahman, Samiur; Robertson, Duncan A.

2017-05-01

This paper discusses the micro-Doppler signatures of small UAVs obtained from a millimeter-wave radar system. At first, simulation results are shown to demonstrate the theoretical concept. It is illustrated that whilst the propeller rotation rate of the small UAVs is quite high, millimeter-wave radar systems are capable of capturing the full micro-Doppler spread. Measurements of small UAVs have been performed with both CW and FMCW radars operating at 94 GHz. The CW radar was used for obtaining micro-Doppler signatures of individual propellers. The field test data of a flying small UAV was collected with the FMCW radar and was processed to extract micro-Doppler signatures. The high fidelity results clearly reveal features such as blade flashes and propeller rotation modulation lines which can be used to classify targets. This work confirms that millimeter-wave radar is suitable for the detection and classification of small UAVs at usefully long ranges.

Development of a Rotary Wing Unmanned Aerial Vehicle (UAV) Simulation Model

DTIC Science & Technology

2014-03-01

Features Language URL Autopilot: DIY UAV - 2 DOF proportional controller - Kalman filtering C http://autopilot.sour ceforge.net Paperazzi - 3 DOF...proprtional controller - Basic navigation OCaml http://paparazzi.ena c.fr JSBSim - Basic control system blockset - Sample autopilot

2015 USAFA Research Report: Discover Falcon Innovation

DTIC Science & Technology

2015-01-01

delivery system deployed from a canister. Their solution allows the canister to release hundreds of the sensors at the right angle and in waves so that...Computer Science at the Air Force Academy. The center develops sensors for the aircraft – it uses commercially available UAVs known as Haulers – to allow... sensors and software development, said Tim McCarthy, one of the co-founders of Aspect Robotics. During the last semester, Academy cadets in the

A Tutorial on Electro-Optical/Infrared (EO/IR) Theory and Systems

DTIC Science & Technology

2013-01-01

engine of a small UAV to an intercontinental ballistic missile (ICBM) launch. Comparison of the available energy at the sensor to the noise level...of the sensor provides the central metric of sensor performance, the noise equivalent irradiance or NEI. The problem of extracting the target from...effectiveness of imaging systems can be degraded by many factors, including limited contrast and luminance, the presence of noise , and blurring due to

Use of UAVs for Remote Measurement of Vegetation Canopy Variables

NASA Astrophysics Data System (ADS)

Rango, A.; Laliberte, A.; Herrick, J.; Steele, C.; Bestelmeyer, B.; Chopping, M. J.

2006-12-01

Remote sensing with different sensors has proven useful for measuring vegetation canopy variables at scales ranging from landscapes down to individual plants. For use at landscape scales, such as desert grasslands invaded by shrubs, it is possible to use multi-angle imagery from satellite sensors, such as MISR and CHRIS/Proba, with geometric optical models to retrieve fractional woody plant cover. Vegetation community states can be mapped using visible and near infrared ASTER imagery at 15 m resolution. At finer scales, QuickBird satellite imagery with approximately 60 cm resolution and piloted aircraft photography with 25-80 cm resolution can be used to measure shrubs above a critical size. Tests conducted with the QuickBird data in the Jornada basin of southern New Mexico have shown that 87% of all shrubs greater than 2 m2 were detected whereas only about 29% of all shrubs less than 2 m2 were detected, even at these high resolutions. Because there is an observational gap between satellite/aircraft measurements and ground observations, we have experimented with Unmanned Aerial Vehicles (UAVs) producing digital photography with approximately 5 cm resolution. We were able to detect all shrubs greater than 2 m2, and we were able to map small subshrubs indicative of rangeland deterioration, as well as remnant grass patches, for the first time. None of these could be identified on the 60 cm resolution data. Additionally, we were able to measure canopy gaps, shrub patterns, percent bare soil, and vegetation cover over mixed rangeland vegetation. This approach is directly applicable to rangeland health monitoring, and it provides a quantitative way to assess shrub invasion over time and to detect the depletion or recovery of grass patches. Further, if the UAV images have sufficient overlap, it may be possible to exploit the stereo viewing capabilities to develop a digital elevation model from the orthophotos, with a potential for extracting canopy height. We envision two parallel routes for investigation: one which emphasizes utilization of the most technically advanced passive and active space and aircraft sensors (e.g., LIDAR, radar, Hyperion, ASTER, QuickBird follow-on) for modeling research, and a second which emphasizes minimization of costs and maximization of simplicity for monitoring purposes utilizing inexpensive sensors such as digital cameras on UAVs for arid and semiarid rangelands. The use of UAVs will provide management agencies a way to assess various vegetation canopy variables for a very reasonable cost.

