Robotic platform for traveling on vertical piping network

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

Nance, Thomas A; Vrettos, Nick J; Krementz, Daniel

This invention relates generally to robotic systems and is specifically designed for a robotic system that can navigate vertical pipes within a waste tank or similar environment. The robotic system allows a process for sampling, cleaning, inspecting and removing waste around vertical pipes by supplying a robotic platform that uses the vertical pipes to support and navigate the platform above waste material contained in the tank.

Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics

PubMed Central

Burms, Jeroen; Caluwaerts, Ken; Dambre, Joni

2015-01-01

In embodied computation (or morphological computation), part of the complexity of motor control is offloaded to the body dynamics. We demonstrate that a simple Hebbian-like learning rule can be used to train systems with (partial) embodiment, and can be extended outside of the scope of traditional neural networks. To this end, we apply the learning rule to optimize the connection weights of recurrent neural networks with different topologies and for various tasks. We then apply this learning rule to a simulated compliant tensegrity robot by optimizing static feedback controllers that directly exploit the dynamics of the robot body. This leads to partially embodied controllers, i.e., hybrid controllers that naturally integrate the computations that are performed by the robot body into a neural network architecture. Our results demonstrate the universal applicability of reward-modulated Hebbian learning. Furthermore, they demonstrate the robustness of systems trained with the learning rule. This study strengthens our belief that compliant robots should or can be seen as computational units, instead of dumb hardware that needs a complex controller. This link between compliant robotics and neural networks is also the main reason for our search for simple universal learning rules for both neural networks and robotics. PMID:26347645

Formation Control over Delayed Communication Network

NASA Astrophysics Data System (ADS)

Secchi, Cristian; Fantuzzi, Cesare

In this Chapter we address the problem of formation control of a group of robots that exchange information over a communication network characterized by a non negligible delay. We consider the Virtual Body Artificial Potential approach for stabilizing a group of robots at a desired formation. We show that it is possible to model the controlled group of robots as a port-Hamiltonian system and we exploit the scattering framework to achieve a passive behavior of the controlled system and to stabilize the robots in the desired formation independently of any communication delay.

Advanced wireless mobile collaborative sensing network for tactical and strategic missions

NASA Astrophysics Data System (ADS)

Xu, Hao

2017-05-01

In this paper, an advanced wireless mobile collaborative sensing network will be developed. Through properly combining wireless sensor network, emerging mobile robots and multi-antenna sensing/communication techniques, we could demonstrate superiority of developed sensing network. To be concrete, heterogeneous mobile robots including unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) are equipped with multi-model sensors and wireless transceiver antennas. Through real-time collaborative formation control, multiple mobile robots can team the best formation that can provide most accurate sensing results. Also, formatting multiple mobile robots can also construct a multiple-input multiple-output (MIMO) communication system that can provide a reliable and high performance communication network.

Toward controlling perturbations in robotic sensor networks

NASA Astrophysics Data System (ADS)

Banerjee, Ashis G.; Majumder, Saikat R.

2014-06-01

Robotic sensor networks (RSNs), which consist of networks of sensors placed on mobile robots, are being increasingly used for environment monitoring applications. In particular, a lot of work has been done on simultaneous localization and mapping of the robots, and optimal sensor placement for environment state estimation1. The deployment of RSNs, however, remains challenging in harsh environments where the RSNs have to deal with significant perturbations in the forms of wind gusts, turbulent water flows, sand storms, or blizzards that disrupt inter-robot communication and individual robot stability. Hence, there is a need to be able to control such perturbations and bring the networks to desirable states with stable nodes (robots) and minimal operational performance (environment sensing). Recent work has demonstrated the feasibility of controlling the non-linear dynamics in other communication networks like emergency management systems and power grids by introducing compensatory perturbations to restore network stability and operation2. In this paper, we develop a computational framework to investigate the usefulness of this approach for RSNs in marine environments. Preliminary analysis shows promising performance and identifies bounds on the original perturbations within which it is possible to control the networks.

Computer graphics testbed to simulate and test vision systems for space applications

NASA Technical Reports Server (NTRS)

Cheatham, John B.

1991-01-01

Artificial intelligence concepts are applied to robotics. Artificial neural networks, expert systems and laser imaging techniques for autonomous space robots are being studied. A computer graphics laser range finder simulator developed by Wu has been used by Weiland and Norwood to study use of artificial neural networks for path planning and obstacle avoidance. Interest is expressed in applications of CLIPS, NETS, and Fuzzy Control. These applications are applied to robot navigation.

Real-time networked control of an industrial robot manipulator via discrete-time second-order sliding modes

NASA Astrophysics Data System (ADS)

Massimiliano Capisani, Luca; Facchinetti, Tullio; Ferrara, Antonella

2010-08-01

This article presents the networked control of a robotic anthropomorphic manipulator based on a second-order sliding mode technique, where the control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the Soft Hard Real-Time Kernel (S.Ha.R.K.) real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using, to perform the experiments, a COMAU SMART3-S2 industrial robot, and the results are analysed to put into evidence the robustness of the proposed approach against possible network delays, packet losses and unmodelled effects.

A cost-effective intelligent robotic system with dual-arm dexterous coordination and real-time vision

NASA Technical Reports Server (NTRS)

Marzwell, Neville I.; Chen, Alexander Y. K.

1991-01-01

Dexterous coordination of manipulators based on the use of redundant degrees of freedom, multiple sensors, and built-in robot intelligence represents a critical breakthrough in development of advanced manufacturing technology. A cost-effective approach for achieving this new generation of robotics has been made possible by the unprecedented growth of the latest microcomputer and network systems. The resulting flexible automation offers the opportunity to improve the product quality, increase the reliability of the manufacturing process, and augment the production procedures for optimizing the utilization of the robotic system. Moreover, the Advanced Robotic System (ARS) is modular in design and can be upgraded by closely following technological advancements as they occur in various fields. This approach to manufacturing automation enhances the financial justification and ensures the long-term profitability and most efficient implementation of robotic technology. The new system also addresses a broad spectrum of manufacturing demand and has the potential to address both complex jobs as well as highly labor-intensive tasks. The ARS prototype employs the decomposed optimization technique in spatial planning. This technique is implemented to the framework of the sensor-actuator network to establish the general-purpose geometric reasoning system. The development computer system is a multiple microcomputer network system, which provides the architecture for executing the modular network computing algorithms. The knowledge-based approach used in both the robot vision subsystem and the manipulation control subsystems results in the real-time image processing vision-based capability. The vision-based task environment analysis capability and the responsive motion capability are under the command of the local intelligence centers. An array of ultrasonic, proximity, and optoelectronic sensors is used for path planning. The ARS currently has 18 degrees of freedom made up by two articulated arms, one movable robot head, and two charged coupled device (CCD) cameras for producing the stereoscopic views, and articulated cylindrical-type lower body, and an optional mobile base. A functional prototype is demonstrated.

An architectural approach to create self organizing control systems for practical autonomous robots

NASA Technical Reports Server (NTRS)

Greiner, Helen

1991-01-01

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive surgery.

PubMed

Sang, Hongqiang; Yang, Chenghao; Liu, Fen; Yun, Jintian; Jin, Guoguang

2016-12-01

It is very important for robotically assisted minimally invasive surgery to achieve a high-precision and smooth motion control. However, the surgical instrument tip will exhibit vibration caused by nonlinear friction and unmodeled dynamics, especially when the surgical robot system is attempting low-speed, fine motion. A fuzzy neural network sliding mode controller (FNNSMC) is proposed to suppress vibration of the surgical robotic system. Nonlinear friction and modeling uncertainties are compensated by a Stribeck model, a radial basis function (RBF) neural network and a fuzzy system, respectively. Simulations and experiments were performed on a 3 degree-of-freedom (DOF) minimally invasive surgical robot. The results demonstrate that the FNNSMC is effective and can suppress vibrations at the surgical instrument tip. The proposed FNNSMC can provide a robust performance and suppress the vibrations at the surgical instrument tip, which can enhance the quality and security of surgical procedures. Copyright © 2016 John Wiley & Sons, Ltd.

Cyber-physical approach to the network-centric robotics control task

NASA Astrophysics Data System (ADS)

Muliukha, Vladimir; Ilyashenko, Alexander; Zaborovsky, Vladimir; Lukashin, Alexey

2016-10-01

Complex engineering tasks concerning control for groups of mobile robots are developed poorly. In our work for their formalization we use cyber-physical approach, which extends the range of engineering and physical methods for a design of complex technical objects by researching the informational aspects of communication and interaction between objects and with an external environment [1]. The paper analyzes network-centric methods for control of cyber-physical objects. Robots or cyber-physical objects interact with each other by transmitting information via computer networks using preemptive queueing system and randomized push-out mechanism [2],[3]. The main field of application for the results of our work is space robotics. The selection of cyber-physical systems as a special class of designed objects is due to the necessity of integrating various components responsible for computing, communications and control processes. Network-centric solutions allow using universal means for the organization of information exchange to integrate different technologies for the control system.

Robot computer problem solving system

NASA Technical Reports Server (NTRS)

Becker, J. D.; Merriam, E. W.

1974-01-01

The conceptual, experimental, and practical phases of developing a robot computer problem solving system are outlined. Robot intelligence, conversion of the programming language SAIL to run under the THNEX monitor, and the use of the network to run several cooperating jobs at different sites are discussed.

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

Dynamical network interactions in distributed control of robots

NASA Astrophysics Data System (ADS)

Buscarino, Arturo; Fortuna, Luigi; Frasca, Mattia; Rizzo, Alessandro

2006-03-01

In this paper the dynamical network model of the interactions within a group of mobile robots is investigated and proposed as a possible strategy for controlling the robots without central coordination. Motivated by the results of the analysis of our simple model, we show that the system performance in the presence of noise can be improved by including long-range connections between the robots. Finally, a suitable strategy based on this model to control exploration and transport is introduced.

A neural-network approach to robotic control

NASA Technical Reports Server (NTRS)

Graham, D. P. W.; Deleuterio, G. M. T.

1993-01-01

An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

The middleware architecture supports heterogeneous network systems for module-based personal robot system

NASA Astrophysics Data System (ADS)

Choo, Seongho; Li, Vitaly; Choi, Dong Hee; Jung, Gi Deck; Park, Hong Seong; Ryuh, Youngsun

2005-12-01

On developing the personal robot system presently, the internal architecture is every module those occupy separated functions are connected through heterogeneous network system. This module-based architecture supports specialization and division of labor at not only designing but also implementation, as an effect of this architecture, it can reduce developing times and costs for modules. Furthermore, because every module is connected among other modules through network systems, we can get easy integrations and synergy effect to apply advanced mutual functions by co-working some modules. In this architecture, one of the most important technologies is the network middleware that takes charge communications among each modules connected through heterogeneous networks systems. The network middleware acts as the human nerve system inside of personal robot system; it relays, transmits, and translates information appropriately between modules that are similar to human organizations. The network middleware supports various hardware platform, heterogeneous network systems (Ethernet, Wireless LAN, USB, IEEE 1394, CAN, CDMA-SMS, RS-232C). This paper discussed some mechanisms about our network middleware to intercommunication and routing among modules, methods for real-time data communication and fault-tolerant network service. There have designed and implemented a layered network middleware scheme, distributed routing management, network monitoring/notification technology on heterogeneous networks for these goals. The main theme is how to make routing information in our network middleware. Additionally, with this routing information table, we appended some features. Now we are designing, making a new version network middleware (we call 'OO M/W') that can support object-oriented operation, also are updating program sources itself for object-oriented architecture. It is lighter, faster, and can support more operation systems and heterogeneous network systems, but other general purposed middlewares like CORBA, UPnP, etc. can support only one network protocol or operating system.

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

PubMed Central

Espinal, A.; Rostro-Gonzalez, H.; Carpio, M.; Guerra-Hernandez, E. I.; Ornelas-Rodriguez, M.; Puga-Soberanes, H. J.; Sotelo-Figueroa, M. A.; Melin, P.

2016-01-01

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases. PMID:27436997

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation.

PubMed

Espinal, A; Rostro-Gonzalez, H; Carpio, M; Guerra-Hernandez, E I; Ornelas-Rodriguez, M; Puga-Soberanes, H J; Sotelo-Figueroa, M A; Melin, P

2016-01-01

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

75 FR 36456 - Channel America Television Network, Inc., EquiMed, Inc., Kore Holdings, Inc., Robotic Vision...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-25

... SECURITIES AND EXCHANGE COMMISSION [File No. 500-1] Channel America Television Network, Inc., EquiMed, Inc., Kore Holdings, Inc., Robotic Vision Systems, Inc. (n/k/a Acuity Cimatrix, Inc.), Security... information concerning the securities of Channel America Television Network, Inc. because it has not filed any...

Modular architecture for robotics and teleoperation

DOEpatents

Anderson, Robert J.

1996-12-03

Systems and methods for modularization and discretization of real-time robot, telerobot and teleoperation systems using passive, network based control laws. Modules consist of network one-ports and two-ports. Wave variables and position information are passed between modules. The behavior of each module is decomposed into uncoupled linear-time-invariant, and coupled, nonlinear memoryless elements and then are separately discretized.