Employing UAVs to Acquire Detailed Vegetation and Bare Ground Data for Assessing Rangeland Health

NASA Astrophysics Data System (ADS)

Rango, A.; Laliberte, A.; Herrick, J. E.; Winters, C.

2007-12-01

Because of its value as a historical record (extending back to the mid 1930s), aerial photography is an important tool used in many rangeland studies. However, these historical photos are not very useful for detailed analysis of rangeland health because of inadequate spatial resolution and scheduling limitations. These issues are now being resolved by using Unmanned Aerial Vehicles (UAVs) over rangeland study areas. Spatial resolution improvements have been rapid in the last 10 years from the QuickBird satellite through improved aerial photography to the new UAV coverage and have utilized improved sensors and the more simplistic approach of low altitude flights. Our rangeland health experiments have shown that the low altitude UAV digital photography is preferred by rangeland scientists because it allows, for the first time, their identification of vegetation and land surface patterns and patches, gap sizes, bare soil percentages, and vegetation type. This hyperspatial imagery (imagery with a resolution finer than the object of interest) is obtained at about 5cm resolution by flying at an altitude of 150m above the surface of the Jornada Experimental Range in southern New Mexico. Additionally, the UAV provides improved temporal flexibility, such as flights immediately following fires, floods, and other catastrophic disturbances, because the flight capability is located near the study area and the vehicles are under the direct control of the users, eliminating the additional steps associated with budgets and contracts. There are significant challenges to improve the data to make them useful for operational agencies, namely, image distortion with inexpensive, consumer grade digital cameras, difficulty in detecting sufficient ground control points in small scenes (152m by 114m), accuracy of exterior UAV information on X,Y, Z, roll, pitch, and heading, the sheer number of images collected, and developing reliable relationships with ground-based data across a broad range of topographies and plant communities. Our efforts are currently focused on developing a complete and efficient workflow for UAV operational missions consisting of flight planning, image acquisition, image rectification and mosaicking, and image classification. The remote sensing capability is being incorporated into existing rangeland health assessment and monitoring protocols.

Autonomous soaring and surveillance in wind fields with an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Gao, Chen

Small unmanned aerial vehicles (UAVs) play an active role in developing a low-cost, low-altitude autonomous aerial surveillance platform. The success of the applications needs to address the challenge of limited on-board power plant that limits the endurance performance in surveillance mission. This thesis studies the mechanics of soaring flight, observed in nature where birds utilize various wind patterns to stay airborne without flapping their wings, and investigates its application to small UAVs in their surveillance missions. In a proposed integrated framework of soaring and surveillance, a bird-mimicking soaring maneuver extracts energy from surrounding wind environment that improves surveillance performance in terms of flight endurance, while the surveillance task not only covers the target area, but also detects energy sources within the area to allow for potential soaring flight. The interaction of soaring and surveillance further enables novel energy based, coverage optimal path planning. Two soaring and associated surveillance strategies are explored. In a so-called static soaring surveillance, the UAV identifies spatially-distributed thermal updrafts for soaring, while incremental surveillance is achieved through gliding flight to visit concentric expanding regions. A Gaussian-process-regression-based algorithm is developed to achieve computationally-efficient and smooth updraft estimation. In a so-called dynamic soaring surveillance, the UAV performs one cycle of dynamic soaring to harvest energy from the horizontal wind gradient to complete one surveillance task by visiting from one target to the next one. A Dubins-path-based trajectory planning approach is proposed to maximize wind energy extraction and ensure smooth transition between surveillance tasks. Finally, a nonlinear trajectory tracking controller is designed for a full six-degree-of-freedom nonlinear UAV dynamics model and extensive simulations are carried to demonstrate the effectiveness of the proposed soaring and surveillance strategies.

Uav for Geodata Acquisition in Agricultureal and Forestal Applications

NASA Astrophysics Data System (ADS)

Reidelstürz, P.; Schrenk, L.; Littmann, W.

2011-09-01

In the field of precision-farming research, solutions are worked out to combine ecological and economical requirements in a harmonic way. Integrating hightech in agricultural machinery, natural differences in the fields (biodiversity) can be detected and considered to economize agricultural resources and to give respect to natural ecological variability at the same time. Using precision farming resources, machining - and labour time can be economized, productivness can be improved, environmental burden can be discharged and documentation of production processes can be improved. To realize precision farming it is essential to make contemporary large scale data of the biodiversity in the field available. In the last years effectual traktor based equipment for real time precision farming applications was developed. Using remote sensing, biomass diversity of the field can be considered while applicating operating ressources economicly. Because these large scale data aquisition depends on expensive tractor based inspections, capable Unmanned Aerial Vehicles (UAVs) could complement or in special situations even replace such tractor based data aquisition needed for the realization of precision farming strategies. The specific advantages and application slots of UAVs seems to be ideal for the usage in the field of precision farming. For example the size of even large agricultural fields in germany can be managed even by smaller UAVs. Data can be captured spontaneously, promptly, in large scale, with less respect of weather conditions. In agricultural regions UAV flights can be arranged in visual range as actually the legislator requires in germany, especially because the use of autopilotsystems in fact is nessecary to assure regular area-wide data without gaps but not to fly in non-visible regions. Also a minimized risk of hazard is given, flying UAVs over deserted agricultural areas. In a first stage CIS GmbH cooperated with "Institute For Flightsystems" of the University of German Armed Forces in Neubiberg/Munich and the well-established precision farming company "Konsultationszentrum Liepen" to develop an applicable UAV for precision farming purposes. Currently Cis GmbH and Technologie Campus Freyung, with intense contact to the „flying robot"- team of DLR Oberpfaffenhofen, collaborate to optimize the existing UAV and to extend the applications from data aquisition for biomass diversity up to detect the water supply situation in agricultural fields, to support pest management systems as much as to check the possibilities to detect bark beetle attacks in european spruce in an early stage of attack (green attack phase) by constructing and integrating further payload modules with different sensors in the existing UAV airframe. Also effective data processing workflows are to be worked out. Actually in the existing UAV autopilotsystem "piccolo" (cloudcaptech) is integrated and also a replaceable payload module is available, carrying a VIS and a NIR camera to calculate maps of NDVI diversity as indicator of biomass diversity. Further modules with a 6 channel multispectral still camera and with a spectrometer are planned. The airframe's wingspan is about 3,45m weighting 4.2 kg, ready to fly. The hand launchable UAV can start from any place in agricultural regions. The wing is configured with flaps, allowing steep approaches and short landings using a „butterfly" brake configuration. In spite of the lightweight configuration the UAV yet proves its worth under windy baltic wether situations by collecting regular sharp images of fields under wind speed up to 15m/s (Beaufort 6 -7). In further projects the development of further payload modules and a user friendly flight planning tool is scheduled considering different payload - and airframe requirements for different precision farming purposes and forest applications. Data processing and workflow will be optimized. Cooperation with further partners to establish UAV systems in agricultural, forest and geodata aquisition is desired.

Experimental Identification and Characterization of Multirotor UAV Propulsion

NASA Astrophysics Data System (ADS)

Kotarski, Denis; Krznar, Matija; Piljek, Petar; Simunic, Nikola

2017-07-01

In this paper, an experimental procedure for the identification and characterization of multirotor Unmanned Aerial Vehicle (UAV) propulsion is presented. Propulsion configuration needs to be defined precisely in order to achieve required flight performance. Based on the accurate dynamic model and empirical measurements of multirotor propulsion physical parameters, it is possible to design diverse configurations with different characteristics for various purposes. As a case study, we investigated design considerations for a micro indoor multirotor which is suitable for control algorithm implementation in structured environment. It consists of open source autopilot, sensors for indoor flight, “take off the shelf” propulsion components and frame. The series of experiments were conducted to show the process of parameters identification and the procedure for analysis and propulsion characterization. Additionally, we explore battery performance in terms of mass and specific energy. Experimental results show identified and estimated propulsion parameters through which blade element theory is verified.

Development and application of a Controlled Release Facility (CRF) to validate flux quantifying methodologies.

NASA Astrophysics Data System (ADS)

Helmore, Jonathan

2017-04-01

The National Physical Laboratory, the UK's National Measurement Institute, has developed a novel facility capable of replicating the gaseous emission flux characteristics of a variety of real-word scenarios as may be found in small to medium scale industry and agriculture. The Controlled Release Facility (CRF) can be used to challenge conventional remote sensing techniques, as well as validate new Unmanned Aerial Vehicle (UAV) and distributed sensor network based methods, for source identification and flux calculation. The CRF method will be described and the results from three case studies will be discussed: The replication of an operational on-shore shale gas well using emissions of natural gas to atmosphere and measurements using Differential Absorption LIDAR (DIAL); the replication of fugitive volatile organic compounds emissions from a petrochemical unit and measurements using DIAL; and the replication of methane and carbon dioxide emissions from landfill and measurements using both fixed wing and multi-rotor UAVs.

Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle

DOE PAGES

Hruska, Ryan; Mitchell, Jessica; Anderson, Matthew; ...

2012-09-17

During the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral in-flight calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the U.S. Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis.more » The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 meters (based on RMSE).« less

Application of a Very-Low-Cost Unmanned Aerial Vehicle (UAV) and Consumer Grade Camera for the Collection of Research Grade Data: Preliminary Findings

NASA Astrophysics Data System (ADS)

Christian, P.; Davis, J. D.; Blesius, L.