Biologically inspired computation and learning in Sensorimotor Systems

NASA Astrophysics Data System (ADS)

Lee, Daniel D.; Seung, H. S.

2001-11-01

Networking systems presently lack the ability to intelligently process the rich multimedia content of the data traffic they carry. Endowing artificial systems with the ability to adapt to changing conditions requires algorithms that can rapidly learn from examples. We demonstrate the application of such learning algorithms on an inexpensive quadruped robot constructed to perform simple sensorimotor tasks. The robot learns to track a particular object by discovering the salient visual and auditory cues unique to that object. The system uses a convolutional neural network that automatically combines color, luminance, motion, and auditory information. The weights of the networks are adjusted using feedback from a teacher to reflect the reliability of the various input channels in the surrounding environment. Additionally, the robot is able to compensate for its own motion by adapting the parameters of a vestibular ocular reflex system.

Perspectives of construction robots

NASA Astrophysics Data System (ADS)

Stepanov, M. A.; Gridchin, A. M.

2018-03-01

This article is an overview of construction robots features, based on formulating the list of requirements for different types of construction robots in relation to different types of construction works.. It describes a variety of construction works and ways to construct new or to adapt existing robot designs for a construction process. Also, it shows the prospects of AI-controlled machines, implementation of automated control systems and networks on construction sites. In the end, different ways to develop and improve, including ecological aspect, the construction process through the wide robotization, creating of data communication networks and, in perspective, establishing of fully AI-controlled construction complex are formulated.

Construction of multi-agent mobile robots control system in the problem of persecution with using a modified reinforcement learning method based on neural networks

NASA Astrophysics Data System (ADS)

Patkin, M. L.; Rogachev, G. N.

2018-02-01

A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.

Cooperative system and method using mobile robots for testing a cooperative search controller

DOEpatents

Byrne, Raymond H.; Harrington, John J.; Eskridge, Steven E.; Hurtado, John E.

2002-01-01

A test system for testing a controller provides a way to use large numbers of miniature mobile robots to test a cooperative search controller in a test area, where each mobile robot has a sensor, a communication device, a processor, and a memory. A method of using a test system provides a way for testing a cooperative search controller using multiple robots sharing information and communicating over a communication network.

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2006-12-01

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

A Decentralized Framework for Multi-Agent Robotic Systems

PubMed Central

2018-01-01

Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles. PMID:29389849

A neural network-based exploratory learning and motor planning system for co-robots

PubMed Central

Galbraith, Byron V.; Guenther, Frank H.; Versace, Massimiliano

2015-01-01

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object. PMID:26257640

A neural network-based exploratory learning and motor planning system for co-robots.

PubMed

Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

2015-01-01

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

PubMed

Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

PubMed Central

Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

Field testing of a remote controlled robotic tele-echo system in an ambulance using broadband mobile communication technology.

PubMed

Takeuchi, Ryohei; Harada, Hiroshi; Masuda, Kohji; Ota, Gen-ichiro; Yokoi, Masaki; Teramura, Nobuyasu; Saito, Tomoyuki

2008-06-01

We report the testing of a mobile Robotic Tele-echo system that was placed in an ambulance and successfully transmitted clear real time echo imaging of a patient's abdomen to the destination hospital from where this device was being remotely operated. Two-way communication between the paramedics in this vehicle and a doctor standing by at the hospital was undertaken. The robot was equipped with an ultrasound probe which was remotely controlled by the clinician at the hospital and ultrasound images of the patient were transmitted wirelessly. The quality of the ultrasound images that were transmitted over the public mobile telephone networks and those transmitted over the Multimedia Wireless Access Network (a private networks) were compared. The transmission rate over the public networks and the private networks was approximately 256 Kbps, 3 Mbps respectively. Our results indicate that ultrasound images of far higher definition could be obtained through the private networks.

Applications of artificial intelligence in safe human-robot interactions.

PubMed

Najmaei, Nima; Kermani, Mehrdad R

2011-04-01

The integration of industrial robots into the human workspace presents a set of unique challenges. This paper introduces a new sensory system for modeling, tracking, and predicting human motions within a robot workspace. A reactive control scheme to modify a robot's operations for accommodating the presence of the human within the robot workspace is also presented. To this end, a special class of artificial neural networks, namely, self-organizing maps (SOMs), is employed for obtaining a superquadric-based model of the human. The SOM network receives information of the human's footprints from the sensory system and infers necessary data for rendering the human model. The model is then used in order to assess the danger of the robot operations based on the measured as well as predicted human motions. This is followed by the introduction of a new reactive control scheme that results in the least interferences between the human and robot operations. The approach enables the robot to foresee an upcoming danger and take preventive actions before the danger becomes imminent. Simulation and experimental results are presented in order to validate the effectiveness of the proposed method.

A networked modular hardware and software system for MRI-guided robotic prostate interventions

NASA Astrophysics Data System (ADS)

Su, Hao; Shang, Weijian; Harrington, Kevin; Camilo, Alex; Cole, Gregory; Tokuda, Junichi; Hata, Nobuhiko; Tempany, Clare; Fischer, Gregory S.

2012-02-01

Magnetic resonance imaging (MRI) provides high resolution multi-parametric imaging, large soft tissue contrast, and interactive image updates making it an ideal modality for diagnosing prostate cancer and guiding surgical tools. Despite a substantial armamentarium of apparatuses and systems has been developed to assist surgical diagnosis and therapy for MRI-guided procedures over last decade, the unified method to develop high fidelity robotic systems in terms of accuracy, dynamic performance, size, robustness and modularity, to work inside close-bore MRI scanner still remains a challenge. In this work, we develop and evaluate an integrated modular hardware and software system to support the surgical workflow of intra-operative MRI, with percutaneous prostate intervention as an illustrative case. Specifically, the distinct apparatuses and methods include: 1) a robot controller system for precision closed loop control of piezoelectric motors, 2) a robot control interface software that connects the 3D Slicer navigation software and the robot controller to exchange robot commands and coordinates using the OpenIGTLink open network communication protocol, and 3) MRI scan plane alignment to the planned path and imaging of the needle as it is inserted into the target location. A preliminary experiment with ex-vivo phantom validates the system workflow, MRI-compatibility and shows that the robotic system has a better than 0.01mm positioning accuracy.

Neural joint control for Space Shuttle Remote Manipulator System

NASA Technical Reports Server (NTRS)

Atkins, Mark A.; Cox, Chadwick J.; Lothers, Michael D.; Pap, Robert M.; Thomas, Charles R.

1992-01-01

Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

Integration of Hierarchical Goal Network Planning and Autonomous Path Planning

DTIC Science & Technology

2016-03-01

Conference on Robotics and Automation (ICRA); 2010 May 3– 7; Anchorage, AK. p. 2902–2908. 4. Ayan NF, Kuter U, Yaman F, Goldman RP. Hotride...DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT Automated planning has...world robotic systems. This report documents work to integrate a hierarchical goal network planning algorithm with low-level path planning. The system

Multidisciplinary approach for developing a new robotic system for domiciliary assistance to elderly people.

PubMed

Cavallo, F; Aquilano, M; Bonaccorsi, M; Mannari, I; Carrozza, M C; Dario, P

2011-01-01

This paper aims to show the effectiveness of a (inter / multi)disciplinary team, based on the technology developers, elderly care organizations, and designers, in developing the ASTRO robotic system for domiciliary assistance to elderly people. The main issues presented in this work concern the improvement of robot's behavior by means of a smart sensor network able to share information with the robot for localization and navigation, and the design of the robot's appearance and functionalities by means of a substantial analysis of users' requirements and attitude to robotic technology to improve acceptability and usability.

Telerobotic management system: coordinating multiple human operators with multiple robots

NASA Astrophysics Data System (ADS)

King, Jamie W.; Pretty, Raymond; Brothers, Brendan; Gosine, Raymond G.

2003-09-01

This paper describes an application called the Tele-robotic management system (TMS) for coordinating multiple operators with multiple robots for applications such as underground mining. TMS utilizes several graphical interfaces to allow the user to define a partially ordered plan for multiple robots. This plan is then converted to a Petri net for execution and monitoring. TMS uses a distributed framework to allow robots and operators to easily integrate with the applications. This framework allows robots and operators to join the network and advertise their capabilities through services. TMS then decides whether tasks should be dispatched to a robot or a remote operator based on the services offered by the robots and operators.

Higher-order neural network software for distortion invariant object recognition

NASA Technical Reports Server (NTRS)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Navigation of robotic system using cricket motes

NASA Astrophysics Data System (ADS)

Patil, Yogendra J.; Baine, Nicholas A.; Rattan, Kuldip S.

2011-06-01

This paper presents a novel algorithm for self-mapping of the cricket motes that can be used for indoor navigation of autonomous robotic systems. The cricket system is a wireless sensor network that can provide indoor localization service to its user via acoustic ranging techniques. The behavior of the ultrasonic transducer on the cricket mote is studied and the regions where satisfactorily distance measurements can be obtained are recorded. Placing the motes in these regions results fine-grain mapping of the cricket motes. Trilateration is used to obtain a rigid coordinate system, but is insufficient if the network is to be used for navigation. A modified SLAM algorithm is applied to overcome the shortcomings of trilateration. Finally, the self-mapped cricket motes can be used for navigation of autonomous robotic systems in an indoor location.

A Novel Cloud-Based Service Robotics Application to Data Center Environmental Monitoring

PubMed Central

Russo, Ludovico Orlando; Rosa, Stefano; Maggiora, Marcello; Bona, Basilio

2016-01-01

This work presents a robotic application aimed at performing environmental monitoring in data centers. Due to the high energy density managed in data centers, environmental monitoring is crucial for controlling air temperature and humidity throughout the whole environment, in order to improve power efficiency, avoid hardware failures and maximize the life cycle of IT devices. State of the art solutions for data center monitoring are nowadays based on environmental sensor networks, which continuously collect temperature and humidity data. These solutions are still expensive and do not scale well in large environments. This paper presents an alternative to environmental sensor networks that relies on autonomous mobile robots equipped with environmental sensors. The robots are controlled by a centralized cloud robotics platform that enables autonomous navigation and provides a remote client user interface for system management. From the user point of view, our solution simulates an environmental sensor network. The system can easily be reconfigured in order to adapt to management requirements and changes in the layout of the data center. For this reason, it is called the virtual sensor network. This paper discusses the implementation choices with regards to the particular requirements of the application and presents and discusses data collected during a long-term experiment in a real scenario. PMID:27509505

Computer graphics testbed to simulate and test vision systems for space applications

NASA Technical Reports Server (NTRS)

Cheatham, John B.

1991-01-01

Research activity has shifted from computer graphics and vision systems to the broader scope of applying concepts of artificial intelligence to robotics. Specifically, the research is directed toward developing Artificial Neural Networks, Expert Systems, and Laser Imaging Techniques for Autonomous Space Robots.

ROS-IGTL-Bridge: an open network interface for image-guided therapy using the ROS environment.

PubMed

Frank, Tobias; Krieger, Axel; Leonard, Simon; Patel, Niravkumar A; Tokuda, Junichi

2017-08-01

With the growing interest in advanced image-guidance for surgical robot systems, rapid integration and testing of robotic devices and medical image computing software are becoming essential in the research and development. Maximizing the use of existing engineering resources built on widely accepted platforms in different fields, such as robot operating system (ROS) in robotics and 3D Slicer in medical image computing could simplify these tasks. We propose a new open network bridge interface integrated in ROS to ensure seamless cross-platform data sharing. A ROS node named ROS-IGTL-Bridge was implemented. It establishes a TCP/IP network connection between the ROS environment and external medical image computing software using the OpenIGTLink protocol. The node exports ROS messages to the external software over the network and vice versa simultaneously, allowing seamless and transparent data sharing between the ROS-based devices and the medical image computing platforms. Performance tests demonstrated that the bridge could stream transforms, strings, points, and images at 30 fps in both directions successfully. The data transfer latency was <1.2 ms for transforms, strings and points, and 25.2 ms for color VGA images. A separate test also demonstrated that the bridge could achieve 900 fps for transforms. Additionally, the bridge was demonstrated in two representative systems: a mock image-guided surgical robot setup consisting of 3D slicer, and Lego Mindstorms with ROS as a prototyping and educational platform for IGT research; and the smart tissue autonomous robot surgical setup with 3D Slicer. The study demonstrated that the bridge enabled cross-platform data sharing between ROS and medical image computing software. This will allow rapid and seamless integration of advanced image-based planning/navigation offered by the medical image computing software such as 3D Slicer into ROS-based surgical robot systems.

Integrated network architecture for sustained human and robotic exploration

NASA Technical Reports Server (NTRS)

Noreen, Gary K.; Cesarone, Robert; Deutsch, Leslie; Edwards, Charlie; Soloff, Jason; Ely, Todd; Cook, Brian; Morabito, David; Hemmati, Hamid; Piazzolla, Sabino;

Using Robots and Contract Learning to Teach Cyber-Physical Systems to Undergraduates

ERIC Educational Resources Information Center

Crenshaw, T. L. A.

2013-01-01

Cyber-physical systems are a genre of networked real-time systems that monitor and control the physical world. Examples include unmanned aerial vehicles and industrial robotics. The experts who develop these complex systems are retiring much faster than universities are graduating engineering majors. As a result, it is important for undergraduates…

Mamdani Fuzzy System for Indoor Autonomous Mobile Robot

NASA Astrophysics Data System (ADS)

Khan, M. K. A. Ahamed; Rashid, Razif; Elamvazuthi, I.

2011-06-01

Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, Mamdani fuzzy system for an autonomous mobile robot is developed. The paper begins with the discussion on the conventional controller and then followed by the description of fuzzy logic controller in detail.

Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network

PubMed Central

2015-01-01

For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system. PMID:26089863

Evolutionary Space Communications Architectures for Human/Robotic Exploration and Science Missions

NASA Technical Reports Server (NTRS)

Bhasin, Kul; Hayden, Jeffrey L.

2004-01-01

NASA enterprises have growing needs for an advanced, integrated, communications infrastructure that will satisfy the capabilities needed for multiple human, robotic and scientific missions beyond 2015. Furthermore, the reliable, multipoint infrastructure is required to provide continuous, maximum coverage of areas of concentrated activities, such as around Earth and in the vicinity of the Moon or Mars, with access made available on demand of the human or robotic user. As a first step, the definitions of NASA's future space communications and networking architectures are underway. Architectures that describe the communications and networking needed between the nodal regions consisting of Earth, Moon, Lagrange points, Mars, and the places of interest within the inner and outer solar system have been laid out. These architectures will need the modular flexibility that must be included in the communication and networking technologies to enable the infrastructure to grow in capability with time and to transform from supporting robotic missions in the solar system to supporting human ventures to Mars, Jupiter, Jupiter's moons, and beyond. The protocol-based networking capability seamlessly connects the backbone, access, inter-spacecraft and proximity network elements of the architectures employed in the infrastructure. In this paper, we present the summary of NASA's near and long term needs and capability requirements that were gathered by participative methods. We describe an integrated architecture concept and model that will enable communications for evolutionary robotic and human science missions. We then define the communication nodes, their requirements, and various options to connect them.

Evolutionary Space Communications Architectures for Human/Robotic Exploration and Science Missions

NASA Astrophysics Data System (ADS)

Bhasin, Kul; Hayden, Jeffrey L.

2004-02-01

NASA enterprises have growing needs for an advanced, integrated, communications infrastructure that will satisfy the capabilities needed for multiple human, robotic and scientific missions beyond 2015. Furthermore, the reliable, multipoint infrastructure is required to provide continuous, maximum coverage of areas of concentrated activities, such as around Earth and in the vicinity of the Moon or Mars, with access made available on demand of the human or robotic user. As a first step, the definitions of NASA's future space communications and networking architectures are underway. Architectures that describe the communications and networking needed between the nodal regions consisting of Earth, Moon, Lagrange points, Mars, and the places of interest within the inner and outer solar system have been laid out. These architectures will need the modular flexibility that must be included in the communication and networking technologies to enable the infrastructure to grow in capability with time and to transform from supporting robotic missions in the solar system to supporting human ventures to Mars, Jupiter, Jupiter's moons, and beyond. The protocol-based networking capability seamlessly connects the backbone, access, inter-spacecraft and proximity network elements of the architectures employed in the infrastructure. In this paper, we present the summary of NASA's near and long term needs and capability requirements that were gathered by participative methods. We describe an integrated architecture concept and model that will enable communications for evolutionary robotic and human science missions. We then define the communication nodes, their requirements, and various options to connect them.

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

NASA Technical Reports Server (NTRS)

Griffin, Sandy (Editor)

1987-01-01

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered.

Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design.

PubMed

Cheng, Long; Hou, Zeng-Guang; Tan, Min; Zhang, W J

2012-10-01

The trajectory tracking problem of a closed-chain five-bar robot is studied in this paper. Based on an error transformation function and the backstepping technique, an approximation-based tracking algorithm is proposed, which can guarantee the control performance of the robotic system in both the stable and transient phases. In particular, the overshoot, settling time, and final tracking error of the robotic system can be all adjusted by properly setting the parameters in the error transformation function. The radial basis function neural network (RBFNN) is used to compensate the complicated nonlinear terms in the closed-loop dynamics of the robotic system. The approximation error of the RBFNN is only required to be bounded, which simplifies the initial "trail-and-error" configuration of the neural network. Illustrative examples are given to verify the theoretical analysis and illustrate the effectiveness of the proposed algorithm. Finally, it is also shown that the proposed approximation-based controller can be simplified by a smart mechanical design of the closed-chain robot, which demonstrates the promise of the integrated design and control philosophy.

A Robotic Solution for Assisting People with MCI at Home: Preliminary Tests of the ENRICHME System.

PubMed

Salatino, Claudia; Pigini, Lucia; Van Kol, Marlies Maria Elisabeth; Gower, Valerio; Andrich, Renzo; Munaro, Giulia; Rosso, Roberto; Castellani, Angelo P; Farina, Elisabetta

2017-01-01

Robots have the potential to support care and independence of older adults. The ENRICHME project is developing an integrated system composed of a robot, sensors and a networking care platform, aiming at assisting older adults with MCI in their home environment. This paper reports findings of the tests performed on a sample of MCI users and their caregivers, with the first version of the ENRICHME system, in a controlled environment.

Center for Neural Engineering: applications of pulse-coupled neural networks

NASA Astrophysics Data System (ADS)

Malkani, Mohan; Bodruzzaman, Mohammad; Johnson, John L.; Davis, Joel

1999-03-01

Pulsed-Coupled Neural Network (PCNN) is an oscillatory model neural network where grouping of cells and grouping among the groups that form the output time series (number of cells that fires in each input presentation also called `icon'). This is based on the synchronicity of oscillations. Recent work by Johnson and others demonstrated the functional capabilities of networks containing such elements for invariant feature extraction using intensity maps. PCNN thus presents itself as a more biologically plausible model with solid functional potential. This paper will present the summary of several projects and their results where we successfully applied PCNN. In project one, the PCNN was applied for object recognition and classification through a robotic vision system. The features (icons) generated by the PCNN were then fed into a feedforward neural network for classification. In project two, we developed techniques for sensory data fusion. The PCNN algorithm was implemented and tested on a B14 mobile robot. The PCNN-based features were extracted from the images taken from the robot vision system and used in conjunction with the map generated by data fusion of the sonar and wheel encoder data for the navigation of the mobile robot. In our third project, we applied the PCNN for speaker recognition. The spectrogram image of speech signals are fed into the PCNN to produce invariant feature icons which are then fed into a feedforward neural network for speaker identification.

Intelligent control of robotic arm/hand systems for the NASA EVA retriever using neural networks

NASA Technical Reports Server (NTRS)

Mclauchlan, Robert A.

1989-01-01

Adaptive/general learning algorithms using varying neural network models are considered for the intelligent control of robotic arm plus dextrous hand/manipulator systems. Results are summarized and discussed for the use of the Barto/Sutton/Anderson neuronlike, unsupervised learning controller as applied to the stabilization of an inverted pendulum on a cart system. Recommendations are made for the application of the controller and a kinematic analysis for trajectory planning to simple object retrieval (chase/approach and capture/grasp) scenarios in two dimensions.

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2008-10-01

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

A Mobile Sensor Network System for Monitoring of Unfriendly Environments.

PubMed

Song, Guangming; Zhou, Yaoxin; Ding, Fei; Song, Aiguo

2008-11-14

Observing microclimate changes is one of the most popular applications of wireless sensor networks. However, some target environments are often too dangerous or inaccessible to humans or large robots and there are many challenges for deploying and maintaining wireless sensor networks in those unfriendly environments. This paper presents a mobile sensor network system for solving this problem. The system architecture, the mobile node design, the basic behaviors and advanced network capabilities have been investigated respectively. A wheel-based robotic node architecture is proposed here that can add controlled mobility to wireless sensor networks. A testbed including some prototype nodes has also been created for validating the basic functions of the proposed mobile sensor network system. Motion performance tests have been done to get the positioning errors and power consumption model of the mobile nodes. Results of the autonomous deployment experiment show that the mobile nodes can be distributed evenly into the previously unknown environments. It provides powerful support for network deployment and maintenance and can ensure that the sensor network will work properly in unfriendly environments.

Determining Locations by Use of Networks of Passive Beacons

NASA Technical Reports Server (NTRS)

Okino, Clayton; Gray, Andrew; Jennings, Esther

2009-01-01

Networks of passive radio beacons spanning moderate-sized terrain areas have been proposed to aid navigation of small robotic aircraft that would be used to explore Saturn s moon Titan. Such networks could also be used on Earth to aid navigation of robotic aircraft, land vehicles, or vessels engaged in exploration or reconnaissance in situations or locations (e.g., underwater locations) in which Global Positioning System (GPS) signals are unreliable or unavailable. Prior to use, it would be necessary to pre-position the beacons at known locations that would be determined by use of one or more precise independent global navigation system(s). Thereafter, while navigating over the area spanned by a given network of passive beacons, an exploratory robot would use the beacons to determine its position precisely relative to the known beacon positions (see figure). If it were necessary for the robot to explore multiple, separated terrain areas spanned by different networks of beacons, the robot could use a long-haul, relatively coarse global navigation system for the lower-precision position determination needed during transit between such areas. The proposed method of precise determination of position of an exploratory robot relative to the positions of passive radio beacons is based partly on the principles of radar and partly on the principles of radio-frequency identification (RFID) tags. The robot would transmit radar-like signals that would be modified and reflected by the passive beacons. The distance to each beacon would be determined from the roundtrip propagation time and/or round-trip phase shift of the signal returning from that beacon. Signals returned from different beacons could be distinguished by means of their RFID characteristics. Alternatively or in addition, the antenna of each beacon could be designed to radiate in a unique pattern that could be identified by the navigation system. Also, alternatively or in addition, sets of identical beacons could be deployed in unique configurations such that the navigation system could identify their unique combinations of radio-frequency reflections as an alternative to leveraging the uniqueness of the RFID tags. The degree of dimensional accuracy would depend not only on the locations of the beacons but also on the number of beacon signals received, the number of samples of each signal, the motion of the robot, and the time intervals between samples. At one extreme, a single sample of the return signal from a single beacon could be used to determine the distance from that beacon and hence to determine that the robot is located somewhere on a sphere, the radius of which equals that distance and the center of which lies at the beacon. In a less extreme example, the three-dimensional position of the robot could be determined with fair precision from a single sample of the signal from each of three beacons. In intermediate cases, position estimates could be refined and/or position ambiguities could be resolved by use of supplementary readings of an altimeter and other instruments aboard the robot.

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system's adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Developing a Telescope Simulator Towards a Global Autonomous Robotic Telescope Network

NASA Astrophysics Data System (ADS)

Giakoumidis, N.; Ioannou, Z.; Dong, H.; Mavridis, N.

2013-05-01

A robotic telescope network is a system that integrates a number of telescopes to observe a variety of astronomical targets without being operated by a human. This system autonomously selects and observes targets in accordance to an optimized target. It dynamically allocates telescope resources depending on the observation requests, specifications of the telescopes, target visibility, meteorological conditions, daylight, location restrictions and availability and many other factors. In this paper, we introduce a telescope simulator, which can control a telescope to a desired position in order to observe a specific object. The system includes a Client Module, a Server Module, and a Dynamic Scheduler module. We make use and integrate a number of open source software to simulate the movement of a robotic telescope, the telescope characteristics, the observational data and weather conditions in order to test and optimize our system.

Robot Task Commander with Extensible Programming Environment

NASA Technical Reports Server (NTRS)

Hart, Stephen W (Inventor); Wightman, Brian J (Inventor); Dinh, Duy Paul (Inventor); Yamokoski, John D. (Inventor); Gooding, Dustin R (Inventor)

2014-01-01

A system for developing distributed robot application-level software includes a robot having an associated control module which controls motion of the robot in response to a commanded task, and a robot task commander (RTC) in networked communication with the control module over a network transport layer (NTL). The RTC includes a script engine(s) and a GUI, with a processor and a centralized library of library blocks constructed from an interpretive computer programming code and having input and output connections. The GUI provides access to a Visual Programming Language (VPL) environment and a text editor. In executing a method, the VPL is opened, a task for the robot is built from the code library blocks, and data is assigned to input and output connections identifying input and output data for each block. A task sequence(s) is sent to the control module(s) over the NTL to command execution of the task.

A telerobotic digital controller system

NASA Technical Reports Server (NTRS)

Brown, Richard J.

1992-01-01

This system is a network of joint mounted dual axes digital servo-controllers (DDSC), providing control of various joints and end effectors of different robotic systems. This report provides description of and user required information for the Digital Controller System Network (DSCN) and, in particular, the DDSC, Model DDSC-2, developed to perform the controller functions. The DDSC can control 3 phase brushless or brush type DC motors, requiring up to 8 amps. Only four wires, two for power and 2 for serial communication, are required, except for local sensor and motor connections. This highly capable, very flexible, programmable servo-controller, contained on a single, compact printed circuit board measuring only 4.5 x 5.1 inches, is applicable to control systems of all types from sub-arc second precision pointing to control of robotic joints and end effectors. This document concentrates on the robotic applications for the DDSC.