2013-12-01

The use of UAV technology in the field of geoscience research has grown almost exponentially in the last decade. UAVs have been utilized as a sensor platform in many fields including geology, biology, climatology, geomorphology and archaeology. A UAV's ability to fly frequently, at very low altitude, and at relatively little cost makes them a perfect compromise between free, low temporal and spatial resolution satellite data and terrestrial based survey when there are insufficient funds to purchase custom satellite or manned aircraft data. Unfortunately, many available UAVs for research are still relatively expensive and often have predetermined imaging systems. However, the proliferation of hobbyist grade UAVs and consumer point and shoot cameras may provide many research projects with an alternative that is both cost-effective and efficient in data collection. This study therefore seeks to answer the question, can these very low cost, hobby-grade UAVs be used to produce research grade data. To achieve this end, in December of 2012 a small grant was obtained (<$6500) to set up a complete UAV system and to employ it in a diverse range of research. The system is comprised of a 3D Robotics hexacopter, Ardupilot automated flight hardware and software, spare parts and tool kit, two Canon point-and-shoot cameras including one modified for near infrared imagery, and a field laptop. To date, successful research flights have been flown for geomorphic research in degraded and restored montane meadows to study stream channel formation using both visible and near infrared imagery as well as for the creation of digital elevation models of large hillslope gullies using structure from motion (SFM). Other applications for the hexacopter, in progress or planned, include landslide monitoring, vegetation monitoring and mapping using the normalized difference vegetation index, archaeological survey, and bird nest identification on small rock islands. An analysis of the results produced so far indicates that this low-cost approach can be used to gather relevant research data but there are significant downsides to using equipment designed for hobbyists and the public rather than that which has been designed primarily for research. Specifically, the repurposing and maintenance of the low-cost equipment greatly increases the time needed before quality data can be obtained.

Impact of plant-soil feedback on plant traits at field scale, testing the use of UAV-based optical sensors

NASA Astrophysics Data System (ADS)

De Deyn, Gerlinde B.; van der Meij, Bob; Barel, Janna M.; Suomalainen, Juha; Kooistra, Lammert

2017-04-01

Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field scale quantification of PSF effects, yet field experiments are warranted to asses actual PSF effects under less controlled conditions. Here we used Unmanned Aerial Vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data was acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE= 5.12 cm, R2= 0.79), chlorophyll content (RMSE= 0.11 g m-2, R2= 0.80), N-content (RMSE= 1.94 g m-2, R2= 0.68), and fresh biomass (RMSE= 0.72 kg m-2, R2=0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m-2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m-2, respectively), while the lowest values (76 cm, 0.41 g m-2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

Remote inspection with multi-copters, radiological sensors and SLAM techniques

NASA Astrophysics Data System (ADS)

Carvalho, Henrique; Vale, Alberto; Marques, Rúben; Ventura, Rodrigo; Brouwer, Yoeri; Gonçalves, Bruno

2018-01-01

Activated material can be found in different scenarios, such as in nuclear reactor facilities or medical facilities (e.g. in positron emission tomography commonly known as PET scanning). In addition, there are unexpected scenarios resulting from possible accidents, or where dangerous material is hidden for terrorism attacks using nuclear weapons. Thus, a technological solution is important to cope with fast and reliable remote inspection. The multi-copter is a common type of Unmanned Aerial Vehicle (UAV) that provides the ability to perform a first radiological inspection in the described scenarios. The paper proposes a solution with a multi-copter equipped with on-board sensors to perform a 3D reconstruction and a radiological mapping of the scenario. A depth camera and a Geiger-Müler counter are the used sensors. The inspection is performed in two steps: i) a 3D reconstruction of the environment and ii) radiation activity inference to localise and quantify sources of radiation. Experimental results were achieved with real 3D data and simulated radiation activity. Experimental tests with real sources of radiation are planned in the next iteration of the work.

Evaluation of Rgb-Based Vegetation Indices from Uav Imagery to Estimate Forage Yield in Grassland

NASA Astrophysics Data System (ADS)

Lussem, U.; Bolten, A.; Gnyp, M. L.; Jasper, J.; Bareth, G.

2018-04-01

Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R2 values of 0.62.

Third-generation imaging sensor system concepts

NASA Astrophysics Data System (ADS)

Reago, Donald A.; Horn, Stuart B.; Campbell, James, Jr.; Vollmerhausen, Richard H.