MEART: The Semi-Living Artist

PubMed Central

Bakkum, Douglas J.; Gamblen, Philip M.; Ben-Ary, Guy; Chao, Zenas C.; Potter, Steve M.

2007-01-01

Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology. PMID:18958276

Integrated Network Architecture for Sustained Human and Robotic Exploration

NASA Technical Reports Server (NTRS)

Noreen, Gary; Cesarone, Robert; Deutsch, Leslie; Edwards, Charles; Soloff, Jason; Ely, Todd; Cook, Brian; Morabito, David; Hemmati, Hamid; Piazolla, Sabino;

Robot, computer problem solving system

NASA Technical Reports Server (NTRS)

Becker, J. D.; Merriam, E. W.

1973-01-01

The TENEX computer system, the ARPA network, and computer language design technology was applied to support the complex system programs. By combining the pragmatic and theoretical aspects of robot development, an approach is created which is grounded in realism, but which also has at its disposal the power that comes from looking at complex problems from an abstract analytical point of view.

Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS.

PubMed

Weidel, Philipp; Djurfeldt, Mikael; Duarte, Renato C; Morrison, Abigail

2016-01-01

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.

Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS

PubMed Central

Weidel, Philipp; Djurfeldt, Mikael; Duarte, Renato C.; Morrison, Abigail

2016-01-01

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning. PMID:27536234

Model of a Soft Robotic Actuator with Embedded Fluidic Network

NASA Astrophysics Data System (ADS)

Gamus, Benny; Or, Yizhar; Gat, Amir

2017-11-01

Soft robotics is an emerging bio-inspired concept of actuation, with promising applications for robotic locomotion and manipulation. Focusing on actuation by pressurized embedded fluidic networks, we present analytic formulation and closed-form solutions of an elastic actuator with pressurized fluidic networks. In this work we account for the effects of solid inertia and elasticity, as well as fluid viscosity, which allows modelling the system's step-response and frequency response as well as suggesting mode elimination and isolation techniques. We also present and model the application of viscous-peeling as an actuation mechanism, simplifying the fabrication process by eliminating the need for internal cavities. The theoretical results describing the viscous-elastic-inertial dynamics of the actuator are illustrated by experiments. The approach presented in this work may pave the way for the design and implementation of soft robotic legged locomotion that exploits dynamic effects.

Decentralized sensor fusion for Ubiquitous Networking Robotics in Urban Areas.

PubMed

Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T J

2010-01-01

In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

A Tree Based Self-routing Scheme for Mobility Support in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Kim, Young-Duk; Yang, Yeon-Mo; Kang, Won-Seok; Kim, Jin-Wook; An, Jinung

Recently, WSNs (Wireless Sensor Networks) with mobile robot is a growing technology that offer efficient communication services for anytime and anywhere applications. However, the tiny sensor node has very limited network resources due to its low battery power, low data rate, node mobility, and channel interference constraint between neighbors. Thus, in this paper, we proposed a tree based self-routing protocol for autonomous mobile robots based on beacon mode and implemented in real test-bed environments. The proposed scheme offers beacon based real-time scheduling for reliable association process between parent and child nodes. In addition, it supports smooth handover procedure by reducing flooding overhead of control packets. Throughout the performance evaluation by using a real test-bed system and simulation, we illustrate that our proposed scheme demonstrates promising performance for wireless sensor networks with mobile robots.

An integrated design and fabrication strategy for entirely soft, autonomous robots.

PubMed

Wehner, Michael; Truby, Ryan L; Fitzgerald, Daniel J; Mosadegh, Bobak; Whitesides, George M; Lewis, Jennifer A; Wood, Robert J

2016-08-25

Soft robots possess many attributes that are difficult, if not impossible, to achieve with conventional robots composed of rigid materials. Yet, despite recent advances, soft robots must still be tethered to hard robotic control systems and power sources. New strategies for creating completely soft robots, including soft analogues of these crucial components, are needed to realize their full potential. Here we report the untethered operation of a robot composed solely of soft materials. The robot is controlled with microfluidic logic that autonomously regulates fluid flow and, hence, catalytic decomposition of an on-board monopropellant fuel supply. Gas generated from the fuel decomposition inflates fluidic networks downstream of the reaction sites, resulting in actuation. The body and microfluidic logic of the robot are fabricated using moulding and soft lithography, respectively, and the pneumatic actuator networks, on-board fuel reservoirs and catalytic reaction chambers needed for movement are patterned within the body via a multi-material, embedded 3D printing technique. The fluidic and elastomeric architectures required for function span several orders of magnitude from the microscale to the macroscale. Our integrated design and rapid fabrication approach enables the programmable assembly of multiple materials within this architecture, laying the foundation for completely soft, autonomous robots.

Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas

PubMed Central

Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M.; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T.J.

2010-01-01

In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted. PMID:22294927

2015 Marine Corps Security Environment Forecast: Futures 2030-2045

DTIC Science & Technology

2015-01-01

The technologies that make the iPhone “smart” were publically funded—the Internet, wireless networks, the global positioning system, microelectronics...Energy Revolution (63 percent);  Internet of Things (ubiquitous sensors embedded in interconnected computing devices) (50 percent);  “Sci-Fi...Neuroscience & artificial intelligence - Sensors /control systems -Power & energy -Human-robot interaction Robots/autonomous systems will become part of the

Parametric motion control of robotic arms: A biologically based approach using neural networks

NASA Technical Reports Server (NTRS)

Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

1993-01-01

A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

NASA Technical Reports Server (NTRS)

Soloway, Donald I.; Bialasiewicz, Jan T.

1992-01-01

A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

Control of autonomous robot using neural networks

NASA Astrophysics Data System (ADS)

Barton, Adam; Volna, Eva

2017-07-01

The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system’s adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

Realtime motion planning for a mobile robot in an unknown environment using a neurofuzzy based approach

NASA Astrophysics Data System (ADS)

Zheng, Taixiong

2005-12-01

A neuro-fuzzy network based approach for robot motion in an unknown environment was proposed. In order to control the robot motion in an unknown environment, the behavior of the robot was classified into moving to the goal and avoiding obstacles. Then, according to the dynamics of the robot and the behavior character of the robot in an unknown environment, fuzzy control rules were introduced to control the robot motion. At last, a 6-layer neuro-fuzzy network was designed to merge from what the robot sensed to robot motion control. After being trained, the network may be used for robot motion control. Simulation results show that the proposed approach is effective for robot motion control in unknown environment.

An Embedded Systems Laboratory to Support Rapid Prototyping of Robotics and the Internet of Things

ERIC Educational Resources Information Center

Hamblen, J. O.; van Bekkum, G. M. E.

2013-01-01

This paper describes a new approach for a course and laboratory designed to allow students to develop low-cost prototypes of robotic and other embedded devices that feature Internet connectivity, I/O, networking, a real-time operating system (RTOS), and object-oriented C/C++. The application programming interface (API) libraries provided permit…

Robotic acquisition programs: technical and performance challenges

NASA Astrophysics Data System (ADS)

Thibadoux, Steven A.

2002-07-01

The Unmanned Ground Vehicles/ Systems Joint Project Office (UGV/S JPO) is developing and fielding a variety of tactical robotic systems for the Army and Marine Corps. The Standardized Robotic System (SRS) provides a family of common components that can be installed in existing military vehicles, to allow unmanned operation of the vehicle and its payloads. The Robotic Combat Support System (RCSS) will be a medium sized unmanned system with interchangeable attachments, allowing a remote operator to perform a variety of engineering tasks. The Gladiator Program is a USMC initiative for a small to medium sized, highly mobile UGV to conduct scout/ surveillance missions and to carry various lethal and non-lethal payloads. Acquisition plans for these programs require preplanned evolutionary block upgrades to add operational capability, as new technology becomes available. This paper discusses technical and performance issues that must be resolved and the enabling technologies needed for near term block upgrades of these first generation robotic systems. Additionally, two Joint Robotics Program (JRP) initiatives, Robotic Acquisition through Virtual Environments and Networked Simulations (RAVENS) and Joint Architecture for Unmanned Ground Systems (JAUGS), will be discussed. RAVENS and JAUGS will be used to efficiently evaluate and integrate new technologies to be incorporated in system upgrades.

Robotic Lunar Landers for Science and Exploration

NASA Technical Reports Server (NTRS)

Cohen, B. A.; Hill, L. A.; Bassler, J. A.; Chavers, D. G.; Hammond, M. S.; Harris, D. W.; Kirby, K. W.; Morse, B. J.; Mulac, B. D.; Reed, C. L. B.

2010-01-01

NASA Marshall Space Flight Center and The Johns Hopkins University Applied Physics Laboratory has been conducting mission studies and performing risk reduction activities for NASA s robotic lunar lander flight projects. In 2005, the Robotic Lunar Exploration Program Mission #2 (RLEP-2) was selected as a Exploration Systems Mission Directorate precursor robotic lunar lander mission to demonstrate precision landing and definitively determine if there was water ice at the lunar poles; however, this project was canceled. Since 2008, the team has been supporting NASA s Science Mission Directorate designing small lunar robotic landers for diverse science missions. The primary emphasis has been to establish anchor nodes of the International Lunar Network (ILN), a network of lunar science stations envisioned to be emplaced by multiple nations. This network would consist of multiple landers carrying instruments to address the geophysical characteristics and evolution of the moon. Additional mission studies have been conducted to support other objectives of the lunar science community and extensive risk reduction design and testing has been performed to advance the design of the lander system and reduce development risk for flight projects. This paper describes the current status of the robotic lunar mission studies that have been conducted by the MSFC/APL Robotic Lunar Lander Development team, including the ILN Anchor Nodes mission. In addition, the results to date of the lunar lander development risk reduction efforts including high pressure propulsion system testing, structure and mechanism development and testing, long cycle time battery testing and combined GN&C and avionics testing will be addressed. The most visible elements of the risk reduction program are two autonomous lander test articles: a compressed air system with limited flight durations and a second version using hydrogen peroxide propellant to achieve significantly longer flight times and the ability to more fully exercise flight sensors and algorithms. Robotic Lunar Lander design and development will have significant feed-forward to other missions to the Moon and, indeed, to other airless bodies such as Mercury, asteroids, and Europa, to which similar science and exploration objectives are applicable.

Robotic Lunar Landers For Science And Exploration

NASA Technical Reports Server (NTRS)

Cohen, B. A.; Bassler, J. A.; Morse, B. J.; Reed, C. L. B.

2010-01-01

NASA Marshall Space Flight Center and The Johns Hopkins University Applied Physics Laboratory have been conducting mission studies and performing risk reduction activities for NASA s robotic lunar lander flight projects. In 2005, the Robotic Lunar Exploration Program Mission #2 (RLEP-2) was selected as an ESMD precursor robotic lander mission to demonstrate precision landing and determine if there was water ice at the lunar poles; however, this project was canceled. Since 2008, the team has been supporting SMD designing small lunar robotic landers for science missions, primarily to establish anchor nodes of the International Lunar Network (ILN), a network of lunar geophysical nodes. Additional mission studies have been conducted to support other objectives of the lunar science community. This paper describes the current status of the MSFC/APL robotic lunar mission studies and risk reduction efforts including high pressure propulsion system testing, structure and mechanism development and testing, long cycle time battery testing, combined GN&C and avionics testing, and two autonomous lander test articles.

Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks.

PubMed

Wang, Zhijun; Mirdamadi, Reza; Wang, Qing

2016-01-01

Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics, unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed. Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances. As the result, simple, reliable, and affordable robots can be deployed to form the network. The simulator simulates a group of robots as an unsupervised learning unit and tests the learning results under scenarios with different complexities. The simulation results show that a group of robots could demonstrate highly collaborative behavior on a complex terrain. This study could potentially provide a software simulation platform for testing individual and group capability of robots before the design process and manufacturing of robots. Therefore, results of the project have the potential to reduce the cost and improve the efficiency of robot design and building.

Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks

PubMed Central

Wang, Zhijun; Mirdamadi, Reza; Wang, Qing

2016-01-01

Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics, unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed. Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances. As the result, simple, reliable, and affordable robots can be deployed to form the network. The simulator simulates a group of robots as an unsupervised learning unit and tests the learning results under scenarios with different complexities. The simulation results show that a group of robots could demonstrate highly collaborative behavior on a complex terrain. This study could potentially provide a software simulation platform for testing individual and group capability of robots before the design process and manufacturing of robots. Therefore, results of the project have the potential to reduce the cost and improve the efficiency of robot design and building. PMID:28540284

Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.

PubMed

Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A; Przekwas, Andrzej; Francis, Joseph T; Lytton, William W

2015-01-01

Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics.

Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human-Robot Interaction.