1999-07-01

Second generation forward looking infrared sensors, based on either parallel scanning, long wave (8 - 12 um) time delay and integration HgCdTe detectors or mid wave (3 - 5 um), medium format staring (640 X 480 pixels) InSb detectors, are being fielded. The science and technology community is now turning its attention toward the definition of a future third generation of FLIR sensors, based on emerging research and development efforts. Modeled third generation sensor performance demonstrates a significant improvement in performance over second generation, resulting in enhanced lethality and survivability on the future battlefield. In this paper we present the current thinking on what third generation sensors systems will be and the resulting requirements for third generation focal plane array detectors. Three classes of sensors have been identified. The high performance sensor will contain a megapixel or larger array with at least two colors. Higher operating temperatures will also be the goal here so that power and weight can be reduced. A high performance uncooled sensor is also envisioned that will perform somewhere between first and second generation cooled detectors, but at significantly lower cost, weight, and power. The final third generation sensor is a very low cost micro sensor. This sensor can open up a whole new IR market because of its small size, weight, and cost. Future unattended throwaway sensors, micro UAVs, and helmet mounted IR cameras will be the result of this new class.

UAV Communication Management and Coordination for Multitarget Tracking

DTIC Science & Technology

2009-02-26

6.3 Weighted Trace Penalty 16 6.4 Results with WTP for ECTG 17 7 Multiple UAV Case 20 7.1 Extension of WTP 20 7.2 Coordinated sensor motion...growth by a weighted trace penalty ( WTP ) term, which is a product of the current covariancc trace and the minimum distance to observability (MDO) for a...Specifically, the terminal cost or ECTG term using the WTP has the form J(b) = JWTP(b) := iD(s, e) Tfc P\\ (6.1) where 7 is a positive constant, i is the

New generation of content addressable memories for associative processing

NASA Astrophysics Data System (ADS)

Lewis, H. G., Jr.; Giambalov, Paul

2000-05-01

Content addressable memories (CAMS) store both key and association data. A key is presented to the CAN when it is searched and all of the addresses are scanned in parallel to find the address referenced by the key. When a match occurs, the corresponding association is returned. With the explosion of telecommunications packet switching protocols, large data base servers, routers and search engines a new generation of dense sub-micron high throughput CAMS has been developed. The introduction of this paper presents a brief history and tutorial on CAMS, their many uses and advantages, and describes the architecture and functionality of several of MUSIC Semiconductors CAM devices. In subsequent sections of the paper we address using Associative Processing to accommodate the continued increase in sensor resolution, number of spectral bands, required coverage, the desire to implement real-time target cueing, and the data flow and image processing required for optimum performance of reconnaissance and surveillance Unmanned Aerial Vehicles (UAVs). To be competitive the system designer must provide the most computational power, per watt, per dollar, per cubic inch, within the boundaries of cost effective UAV environmental control systems. To address these problems we demonstrate leveraging DARPA and DoD funded Commercial Off-the-Shelf technology to integrate CAM based Associative Processing into a real-time heterogenous multiprocessing system for UAVs and other platforms with limited weight, volume and power budgets.

Exploring the Integration of COSYSMO with a Model-Based Systems Engineering Methodology in Early Trade Space Analytics and Decisions

DTIC Science & Technology

2016-06-01

UAV system but had a larger scope. The publishing timeline for the second UAV model and this thesis ...r l o g i c a l ) t h a t can be used t o d e s c r i b e a p a r t o f t h e model . < / d e s c r i p t i o n > 7 <h idden> f a l s e < / h idden...July). Program manager e -tool kit: Hierarchy of models and simulations . [Online]. Available: https://acc.dau.mil/CommunityBrowser.aspx?id= 294530

Remote Sensing Systems to Detect and Analyze Oil Spills on the U.S. Outer Continental Shelf - A State of the Art Assessment

DTIC Science & Technology

2016-08-18

multi- sensor remote sensing approach to describe the distribution of oil from the DWH spill. They used airborne and satellite , multi- and hyperspectral...Experimental Sensors e.g., Acoustic and Nuclear Magnetic Resonance (NMR) (Fingas and Brown, 2012; Puestow et al., 2013). These are further...ship, aerial - aircraft, aerostat or UAV, or satellite ), among other classification criteria. A comprehensive review of sensor categories employed

10000 pixels wide CMOS frame imager for earth observation from a HALE UAV

NASA Astrophysics Data System (ADS)

Delauré, B.; Livens, S.; Everaerts, J.; Kleihorst, R.; Schippers, Gert; de Wit, Yannick; Compiet, John; Banachowicz, Bartosz