PubMed

Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

2016-01-01

To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language-behavior relationships and the temporal patterns of interaction. Here, "internal dynamics" refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language-behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language-behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

Towards multi-platform software architecture for Collaborative Teleoperation

NASA Astrophysics Data System (ADS)

Domingues, Christophe; Otmane, Samir; Davesne, Frederic; Mallem, Malik

2009-03-01

Augmented Reality (AR) can provide to a Human Operator (HO) a real help in achieving complex tasks, such as remote control of robots and cooperative teleassistance. Using appropriate augmentations, the HO can interact faster, safer and easier with the remote real world. In this paper, we present an extension of an existing distributed software and network architecture for collaborative teleoperation based on networked human-scaled mixed reality and mobile platform. The first teleoperation system was composed by a VR application and a Web application. However the 2 systems cannot be used together and it is impossible to control a distant robot simultaneously. Our goal is to update the teleoperation system to permit a heterogeneous collaborative teleoperation between the 2 platforms. An important feature of this interface is based on the use of different Virtual Reality platforms and different Mobile platforms to control one or many robots.

Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

PubMed Central

Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V.; Alvarez-Santos, Victor; Pardo, Xose Manuel

2013-01-01

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal. PMID:23271604

Self-organized multi-camera network for a fast and easy deployment of ubiquitous robots in unknown environments.

PubMed

Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V; Alvarez-Santos, Victor; Pardo, Xose Manuel

2012-12-27

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal.

A fault-tolerant intelligent robotic control system

NASA Technical Reports Server (NTRS)

Marzwell, Neville I.; Tso, Kam Sing

1993-01-01

This paper describes the concept, design, and features of a fault-tolerant intelligent robotic control system being developed for space and commercial applications that require high dependability. The comprehensive strategy integrates system level hardware/software fault tolerance with task level handling of uncertainties and unexpected events for robotic control. The underlying architecture for system level fault tolerance is the distributed recovery block which protects against application software, system software, hardware, and network failures. Task level fault tolerance provisions are implemented in a knowledge-based system which utilizes advanced automation techniques such as rule-based and model-based reasoning to monitor, diagnose, and recover from unexpected events. The two level design provides tolerance of two or more faults occurring serially at any level of command, control, sensing, or actuation. The potential benefits of such a fault tolerant robotic control system include: (1) a minimized potential for damage to humans, the work site, and the robot itself; (2) continuous operation with a minimum of uncommanded motion in the presence of failures; and (3) more reliable autonomous operation providing increased efficiency in the execution of robotic tasks and decreased demand on human operators for controlling and monitoring the robotic servicing routines.

Biobotic insect swarm based sensor networks for search and rescue

NASA Astrophysics Data System (ADS)

Bozkurt, Alper; Lobaton, Edgar; Sichitiu, Mihail; Hedrick, Tyson; Latif, Tahmid; Dirafzoon, Alireza; Whitmire, Eric; Verderber, Alexander; Marin, Juan; Xiong, Hong

2014-06-01

The potential benefits of distributed robotics systems in applications requiring situational awareness, such as search-and-rescue in emergency situations, are indisputable. The efficiency of such systems requires robotic agents capable of coping with uncertain and dynamic environmental conditions. For example, after an earthquake, a tremendous effort is spent for days to reach to surviving victims where robotic swarms or other distributed robotic systems might play a great role in achieving this faster. However, current technology falls short of offering centimeter scale mobile agents that can function effectively under such conditions. Insects, the inspiration of many robotic swarms, exhibit an unmatched ability to navigate through such environments while successfully maintaining control and stability. We have benefitted from recent developments in neural engineering and neuromuscular stimulation research to fuse the locomotory advantages of insects with the latest developments in wireless networking technologies to enable biobotic insect agents to function as search-and-rescue agents. Our research efforts towards this goal include development of biobot electronic backpack technologies, establishment of biobot tracking testbeds to evaluate locomotion control efficiency, investigation of biobotic control strategies with Gromphadorhina portentosa cockroaches and Manduca sexta moths, establishment of a localization and communication infrastructure, modeling and controlling collective motion by learning deterministic and stochastic motion models, topological motion modeling based on these models, and the development of a swarm robotic platform to be used as a testbed for our algorithms.

Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

DTIC Science & Technology

2010-09-24

system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

Automation and Robotics for Space-Based Systems, 1991

NASA Technical Reports Server (NTRS)

Williams, Robert L., II (Editor)

1992-01-01

The purpose of this in-house workshop was to assess the state-of-the-art of automation and robotics for space operations from an LaRC perspective and to identify areas of opportunity for future research. Over half of the presentations came from the Automation Technology Branch, covering telerobotic control, extravehicular activity (EVA) and intra-vehicular activity (IVA) robotics, hand controllers for teleoperation, sensors, neural networks, and automated structural assembly, all applied to space missions. Other talks covered the Remote Manipulator System (RMS) active damping augmentation, space crane work, modeling, simulation, and control of large, flexible space manipulators, and virtual passive controller designs for space robots.

Material handling robot system for flow-through storage applications

NASA Astrophysics Data System (ADS)

Dill, James F.; Candiloro, Brian; Downer, James; Wiesman, Richard; Fallin, Larry; Smith, Ron

1999-01-01

This paper describes the design, development and planned implementation of a system of mobile robots for use in flow through storage applications. The robots are being designed with on-board embedded controls so that they can perform their tasks as semi-autonomous workers distributed within a centrally controlled network. On the storage input side, boxes will be identified by bar-codes and placed into preassigned flow through bins. On the shipping side, orders will be forwarded to the robots from a central order processing station and boxes will be picked from designated storage bins following proper sequencing to permit direct loading into trucks for shipping. Because of the need to maintain high system availability, a distributed control strategy has been selected. When completed, the system will permit robots to be dynamically reassigned responsibilities if an individual unit fails. On-board health diagnostics and condition monitoring will be used to maintain high reliability of the units.

Center of excellence for small robots

NASA Astrophysics Data System (ADS)

Nguyen, Hoa G.; Carroll, Daniel M.; Laird, Robin T.; Everett, H. R.

2005-05-01

The mission of the Unmanned Systems Branch of SPAWAR Systems Center, San Diego (SSC San Diego) is to provide network-integrated robotic solutions for Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) applications, serving and partnering with industry, academia, and other government agencies. We believe the most important criterion for a successful acquisition program is producing a value-added end product that the warfighter needs, uses and appreciates. Through our accomplishments in the laboratory and field, SSC San Diego has been designated the Center of Excellence for Small Robots by the Office of the Secretary of Defense Joint Robotics Program. This paper covers the background, experience, and collaboration efforts by SSC San Diego to serve as the "Impedance-Matching Transformer" between the robotic user and technical communities. Special attention is given to our Unmanned Systems Technology Imperatives for Research, Development, Testing and Evaluation (RDT&E) of Small Robots. Active projects, past efforts, and architectures are provided as success stories for the Unmanned Systems Development Approach.

A mobile robots experimental environment with event-based wireless communication.

PubMed

Guinaldo, María; Fábregas, Ernesto; Farias, Gonzalo; Dormido-Canto, Sebastián; Chaos, Dictino; Sánchez, José; Dormido, Sebastián

2013-07-22

An experimental platform to communicate between a set of mobile robots through a wireless network has been developed. The mobile robots get their position through a camera which performs as sensor. The video images are processed in a PC and a Waspmote card sends the corresponding position to each robot using the ZigBee standard. A distributed control algorithm based on event-triggered communications has been designed and implemented to bring the robots into the desired formation. Each robot communicates to its neighbors only at event times. Furthermore, a simulation tool has been developed to design and perform experiments with the system. An example of usage is presented.

Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method

NASA Astrophysics Data System (ADS)

Hsu, Roy CHaoming; Jian, Jhih-Wei; Lin, Chih-Chuan; Lai, Chien-Hung; Liu, Cheng-Ting

2013-01-01

The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.

An assembly system based on industrial robot with binocular stereo vision

NASA Astrophysics Data System (ADS)

Tang, Hong; Xiao, Nanfeng

2017-01-01

This paper proposes an electronic part and component assembly system based on an industrial robot with binocular stereo vision. Firstly, binocular stereo vision with a visual attention mechanism model is used to get quickly the image regions which contain the electronic parts and components. Secondly, a deep neural network is adopted to recognize the features of the electronic parts and components. Thirdly, in order to control the end-effector of the industrial robot to grasp the electronic parts and components, a genetic algorithm (GA) is proposed to compute the transition matrix and the inverse kinematics of the industrial robot (end-effector), which plays a key role in bridging the binocular stereo vision and the industrial robot. Finally, the proposed assembly system is tested in LED component assembly experiments, and the results denote that it has high efficiency and good applicability.

A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems.

PubMed

Wang, Lujia; Liu, Ming; Meng, Max Q-H

2017-02-01

Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point of failure. Robots need to continuously update intensive data to execute tasks in a coordinated manner, which implies real-time requirements. Thus, a resource allocation strategy is required, especially in such resource-constrained environments. This paper proposes a hierarchical auction-based mechanism, namely link quality matrix (LQM) auction, which is suitable for ad hoc networks by introducing a link quality indicator. The proposed algorithm produces a fast and robust method that is accurate and scalable. It reduces both global communication and unnecessary repeated computation. The proposed method is designed for firm real-time resource retrieval for physical multirobot systems. A joint surveillance scenario empirically validates the proposed mechanism by assessing several practical metrics. The results show that the proposed LQM auction outperforms state-of-the-art algorithms for resource allocation.

Heterogeneous Multi-Robot System for Mapping Environmental Variables of Greenhouses

PubMed Central

Roldán, Juan Jesús; Garcia-Aunon, Pablo; Garzón, Mario; de León, Jorge; del Cerro, Jaime; Barrientos, Antonio

2016-01-01

The productivity of greenhouses highly depends on the environmental conditions of crops, such as temperature and humidity. The control and monitoring might need large sensor networks, and as a consequence, mobile sensory systems might be a more suitable solution. This paper describes the application of a heterogeneous robot team to monitor environmental variables of greenhouses. The multi-robot system includes both ground and aerial vehicles, looking to provide flexibility and improve performance. The multi-robot sensory system measures the temperature, humidity, luminosity and carbon dioxide concentration in the ground and at different heights. Nevertheless, these measurements can be complemented with other ones (e.g., the concentration of various gases or images of crops) without a considerable effort. Additionally, this work addresses some relevant challenges of multi-robot sensory systems, such as the mission planning and task allocation, the guidance, navigation and control of robots in greenhouses and the coordination among ground and aerial vehicles. This work has an eminently practical approach, and therefore, the system has been extensively tested both in simulations and field experiments. PMID:27376297

Optimized Assistive Human-Robot Interaction Using Reinforcement Learning.

PubMed

Modares, Hamidreza; Ranatunga, Isura; Lewis, Frank L; Popa, Dan O

2016-03-01

An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x - y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

Mastering Mobile Security

ERIC Educational Resources Information Center

Panettieri, Joseph C.

2007-01-01

Without proper security, mobile devices are easy targets for worms, viruses, and so-called robot ("bot") networks. Hackers increasingly use bot networks to launch massive attacks against eCommerce websites--potentially targeting one's online tuition payment or fundraising/financial development systems. How can one defend his mobile systems against…

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications †

PubMed Central

Shao, Chenzhong; Tanaka, Shuji; Nakayama, Takahiro; Hata, Yoshiyuki; Bartley, Travis; Muroyama, Masanori

2017-01-01

Robot tactile sensation can enhance human–robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as “sensor platform LSI”) as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated. PMID:29061954

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications.

PubMed

Shao, Chenzhong; Tanaka, Shuji; Nakayama, Takahiro; Hata, Yoshiyuki; Bartley, Travis; Nonomura, Yutaka; Muroyama, Masanori

2017-08-28

Robot tactile sensation can enhance human-robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as "sensor platform LSI") as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated.

A stability-based mechanism for hysteresis in the walk–trot transition in quadruped locomotion

PubMed Central

Aoi, Shinya; Katayama, Daiki; Fujiki, Soichiro; Tomita, Nozomi; Funato, Tetsuro; Yamashita, Tsuyoshi; Senda, Kei; Tsuchiya, Kazuo

2013-01-01

Quadrupeds vary their gaits in accordance with their locomotion speed. Such gait transitions exhibit hysteresis. However, the underlying mechanism for this hysteresis remains largely unclear. It has been suggested that gaits correspond to attractors in their dynamics and that gait transitions are non-equilibrium phase transitions that are accompanied by a loss in stability. In the present study, we used a robotic platform to investigate the dynamic stability of gaits and to clarify the hysteresis mechanism in the walk–trot transition of quadrupeds. Specifically, we used a quadruped robot as the body mechanical model and an oscillator network for the nervous system model to emulate dynamic locomotion of a quadruped. Experiments using this robot revealed that dynamic interactions among the robot mechanical system, the oscillator network, and the environment generate walk and trot gaits depending on the locomotion speed. In addition, a walk–trot transition that exhibited hysteresis was observed when the locomotion speed was changed. We evaluated the gait changes of the robot by measuring the locomotion of dogs. Furthermore, we investigated the stability structure during the gait transition of the robot by constructing a potential function from the return map of the relative phase of the legs and clarified the physical characteristics inherent to the gait transition in terms of the dynamics. PMID:23389894

A stability-based mechanism for hysteresis in the walk-trot transition in quadruped locomotion.