2009-09-01

MEDUSA is the lightweight high resolution camera, designed to be operated from a solar-powered Unmanned Aerial Vehicle (UAV) flying at stratospheric altitudes. The instrument is a technology demonstrator within the Pegasus program and targets applications such as crisis management and cartography. A special wide swath CMOS imager has been developed by Cypress Semiconductor Cooperation Belgium to meet the specific sensor requirements of MEDUSA. The CMOS sensor has a stitched design comprising a panchromatic and color sensor on the same die. Each sensor consists of 10000*1200 square pixels (5.5μm size, novel 6T architecture) with micro-lenses. The exposure is performed by means of a high efficiency snapshot shutter. The sensor is able to operate at a rate of 30fps in full frame readout. Due to a novel pixel design, the sensor has low dark leakage of the memory elements (PSNL) and low parasitic light sensitivity (PLS). Still it maintains a relative high QE (Quantum efficiency) and a FF (fill factor) of over 65%. It features an MTF (Modulation Transfer Function) higher than 60% at Nyquist frequency in both X and Y directions The measured optical/electrical crosstalk (expressed as MTF) of this 5.5um pixel is state-of-the art. These properties makes it possible to acquire sharp images also in low-light conditions.

Uav Photogrammetric Solution Using a Raspberry pi Camera Module and Smart Devices: Test and Results

NASA Astrophysics Data System (ADS)

Piras, M.; Grasso, N.; Jabbar, A. Abdul

2017-08-01

Nowadays, smart technologies are an important part of our action and life, both in indoor and outdoor environment. There are several smart devices very friendly to be setting, where they can be integrated and embedded with other sensors, having a very low cost. Raspberry allows to install an internal camera called Raspberry Pi Camera Module, both in RGB band and NIR band. The advantage of this system is the limited cost (< 60 euro), their light weight and their simplicity to be used and embedded. This paper will describe a research where a Raspberry Pi with the Camera Module was installed onto a UAV hexacopter based on arducopter system, with purpose to collect pictures for photogrammetry issue. Firstly, the system was tested with aim to verify the performance of RPi camera in terms of frame per second/resolution and the power requirement. Moreover, a GNSS receiver Ublox M8T was installed and connected to the Raspberry platform in order to collect real time position and the raw data, for data processing and to define the time reference. IMU was also tested to see the impact of UAV rotors noise on different sensors like accelerometer, Gyroscope and Magnetometer. A comparison of the achieved results (accuracy) on some check points of the point clouds obtained by the camera will be reported as well in order to analyse in deeper the main discrepancy on the generated point cloud and the potentiality of these proposed approach. In this contribute, the assembling of the system is described, in particular the dataset acquired and the results carried out will be analysed.

Development of a UAV system for VNIR-TIR acquisitions in precision agriculture

NASA Astrophysics Data System (ADS)

Misopolinos, L.; Zalidis, Ch.; Liakopoulos, V.; Stavridou, D.; Katsigiannis, P.; Alexandridis, T. K.; Zalidis, G.

2015-06-01

Adoption of precision agriculture techniques requires the development of specialized tools that provide spatially distributed information. Both flying platforms and airborne sensors are being continuously evolved to cover the needs of plant and soil sensing at affordable costs. Due to restrictions in payload, flying platforms are usually limited to carry a single sensor on board. The aim of this work is to present the development of a vertical take-off and landing autonomous unmanned aerial vehicle (VTOL UAV) system for the simultaneous acquisition of high resolution vertical images at the visible, near infrared (VNIR) and thermal infrared (TIR) wavelengths. A system was developed that has the ability to trigger two cameras simultaneously with a fully automated process and no pilot intervention. A commercial unmanned hexacopter UAV platform was optimized to increase reliability, ease of operation and automation. The designed systems communication platform is based on a reduced instruction set computing (RISC) processor running Linux OS with custom developed drivers in an efficient way, while keeping the cost and weight to a minimum. Special software was also developed for the automated image capture, data processing and on board data and metadata storage. The system was tested over a kiwifruit field in northern Greece, at flying heights of 70 and 100m above the ground. The acquired images were mosaicked and geo-corrected. Images from both flying heights were of good quality and revealed unprecedented detail within the field. The normalized difference vegetation index (NDVI) was calculated along with the thermal image in order to provide information on the accurate location of stressors and other parameters related to the crop productivity. Compared to other available sources of data, this system can provide low cost, high resolution and easily repeatable information to cover the requirements of precision agriculture.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

PubMed Central

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-01-01

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084

Distributed pheromone-based swarming control of unmanned air and ground vehicles for RSTA

NASA Astrophysics Data System (ADS)

Sauter, John A.; Mathews, Robert S.; Yinger, Andrew; Robinson, Joshua S.; Moody, John; Riddle, Stephanie