PubMed

Aoi, Shinya; Katayama, Daiki; Fujiki, Soichiro; Tomita, Nozomi; Funato, Tetsuro; Yamashita, Tsuyoshi; Senda, Kei; Tsuchiya, Kazuo

2013-04-06

Quadrupeds vary their gaits in accordance with their locomotion speed. Such gait transitions exhibit hysteresis. However, the underlying mechanism for this hysteresis remains largely unclear. It has been suggested that gaits correspond to attractors in their dynamics and that gait transitions are non-equilibrium phase transitions that are accompanied by a loss in stability. In the present study, we used a robotic platform to investigate the dynamic stability of gaits and to clarify the hysteresis mechanism in the walk-trot transition of quadrupeds. Specifically, we used a quadruped robot as the body mechanical model and an oscillator network for the nervous system model to emulate dynamic locomotion of a quadruped. Experiments using this robot revealed that dynamic interactions among the robot mechanical system, the oscillator network, and the environment generate walk and trot gaits depending on the locomotion speed. In addition, a walk-trot transition that exhibited hysteresis was observed when the locomotion speed was changed. We evaluated the gait changes of the robot by measuring the locomotion of dogs. Furthermore, we investigated the stability structure during the gait transition of the robot by constructing a potential function from the return map of the relative phase of the legs and clarified the physical characteristics inherent to the gait transition in terms of the dynamics.

Calibration of an Outdoor Distributed Camera Network with a 3D Point Cloud

PubMed Central

Ortega, Agustín; Silva, Manuel; Teniente, Ernesto H.; Ferreira, Ricardo; Bernardino, Alexandre; Gaspar, José; Andrade-Cetto, Juan

2014-01-01

Outdoor camera networks are becoming ubiquitous in critical urban areas of the largest cities around the world. Although current applications of camera networks are mostly tailored to video surveillance, recent research projects are exploiting their use to aid robotic systems in people-assisting tasks. Such systems require precise calibration of the internal and external parameters of the distributed camera network. Despite the fact that camera calibration has been an extensively studied topic, the development of practical methods for user-assisted calibration that minimize user intervention time and maximize precision still pose significant challenges. These camera systems have non-overlapping fields of view, are subject to environmental stress, and are likely to suffer frequent recalibration. In this paper, we propose the use of a 3D map covering the area to support the calibration process and develop an automated method that allows quick and precise calibration of a large camera network. We present two cases of study of the proposed calibration method: one is the calibration of the Barcelona Robot Lab camera network, which also includes direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms. The second case consist of improving the GPS positioning of geo-tagged images taken with a mobile device in the Facultat de Matemàtiques i Estadística (FME) patio at the Universitat Politècnica de Catalunya (UPC). PMID:25076221

Calibration of an outdoor distributed camera network with a 3D point cloud.

PubMed

Ortega, Agustín; Silva, Manuel; Teniente, Ernesto H; Ferreira, Ricardo; Bernardino, Alexandre; Gaspar, José; Andrade-Cetto, Juan

2014-07-29

Outdoor camera networks are becoming ubiquitous in critical urban areas of the largest cities around the world. Although current applications of camera networks are mostly tailored to video surveillance, recent research projects are exploiting their use to aid robotic systems in people-assisting tasks. Such systems require precise calibration of the internal and external parameters of the distributed camera network. Despite the fact that camera calibration has been an extensively studied topic, the development of practical methods for user-assisted calibration that minimize user intervention time and maximize precision still pose significant challenges. These camera systems have non-overlapping fields of view, are subject to environmental stress, and are likely to suffer frequent recalibration. In this paper, we propose the use of a 3D map covering the area to support the calibration process and develop an automated method that allows quick and precise calibration of a large camera network. We present two cases of study of the proposed calibration method: one is the calibration of the Barcelona Robot Lab camera network, which also includes direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms. The second case consist of improving the GPS positioning of geo-tagged images taken with a mobile device in the Facultat de Matemàtiques i Estadística (FME) patio at the Universitat Politècnica de Catalunya (UPC).

Distributed communications and control network for robotic mining

NASA Technical Reports Server (NTRS)

Schiffbauer, William H.

1989-01-01

The application of robotics to coal mining machines is one approach pursued to increase productivity while providing enhanced safety for the coal miner. Toward that end, a network composed of microcontrollers, computers, expert systems, real time operating systems, and a variety of program languages are being integrated that will act as the backbone for intelligent machine operation. Actual mining machines, including a few customized ones, have been given telerobotic semiautonomous capabilities by applying the described network. Control devices, intelligent sensors and computers onboard these machines are showing promise of achieving improved mining productivity and safety benefits. Current research using these machines involves navigation, multiple machine interaction, machine diagnostics, mineral detection, and graphical machine representation. Guidance sensors and systems employed include: sonar, laser rangers, gyroscopes, magnetometers, clinometers, and accelerometers. Information on the network of hardware/software and its implementation on mining machines are presented. Anticipated coal production operations using the network are discussed. A parallelism is also drawn between the direction of present day underground coal mining research to how the lunar soil (regolith) may be mined. A conceptual lunar mining operation that employs a distributed communication and control network is detailed.

Dual Arm Work Package performance estimates and telerobot task network simulation

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

Draper, J.V.; Blair, L.M.

1997-02-01

This paper describes the methodology and results of a network simulation study of the Dual Arm Work Package (DAWP), to be employed for dismantling the Argonne National Laboratory CP-5 reactor. The development of the simulation model was based upon the results of a task analysis for the same system. This study was performed by the Oak Ridge National Laboratory (ORNL), in the Robotics and Process Systems Division. Funding was provided the US Department of Energy`s Office of Technology Development, Robotics Technology Development Program (RTDP). The RTDP is developing methods of computer simulation to estimate telerobotic system performance. Data were collectedmore » to provide point estimates to be used in a task network simulation model. Three skilled operators performed six repetitions of a pipe cutting task representative of typical teleoperation cutting operations.« less

A neuro-collision avoidance strategy for robot manipulators

NASA Technical Reports Server (NTRS)

Onema, Joel P.; Maclaunchlan, Robert A.

1992-01-01

The area of collision avoidance and path planning in robotics has received much attention in the research community. Our study centers on a combination of an artificial neural network paradigm with a motion planning strategy that insures safe motion of the Articulated Two-Link Arm with Scissor Hand System relative to an object. Whenever an obstacle is encountered, the arm attempts to slide along the obstacle surface, thereby avoiding collision by means of the local tangent strategy and its artificial neural network implementation. This combination compensates the inverse kinematics of a robot manipulator. Simulation results indicate that a neuro-collision avoidance strategy can be achieved by means of a learning local tangent method.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

PubMed Central

Milde, Moritz B.; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. PMID:28747883

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.

PubMed

Milde, Moritz B; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.

Using parallel evolutionary development for a biologically-inspired computer vision system for mobile robots.

PubMed

Wright, Cameron H G; Barrett, Steven F; Pack, Daniel J

2005-01-01

We describe a new approach to attacking the problem of robust computer vision for mobile robots. The overall strategy is to mimic the biological evolution of animal vision systems. Our basic imaging sensor is based upon the eye of the common house fly, Musca domestica. The computational algorithms are a mix of traditional image processing, subspace techniques, and multilayer neural networks.

Analyzing Cyber-Physical Threats on Robotic Platforms.

PubMed

Ahmad Yousef, Khalil M; AlMajali, Anas; Ghalyon, Salah Abu; Dweik, Waleed; Mohd, Bassam J

2018-05-21

Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking system is amenable of being attacked and vulnerable to the security threats. Ensuring security and privacy for robotic platforms is thus critical, as failures and attacks could have devastating consequences. In this paper, we examine several cyber-physical security threats that are unique to the robotic platforms; specifically the communication link and the applications. Threats target integrity, availability and confidential security requirements of the robotic platforms, which use MobileEyes/arnlServer client/server applications. A robot attack tool (RAT) was developed to perform specific security attacks. An impact-oriented approach was adopted to analyze the assessment results of the attacks. Tests and experiments of attacks were conducted in simulation environment and physically on the robot. The simulation environment was based on MobileSim; a software tool for simulating, debugging and experimenting on MobileRobots/ActivMedia platforms and their environments. The robot platform PeopleBot TM was used for physical experiments. The analysis and testing results show that certain attacks were successful at breaching the robot security. Integrity attacks modified commands and manipulated the robot behavior. Availability attacks were able to cause Denial-of-Service (DoS) and the robot was not responsive to MobileEyes commands. Integrity and availability attacks caused sensitive information on the robot to be hijacked. To mitigate security threats, we provide possible mitigation techniques and suggestions to raise awareness of threats on the robotic platforms, especially when the robots are involved in critical missions or applications.

Analyzing Cyber-Physical Threats on Robotic Platforms †

PubMed Central

2018-01-01

Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking system is amenable of being attacked and vulnerable to the security threats. Ensuring security and privacy for robotic platforms is thus critical, as failures and attacks could have devastating consequences. In this paper, we examine several cyber-physical security threats that are unique to the robotic platforms; specifically the communication link and the applications. Threats target integrity, availability and confidential security requirements of the robotic platforms, which use MobileEyes/arnlServer client/server applications. A robot attack tool (RAT) was developed to perform specific security attacks. An impact-oriented approach was adopted to analyze the assessment results of the attacks. Tests and experiments of attacks were conducted in simulation environment and physically on the robot. The simulation environment was based on MobileSim; a software tool for simulating, debugging and experimenting on MobileRobots/ActivMedia platforms and their environments. The robot platform PeopleBotTM was used for physical experiments. The analysis and testing results show that certain attacks were successful at breaching the robot security. Integrity attacks modified commands and manipulated the robot behavior. Availability attacks were able to cause Denial-of-Service (DoS) and the robot was not responsive to MobileEyes commands. Integrity and availability attacks caused sensitive information on the robot to be hijacked. To mitigate security threats, we provide possible mitigation techniques and suggestions to raise awareness of threats on the robotic platforms, especially when the robots are involved in critical missions or applications. PMID:29883403

A Mobile Robots Experimental Environment with Event-Based Wireless Communication

PubMed Central

Guinaldo, María; Fábregas, Ernesto; Farias, Gonzalo; Dormido-Canto, Sebastián; Chaos, Dictino; Sánchez, José; Dormido, Sebastián

2013-01-01

An experimental platform to communicate between a set of mobile robots through a wireless network has been developed. The mobile robots get their position through a camera which performs as sensor. The video images are processed in a PC and a Waspmote card sends the corresponding position to each robot using the ZigBee standard. A distributed control algorithm based on event-triggered communications has been designed and implemented to bring the robots into the desired formation. Each robot communicates to its neighbors only at event times. Furthermore, a simulation tool has been developed to design and perform experiments with the system. An example of usage is presented. PMID:23881139

Evolutionary Design of a Robotic Material Defect Detection System

NASA Technical Reports Server (NTRS)

Ballard, Gary; Howsman, Tom; Craft, Mike; ONeil, Daniel; Steincamp, Jim; Howell, Joe T. (Technical Monitor)

2002-01-01

During the post-flight inspection of SSME engines, several inaccessible regions must be disassembled to inspect for defects such as cracks, scratches, gouges, etc. An improvement to the inspection process would be the design and development of very small robots capable of penetrating these inaccessible regions and detecting the defects. The goal of this research was to utilize an evolutionary design approach for the robotic detection of these types of defects. A simulation and visualization tool was developed prior to receiving the hardware as a development test bed. A small, commercial off-the-shelf (COTS) robot was selected from several candidates as the proof of concept robot. The basic approach to detect the defects was to utilize Cadmium Sulfide (CdS) sensors to detect changes in contrast of an illuminated surface. A neural network, optimally designed utilizing a genetic algorithm, was employed to detect the presence of the defects (cracks). By utilization of the COTS robot and US sensors, the research successfully demonstrated that an evolutionarily designed neural network can detect the presence of surface defects.

Bridging the gap between motor imagery and motor execution with a brain-robot interface.

PubMed

Bauer, Robert; Fels, Meike; Vukelić, Mathias; Ziemann, Ulf; Gharabaghi, Alireza

2015-03-01

According to electrophysiological studies motor imagery and motor execution are associated with perturbations of brain oscillations over spatially similar cortical areas. By contrast, neuroimaging and lesion studies suggest that at least partially distinct cortical networks are involved in motor imagery and execution. We sought to further disentangle this relationship by studying the role of brain-robot interfaces in the context of motor imagery and motor execution networks. Twenty right-handed subjects performed several behavioral tasks as indicators for imagery and execution of movements of the left hand, i.e. kinesthetic imagery, visual imagery, visuomotor integration and tonic contraction. In addition, subjects performed motor imagery supported by haptic/proprioceptive feedback from a brain-robot-interface. Principal component analysis was applied to assess the relationship of these indicators. The respective cortical resting state networks in the α-range were investigated by electroencephalography using the phase slope index. We detected two distinct abilities and cortical networks underlying motor control: a motor imagery network connecting the left parietal and motor areas with the right prefrontal cortex and a motor execution network characterized by transmission from the left to right motor areas. We found that a brain-robot-interface might offer a way to bridge the gap between these networks, opening thereby a backdoor to the motor execution system. This knowledge might promote patient screening and may lead to novel treatment strategies, e.g. for the rehabilitation of hemiparesis after stroke. Copyright © 2014 Elsevier Inc. All rights reserved.