2008-04-01

The use of unmanned vehicles in Reconnaissance, Surveillance, and Target Acquisition (RSTA) applications has received considerable attention recently. Cooperating land and air vehicles can support multiple sensor modalities providing pervasive and ubiquitous broad area sensor coverage. However coordination of multiple air and land vehicles serving different mission objectives in a dynamic and complex environment is a challenging problem. Swarm intelligence algorithms, inspired by the mechanisms used in natural systems to coordinate the activities of many entities provide a promising alternative to traditional command and control approaches. This paper describes recent advances in a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of swarming unmanned systems. The results of a recent demonstration at NASA's Wallops Island of multiple Aerosonde Unmanned Air Vehicles (UAVs) and Pioneer Unmanned Ground Vehicles (UGVs) cooperating in a coordinated RSTA application are discussed. The vehicles were autonomously controlled by the onboard digital pheromone responding to the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm self-organized to perform total area surveillance, automatic target detection, sensor cueing, and automatic target recognition with no central processing or control and minimal operator input. Complete autonomy adds several safety and fault tolerance requirements which were integrated into the basic pheromone framework. The adaptive algorithms demonstrated the ability to handle some unplanned hardware failures during the demonstration without any human intervention. The paper describes lessons learned and the next steps for this promising technology.

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

Integration and flight test of a biomimetic heading sensor

NASA Astrophysics Data System (ADS)

Chahl, Javaan; Mizutani, Akiko

2013-04-01

We report on the first successful development and implementation of an automatic polarisation compass as the primary heading sensor for a UAV. Polarisation compassing is the primary navigation sense of many flying and walking insects, including bees, ants and crickets. Manually operated polarisation astrolabes were fitted in some passenger airliners prior to the implementation of the global positioning system, to compensate for the overal degradation of magnetic and gyrocompass sensors in polar regions. The device we developed demonstrated accurate determination of the direction of the Sun, with repeatability of better than 0.2 degrees. These figures are comparable to any solid state magnetic compass, including flux gate based devices. Flight trials were undertaken in which the output of the polarimeter was the only heading reference used by the aircraft as it flew through GPS waypoints.

The Littoral Combat Ship (LCS) Surface Warfare (SUW) Module: Determining the Surface-To-Surface Missile and Air-To-Surface Missile Mix

DTIC Science & Technology

2010-09-01

agent-based modeling platform known as MANA. The simulation is exercised over a broad range of different weapon systems types with their capabilities...Navy B.A., University of Florida, 2004 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN MODELING ...aerial vehicle (UAV) will have. This study uses freely available data to build a simulation utilizing an agent-based modeling platform known as MANA

Test and Evaluation of the Man-Machine Interface between the Apache Longbow and an Unmanned Aerial Vehicle

DTIC Science & Technology

2000-04-01

two week test was a part of an The Boeing Company is studying a concept that on- going Boeing internal research and development involves teaming a...study and effectiveness of attack/reconnaissance teams. A assessment of employment modes and their major concern is the level of crew interaction...Based on the UAV control mode, these controls will Test subjects received training concerning the operate either the TADS sensors (control mode mne

X-Band Radar for Studies of Tropical Storms from High Altitude UAV Platform

NASA Technical Reports Server (NTRS)

Rodriquez, Shannon; Heymsfield, Gerald; Li, Lihua; Bradley, Damon

2007-01-01

The increased role of unmanned aerial vehicles (UAV) in NASA's suborbital program has created a strong interest in the development of instruments with new capabilities, more compact sizes and reduced weights than the instruments currently operated on manned aircrafts. There is a strong demand and tremendous potential for using high altitude UAV (HUAV) to carry weather radars for measurements of reflectivity and wind fields from tropical storms. Tropical storm genesis frequently occurs in ocean regions that are inaccessible to piloted aircraft due to the long off shore range and the required periods of time to gather significant data. Important factors of interest for the study of hurricane genesis include surface winds, profiled winds, sea surface temperatures, precipitation, and boundary layer conditions. Current satellite precipitation and surface wind sensors have resolutions that are too large and revisit times that are too infrequent to study this problem. Furthermore, none of the spaceborne sensors measure winds within the storm itself. A dual beam X-band Doppler radar, UAV Radar (URAD), is under development at the NASA Goddard Space Flight Center for the study of tropical storms from HUAV platforms, such as a Global Hawk. X-band is the most desirable frequency for airborne weather radars since these can be built in a relatively compact size using off-the-shelf components which cost significantly less than other higher frequency radars. Furthermore, X-band radars provide good sensitivity with tolerable attenuation in storms. The low-cost and light-weight URAD will provide new capabilities for studying hurricane genesis by analyzing the vertical structure of tropical cyclones as well as 3D reflectivity and wind fields in clouds. It will enable us to measure both the 3D precipitation structure and surface winds by using two antenna beams: fixed nadir and conical scanning each produced by its associated subsystem. The nadir subsystem is a magnetron based radar modified from a marine radar transceiver. It is capable of measuring vertical reflectivity and velocity profile while being a lower-cost, smaller size, and lighter weight version of the NASA ER-2 Doppler Radar (EDOP), which has flown during many NASA field campaigns and has provided valuable scientific information on hurricanes and weather phenomena. Unfortunately, EDOP is too large and heavy for most UAV platforms, but the experience gained with this instrument provided us with the heritage to build a new low-cost, light-weight, smaller system that will be capable of flying on UAVs. The scanning subsystem uses a TWT transmitter and provides measurements of 3D reflectivity/wind fields in-clouds. Conical scanning of the radar beam at a 35 deg. incidence angle will also provide information of surface wind speed and direction derived from the surface return over a single 360 deg. sweep. URAD data system will be Linux based with the capability of autonomous operation. It will utilize cutting edge digital receiver and FPGA technologies to carry out the data acquisition and processing tasks. High speed navigation data from the aircraft will also be captured and saved along with radar data for 3D measurement field reconstruction and aircraft motion correction. There is a tremendous potential for UAVs to carry down-looking weather radars for measurements of reflectivity, horizontal and vertical winds from tropical storms. With operation from HUAV platforms, the dual beam X-band radar under development promises to provide greatly needed information for tropical storm research.

Optimal design of UAV's pod shape

NASA Astrophysics Data System (ADS)

Wei, Qun; Jia, Hong-guang

2011-08-01

In the modern war, UAV(unmanned aircraft system) plays a more and more important role in the army. UAVs always carry electrical-optical reconnaissance systems. These systems are used to accomplish the missions of observing and reconnaissance the battlefield. For traditional UAV, the shape of the pod on UAV is sphericity. In addition, the pod of UAV not only has the job of observing and reconnaissance the battlefield, but its shape also has impact on the UAV's drag when it flies in the air. In this paper, two different kinds of pod models are set up, one is the traditional sphericity model, the other is a new model. Unstructured grid is used on the flow field. Using CFD(computational fluid dynamic) method, the results of the drags of the different kinds of pod are got. The drag's relationship between the pod and the UAV is obtained by comparing the results of simulations. After analyzing the results we can get: when UAV flies at low speed(0.3Ma{0.7Ma), the drag's difference between the two kinds of pod is little, the pod's drag takes a small part of the UAV's whole drag which is only about 14%. At transonic speed(0.8Ma{1.2Ma), the drag's difference between these two kinds of pod is getting bigger and bigger along with the speed goes higher. The traditional pod's drag is 1/3 of the UAV's whole drag value, but for the new pod, it is only 1/5. At supersonic speed(1.3Ma{2.0Ma), the traditional pod's drag goes up rapidly, but the new kind of pod's drag goes up slowly. This makes the difference between the two kinds of UAVs' total drag comes greater. For example, at 2Ma, the total drag of new UAV is only 2/3 of the traditional UAV. These results show: when the UAV flies at low speed, these two kinds of pod have little difference in drag. But if it flies at supersonic speed, the pod has great impact on the UAV's total drag, so the designer of UAV's pod should pay more attention on the out shape.

Simulation to Flight Test for a UAV Controls Testbed

NASA Technical Reports Server (NTRS)

Motter, Mark A.; Logan, Michael J.; French, Michael L.; Guerreiro, Nelson M.

2006-01-01

The NASA Flying Controls Testbed (FLiC) is a relatively small and inexpensive unmanned aerial vehicle developed specifically to test highly experimental flight control approaches. The most recent version of the FLiC is configured with 16 independent aileron segments, supports the implementation of C-coded experimental controllers, and is capable of fully autonomous flight from takeoff roll to landing, including flight test maneuvers. The test vehicle is basically a modified Army target drone, AN/FQM-117B, developed as part of a collaboration between the Aviation Applied Technology Directorate (AATD) at Fort Eustis, Virginia and NASA Langley Research Center. Several vehicles have been constructed and collectively have flown over 600 successful test flights, including a fully autonomous demonstration at the Association of Unmanned Vehicle Systems International (AUVSI) UAV Demo 2005. Simulations based on wind tunnel data are being used to further develop advanced controllers for implementation and flight test.

Proteus in flight over Southern California

NASA Image and Video Library

2003-03-27

Scaled Composites' unique tandem-wing Proteus was the testbed for a series of UAV collision-avoidance flight demonstrations. An Amphitech 35GHz radar unit installed below Proteus' nose was the primary sensor for the Detect, See and Avoid tests.

Proteus front view in flight

NASA Image and Video Library

2003-03-27

Scaled Composites' unique tandem-wing Proteus was the testbed for a series of UAV collision-avoidance flight demonstrations. An Amphitech 35GHz radar unit installed below Proteus' nose was the primary sensor for the Detect, See and Avoid tests.