Leveraging Large-Scale Semantic Networks for Adaptive Robot Task Learning and Execution.

PubMed

Boteanu, Adrian; St Clair, Aaron; Mohseni-Kabir, Anahita; Saldanha, Carl; Chernova, Sonia

2016-12-01

This work seeks to leverage semantic networks containing millions of entries encoding assertions of commonsense knowledge to enable improvements in robot task execution and learning. The specific application we explore in this project is object substitution in the context of task adaptation. Humans easily adapt their plans to compensate for missing items in day-to-day tasks, substituting a wrap for bread when making a sandwich, or stirring pasta with a fork when out of spoons. Robot plan execution, however, is far less robust, with missing objects typically leading to failure if the robot is not aware of alternatives. In this article, we contribute a context-aware algorithm that leverages the linguistic information embedded in the task description to identify candidate substitution objects without reliance on explicit object affordance information. Specifically, we show that the task context provided by the task labels within the action structure of a task plan can be leveraged to disambiguate information within a noisy large-scale semantic network containing hundreds of potential object candidates to identify successful object substitutions with high accuracy. We present two extensive evaluations of our work on both abstract and real-world robot tasks, showing that the substitutions made by our system are valid, accepted by users, and lead to a statistically significant reduction in robot learning time. In addition, we report the outcomes of testing our approach with a large number of crowd workers interacting with a robot in real time.

Distributed Fault-Tolerant Control of Networked Uncertain Euler-Lagrange Systems Under Actuator Faults.

PubMed

Chen, Gang; Song, Yongduan; Lewis, Frank L

2016-05-03

This paper investigates the distributed fault-tolerant control problem of networked Euler-Lagrange systems with actuator and communication link faults. An adaptive fault-tolerant cooperative control scheme is proposed to achieve the coordinated tracking control of networked uncertain Lagrange systems on a general directed communication topology, which contains a spanning tree with the root node being the active target system. The proposed algorithm is capable of compensating for the actuator bias fault, the partial loss of effectiveness actuation fault, the communication link fault, the model uncertainty, and the external disturbance simultaneously. The control scheme does not use any fault detection and isolation mechanism to detect, separate, and identify the actuator faults online, which largely reduces the online computation and expedites the responsiveness of the controller. To validate the effectiveness of the proposed method, a test-bed of multiple robot-arm cooperative control system is developed for real-time verification. Experiments on the networked robot-arms are conduced and the results confirm the benefits and the effectiveness of the proposed distributed fault-tolerant control algorithms.

Olfaction and Hearing Based Mobile Robot Navigation for Odor/Sound Source Search

PubMed Central

Song, Kai; Liu, Qi; Wang, Qi

2011-01-01

Bionic technology provides a new elicitation for mobile robot navigation since it explores the way to imitate biological senses. In the present study, the challenging problem was how to fuse different biological senses and guide distributed robots to cooperate with each other for target searching. This paper integrates smell, hearing and touch to design an odor/sound tracking multi-robot system. The olfactory robot tracks the chemical odor plume step by step through information fusion from gas sensors and airflow sensors, while two hearing robots localize the sound source by time delay estimation (TDE) and the geometrical position of microphone array. Furthermore, this paper presents a heading direction based mobile robot navigation algorithm, by which the robot can automatically and stably adjust its velocity and direction according to the deviation between the current heading direction measured by magnetoresistive sensor and the expected heading direction acquired through the odor/sound localization strategies. Simultaneously, one robot can communicate with the other robots via a wireless sensor network (WSN). Experimental results show that the olfactory robot can pinpoint the odor source within the distance of 2 m, while two hearing robots can quickly localize and track the olfactory robot in 2 min. The devised multi-robot system can achieve target search with a considerable success ratio and high stability. PMID:22319401

Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement.

PubMed

Yue, Shigang; Rind, F Claire

2006-05-01

The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by background detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds.

SafeNet: a methodology for integrating general-purpose unsafe devices in safe-robot rehabilitation systems.

PubMed

Vicentini, Federico; Pedrocchi, Nicola; Malosio, Matteo; Molinari Tosatti, Lorenzo

2014-09-01

Robot-assisted neurorehabilitation often involves networked systems of sensors ("sensory rooms") and powerful devices in physical interaction with weak users. Safety is unquestionably a primary concern. Some lightweight robot platforms and devices designed on purpose include safety properties using redundant sensors or intrinsic safety design (e.g. compliance and backdrivability, limited exchange of energy). Nonetheless, the entire "sensory room" shall be required to be fail-safe and safely monitored as a system at large. Yet, sensor capabilities and control algorithms used in functional therapies require, in general, frequent updates or re-configurations, making a safety-grade release of such devices hardly sustainable in cost-effectiveness and development time. As such, promising integrated platforms for human-in-the-loop therapies could not find clinical application and manufacturing support because of lacking in the maintenance of global fail-safe properties. Under the general context of cross-machinery safety standards, the paper presents a methodology called SafeNet for helping in extending the safety rate of Human Robot Interaction (HRI) systems using unsafe components, including sensors and controllers. SafeNet considers, in fact, the robotic system as a device at large and applies the principles of functional safety (as in ISO 13489-1) through a set of architectural procedures and implementation rules. The enabled capability of monitoring a network of unsafe devices through redundant computational nodes, allows the usage of any custom sensors and algorithms, usually planned and assembled at therapy planning-time rather than at platform design-time. A case study is presented with an actual implementation of the proposed methodology. A specific architectural solution is applied to an example of robot-assisted upper-limb rehabilitation with online motion tracking. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Vision-based mobile robot navigation through deep convolutional neural networks and end-to-end learning

NASA Astrophysics Data System (ADS)

Zhang, Yachu; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Kong, Lingqin; Liu, Lingling

2017-09-01

In contrast to humans, who use only visual information for navigation, many mobile robots use laser scanners and ultrasonic sensors along with vision cameras to navigate. This work proposes a vision-based robot control algorithm based on deep convolutional neural networks. We create a large 15-layer convolutional neural network learning system and achieve the advanced recognition performance. Our system is trained from end to end to map raw input images to direction in supervised mode. The images of data sets are collected in a wide variety of weather conditions and lighting conditions. Besides, the data sets are augmented by adding Gaussian noise and Salt-and-pepper noise to avoid overfitting. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance. The line tracking experiment is proceeded in order to track the desired path which is composed of straight and curved lines. The goal of obstacle avoidance experiment is to avoid the obstacles indoor. Finally, we get 3.29% error rate on the training set and 5.1% error rate on the test set in the line tracking experiment, 1.8% error rate on the training set and less than 5% error rate on the test set in the obstacle avoidance experiment. During the actual test, the robot can follow the runway centerline outdoor and avoid the obstacle in the room accurately. The result confirms the effectiveness of the algorithm and our improvement in the network structure and train parameters

Medical Engineering and Microneurosurgery: Application and Future.

PubMed

Morita, Akio; Sora, Shigeo; Nakatomi, Hirofumi; Harada, Kanako; Sugita, Naohiko; Saito, Nobuhito; Mitsuishi, Mamoru

2016-10-15

Robotics and medical engineering can convert traditional surgery into digital and scientific procedures. Here, we describe our work to develop microsurgical robotic systems and apply engineering technology to assess microsurgical skills. With the collaboration of neurosurgeons and an engineering team, we have developed two types of microsurgical robotic systems. The first, the deep surgical systems, enable delicate surgical procedures such as vessel suturing in a deep and narrow space. The second type allows for super-fine surgical procedures such as anastomosing artificial vessels of 0.3 mm in diameter. Both systems are constructed with master and slave manipulator robots connected to local area networks. Robotic systems allowed for secure and accurate procedures in a deep surgical field. In cadaveric models, these systems showed a good potential of being useful in actual human surgeries, but mechanical refinements in thickness and durability are necessary for them to be established as clinical systems. The super-fine robotic system made the very intricate surgery possible and will be applied in clinical trials. Another trial included the digitization of surgical technique and scientific analysis of surgical skills. Robotic and human hand motions were analyzed in numerical fashion as we tried to define surgical skillfulness in a digital format. Engineered skill assessment is also feasible and should be useful for microsurgical training. Robotics and medical engineering should bring science into the surgical field and training of surgeons. Active collaboration between medical and engineering teams and academic and industry groups is mandatory to establish such medical systems to improve patient care.

Medical Engineering and Microneurosurgery: Application and Future

PubMed Central

MORITA, Akio; SORA, Shigeo; NAKATOMI, Hirofumi; HARADA, Kanako; SUGITA, Naohiko; SAITO, Nobuhito; MITSUISHI, Mamoru

2016-01-01

Robotics and medical engineering can convert traditional surgery into digital and scientific procedures. Here, we describe our work to develop microsurgical robotic systems and apply engineering technology to assess microsurgical skills. With the collaboration of neurosurgeons and an engineering team, we have developed two types of microsurgical robotic systems. The first, the deep surgical systems, enable delicate surgical procedures such as vessel suturing in a deep and narrow space. The second type allows for super-fine surgical procedures such as anastomosing artificial vessels of 0.3 mm in diameter. Both systems are constructed with master and slave manipulator robots connected to local area networks. Robotic systems allowed for secure and accurate procedures in a deep surgical field. In cadaveric models, these systems showed a good potential of being useful in actual human surgeries, but mechanical refinements in thickness and durability are necessary for them to be established as clinical systems. The super-fine robotic system made the very intricate surgery possible and will be applied in clinical trials. Another trial included the digitization of surgical technique and scientific analysis of surgical skills. Robotic and human hand motions were analyzed in numerical fashion as we tried to define surgical skillfulness in a digital format. Engineered skill assessment is also feasible and should be useful for microsurgical training. Robotics and medical engineering should bring science into the surgical field and training of surgeons. Active collaboration between medical and engineering teams and academic and industry groups is mandatory to establish such medical systems to improve patient care. PMID:27464471

Open core control software for surgical robots.

PubMed

Arata, Jumpei; Kozuka, Hiroaki; Kim, Hyung Wook; Takesue, Naoyuki; Vladimirov, B; Sakaguchi, Masamichi; Tokuda, Junichi; Hata, Nobuhiko; Chinzei, Kiyoyuki; Fujimoto, Hideo

2010-05-01

In these days, patients and doctors in operation room are surrounded by many medical devices as resulting from recent advancement of medical technology. However, these cutting-edge medical devices are working independently and not collaborating with each other, even though the collaborations between these devices such as navigation systems and medical imaging devices are becoming very important for accomplishing complex surgical tasks (such as a tumor removal procedure while checking the tumor location in neurosurgery). On the other hand, several surgical robots have been commercialized, and are becoming common. However, these surgical robots are not open for collaborations with external medical devices in these days. A cutting-edge "intelligent surgical robot" will be possible in collaborating with surgical robots, various kinds of sensors, navigation system and so on. On the other hand, most of the academic software developments for surgical robots are "home-made" in their research institutions and not open to the public. Therefore, open source control software for surgical robots can be beneficial in this field. From these perspectives, we developed Open Core Control software for surgical robots to overcome these challenges. In general, control softwares have hardware dependencies based on actuators, sensors and various kinds of internal devices. Therefore, these control softwares cannot be used on different types of robots without modifications. However, the structure of the Open Core Control software can be reused for various types of robots by abstracting hardware dependent parts. In addition, network connectivity is crucial for collaboration between advanced medical devices. The OpenIGTLink is adopted in Interface class which plays a role to communicate with external medical devices. At the same time, it is essential to maintain the stable operation within the asynchronous data transactions through network. In the Open Core Control software, several techniques for this purpose were introduced. Virtual fixture is well known technique as a "force guide" for supporting operators to perform precise manipulation by using a master-slave robot. The virtual fixture for precise and safety surgery was implemented on the system to demonstrate an idea of high-level collaboration between a surgical robot and a navigation system. The extension of virtual fixture is not a part of the Open Core Control system, however, the function such as virtual fixture cannot be realized without a tight collaboration between cutting-edge medical devices. By using the virtual fixture, operators can pre-define an accessible area on the navigation system, and the area information can be transferred to the robot. In this manner, the surgical console generates the reflection force when the operator tries to get out from the pre-defined accessible area during surgery. The Open Core Control software was implemented on a surgical master-slave robot and stable operation was observed in a motion test. The tip of the surgical robot was displayed on a navigation system by connecting the surgical robot with a 3D position sensor through the OpenIGTLink. The accessible area was pre-defined before the operation, and the virtual fixture was displayed as a "force guide" on the surgical console. In addition, the system showed stable performance in a duration test with network disturbance. In this paper, a design of the Open Core Control software for surgical robots and the implementation of virtual fixture were described. The Open Core Control software was implemented on a surgical robot system and showed stable performance in high-level collaboration works. The Open Core Control software is developed to be a widely used platform of surgical robots. Safety issues are essential for control software of these complex medical devices. It is important to follow the global specifications such as a FDA requirement "General Principles of Software Validation" or IEC62304. For following these regulations, it is important to develop a self-test environment. Therefore, a test environment is now under development to test various interference in operation room such as a noise of electric knife by considering safety and test environment regulations such as ISO13849 and IEC60508. The Open Core Control software is currently being developed software in open-source manner and available on the Internet. A communization of software interface is becoming a major trend in this field. Based on this perspective, the Open Core Control software can be expected to bring contributions in this field.

Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection.

PubMed

Sarikaya, Duygu; Corso, Jason J; Guru, Khurshid A

2017-07-01

Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.

Robot Tracer with Visual Camera

NASA Astrophysics Data System (ADS)

Jabbar Lubis, Abdul; Dwi Lestari, Yuyun; Dafitri, Haida; Azanuddin

2017-12-01

Robot is a versatile tool that can function replace human work function. The robot is a device that can be reprogrammed according to user needs. The use of wireless networks for remote monitoring needs can be utilized to build a robot that can be monitored movement and can be monitored using blueprints and he can track the path chosen robot. This process is sent using a wireless network. For visual robot using high resolution cameras to facilitate the operator to control the robot and see the surrounding circumstances.

Method for neural network control of motion using real-time environmental feedback

NASA Technical Reports Server (NTRS)

Buckley, Theresa M. (Inventor)

1997-01-01

A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

PubMed Central

Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A.; Przekwas, Andrzej; Francis, Joseph T.; Lytton, William W.

2015-01-01

Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics. PMID:26635598

Aerial robot intelligent control method based on back-stepping

NASA Astrophysics Data System (ADS)

Zhou, Jian; Xue, Qian

2018-05-01

The aerial robot is characterized as strong nonlinearity, high coupling and parameter uncertainty, a self-adaptive back-stepping control method based on neural network is proposed in this paper. The uncertain part of the aerial robot model is compensated online by the neural network of Cerebellum Model Articulation Controller and robust control items are designed to overcome the uncertainty error of the system during online learning. At the same time, particle swarm algorithm is used to optimize and fix parameters so as to improve the dynamic performance, and control law is obtained by the recursion of back-stepping regression. Simulation results show that the designed control law has desired attitude tracking performance and good robustness in case of uncertainties and large errors in the model parameters.

A robotic home telehealth platform system for treatment adherence, social assistance and companionship - an overview.

PubMed

Oddsson, Lars I E; Radomski, Mary V; White, Matthew; Nilsson, Daniel

2009-01-01

Well-known difficulties of making patients adhere to assigned treatments have made engineers and clinicians look towards technology for possible solutions. Recent studies have found that cell phone-based text messaging can help drive positive changes in patients' disease management and preventive health behavior. Furthermore, work in the area of assistive robotics indicates benefits for patients although robotic solutions tend to become expensive. However, continued improvement in sensor, computer and wireless technologies combined with decreases in cost is paving the way for development of affordable robotic systems that can help improve patient care and potentially add value to the healthcare system. This paper provides a high-level design overview of SKOTEE, the Sister Kenny hOme ThErapy systEm, an inexpensive robotic platform system designed to provide adherence support for home exercise programs, taking medication, appointment reminders and clinician communication. SKOTEE will also offer companionship as well as entertainment and social networking opportunities to the patient in their home. A video of the system is presented at the conference.

Self-organization via active exploration in robotic applications

NASA Technical Reports Server (NTRS)

Ogmen, H.; Prakash, R. V.

1992-01-01

We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization of sequence of actions.

Maintaining Limited-Range Connectivity Among Second-Order Agents

DTIC Science & Technology

2016-07-07

we consider ad-hoc networks of robotic agents with double integrator dynamics. For such networks, the connectivity maintenance problems are: (i) do...hoc networks of mobile autonomous agents. This loose ter- minology refers to groups of robotic agents with limited mobility and communica- tion...connectivity can be preserved. 3.1. Networks of robotic agents with second-order dynamics and the connectivity maintenance problem. We begin by

Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments

PubMed Central

Yao, Yao; Marchal, Kathleen; Van de Peer, Yves

2014-01-01

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485

Experiments in Neural-Network Control of a Free-Flying Space Robot

NASA Technical Reports Server (NTRS)

Wilson, Edward

1995-01-01

Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

LABRADOR: a learning autonomous behavior-based robot for adaptive detection and object retrieval

NASA Astrophysics Data System (ADS)

Yamauchi, Brian; Moseley, Mark; Brookshire, Jonathan

2013-01-01

As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies - including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.

Self-organization via active exploration in robotic applications. Phase 2: Hybrid hardware prototype

NASA Technical Reports Server (NTRS)

Oegmen, Haluk

1993-01-01

In many environments human-like intelligent behavior is required from robots to assist and/or replace human operators. The purpose of these robots is to reduce human time and effort in various tasks. Thus the robot should be robust and as autonomous as possible in order to eliminate or to keep to a strict minimum its maintenance and external control. Such requirements lead to the following properties: fault tolerance, self organization, and intelligence. A good insight into implementing these properties in a robot can be gained by considering human behavior. In the first phase of this project, a neural network architecture was developed that captures some fundamental aspects of human categorization, habit, novelty, and reinforcement behavior. The model, called FRONTAL, is a 'cognitive unit' regulating the exploratory behavior of the robot. In the second phase of the project, FRONTAL was interfaced with an off-the-shelf robotic arm and a real-time vision system. The components of this robotic system, a review of FRONTAL, and simulation studies are presented in this report.

Feedback Error Learning Controller for Functional Electrical Stimulation Assistance in a Hybrid Robotic System for Reaching Rehabilitation

PubMed Central

Resquín, Francisco; Gonzalez-Vargas, Jose; Ibáñez, Jaime; Brunetti, Fernando; Pons, José Luis

2016-01-01

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model. PMID:27990245

Multi-Robot Systems in Military Domains (Les Systemes Multi-Robots Dans les Domaines Militaires)

DTIC Science & Technology

2008-12-01

to allow him to react quickly to improve his personal safety , it is mandatory to shorten the current very long delay needed for the human operator to...Hard RT tasks 2 OS / API Process monitoring 3 H / API Flexible communication medium 4 H / API Networking capabilities 5 H / API Safety 6 API...also be considered between high level services and legacy systems. 4) This is the one of the basic requirement for CoRoDe. 5) Safety : CRC, Timeouts

Rapid Human-Computer Interactive Conceptual Design of Mobile and Manipulative Robot Systems

DTIC Science & Technology

2015-05-19

algorithm based on Age-Fitness Pareto Optimization (AFPO) ([9]) with an additional user prefer- ence objective and a neural network-based user model, we...greater than 40, which is about 5 times further than any robot traveled in our experiments. 6 3.3 Methods The algorithm uses a client -server computational...architecture. The client here is an interactive pro- gram which takes a pair of controllers as input, simulates4 two copies of the robot with

Neural associative memories for the integration of language, vision and action in an autonomous agent.

PubMed

Markert, H; Kaufmann, U; Kara Kayikci, Z; Palm, G

2009-03-01

Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms.

Piezoelectrically Actuated Robotic System for MRI-Guided Prostate Percutaneous Therapy

PubMed Central

Su, Hao; Shang, Weijian; Cole, Gregory; Li, Gang; Harrington, Kevin; Camilo, Alexander; Tokuda, Junichi; Tempany, Clare M.; Hata, Nobuhiko; Fischer, Gregory S.

2014-01-01

This paper presents a fully-actuated robotic system for percutaneous prostate therapy under continuously acquired live magnetic resonance imaging (MRI) guidance. The system is composed of modular hardware and software to support the surgical workflow of intra-operative MRI-guided surgical procedures. We present the development of a 6-degree-of-freedom (DOF) needle placement robot for transperineal prostate interventions. The robot consists of a 3-DOF needle driver module and a 3-DOF Cartesian motion module. The needle driver provides needle cannula translation and rotation (2-DOF) and stylet translation (1-DOF). A custom robot controller consisting of multiple piezoelectric motor drivers provides precision closed-loop control of piezoelectric motors and enables simultaneous robot motion and MR imaging. The developed modular robot control interface software performs image-based registration, kinematics calculation, and exchanges robot commands and coordinates between the navigation software and the robot controller with a new implementation of the open network communication protocol OpenIGTLink. Comprehensive compatibility of the robot is evaluated inside a 3-Tesla MRI scanner using standard imaging sequences and the signal-to-noise ratio (SNR) loss is limited to 15%. The image deterioration due to the present and motion of robot demonstrates unobservable image interference. Twenty-five targeted needle placements inside gelatin phantoms utilizing an 18-gauge ceramic needle demonstrated 0.87 mm root mean square (RMS) error in 3D Euclidean distance based on MRI volume segmentation of the image-guided robotic needle placement procedure. PMID:26412962

Design and Implementation of Sound Searching Robots in Wireless Sensor Networks

PubMed Central

Han, Lianfu; Shen, Zhengguang; Fu, Changfeng; Liu, Chao

2016-01-01

A sound target-searching robot system which includes a 4-channel microphone array for sound collection, magneto-resistive sensor for declination measurement, and a wireless sensor networks (WSN) for exchanging information is described. It has an embedded sound signal enhancement, recognition and location method, and a sound searching strategy based on a digital signal processor (DSP). As the wireless network nodes, three robots comprise the WSN a personal computer (PC) in order to search the three different sound targets in task-oriented collaboration. The improved spectral subtraction method is used for noise reduction. As the feature of audio signal, Mel-frequency cepstral coefficient (MFCC) is extracted. Based on the K-nearest neighbor classification method, we match the trained feature template to recognize sound signal type. This paper utilizes the improved generalized cross correlation method to estimate time delay of arrival (TDOA), and then employs spherical-interpolation for sound location according to the TDOA and the geometrical position of the microphone array. A new mapping has been proposed to direct the motor to search sound targets flexibly. As the sink node, the PC receives and displays the result processed in the WSN, and it also has the ultimate power to make decision on the received results in order to improve their accuracy. The experiment results show that the designed three-robot system implements sound target searching function without collisions and performs well. PMID:27657088

Design and Implementation of Sound Searching Robots in Wireless Sensor Networks.

PubMed

Han, Lianfu; Shen, Zhengguang; Fu, Changfeng; Liu, Chao

2016-09-21

A sound target-searching robot system which includes a 4-channel microphone array for sound collection, magneto-resistive sensor for declination measurement, and a wireless sensor networks (WSN) for exchanging information is described. It has an embedded sound signal enhancement, recognition and location method, and a sound searching strategy based on a digital signal processor (DSP). As the wireless network nodes, three robots comprise the WSN a personal computer (PC) in order to search the three different sound targets in task-oriented collaboration. The improved spectral subtraction method is used for noise reduction. As the feature of audio signal, Mel-frequency cepstral coefficient (MFCC) is extracted. Based on the K-nearest neighbor classification method, we match the trained feature template to recognize sound signal type. This paper utilizes the improved generalized cross correlation method to estimate time delay of arrival (TDOA), and then employs spherical-interpolation for sound location according to the TDOA and the geometrical position of the microphone array. A new mapping has been proposed to direct the motor to search sound targets flexibly. As the sink node, the PC receives and displays the result processed in the WSN, and it also has the ultimate power to make decision on the received results in order to improve their accuracy. The experiment results show that the designed three-robot system implements sound target searching function without collisions and performs well.

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots.

PubMed

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

2017-03-11

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

Non-equilibrium assembly of microtubules: from molecules to autonomous chemical robots.

PubMed

Hess, H; Ross, Jennifer L

2017-09-18

Biological systems have evolved to harness non-equilibrium processes from the molecular to the macro scale. It is currently a grand challenge of chemistry, materials science, and engineering to understand and mimic biological systems that have the ability to autonomously sense stimuli, process these inputs, and respond by performing mechanical work. New chemical systems are responding to the challenge and form the basis for future responsive, adaptive, and active materials. In this article, we describe a particular biochemical-biomechanical network based on the microtubule cytoskeletal filament - itself a non-equilibrium chemical system. We trace the non-equilibrium aspects of the system from molecules to networks and describe how the cell uses this system to perform active work in essential processes. Finally, we discuss how microtubule-based engineered systems can serve as testbeds for autonomous chemical robots composed of biological and synthetic components.

Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

NASA Technical Reports Server (NTRS)

Tawel, Raoul (Inventor)

1994-01-01

A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

A Step Towards Developing Adaptive Robot-Mediated Intervention Architecture (ARIA) for Children With Autism

PubMed Central

Bekele, Esubalew T; Lahiri, Uttama; Swanson, Amy R.; Crittendon, Julie A.; Warren, Zachary E.; Sarkar, Nilanjan

2013-01-01

Emerging technology, especially robotic technology, has been shown to be appealing to children with autism spectrum disorders (ASD). Such interest may be leveraged to provide repeatable, accurate and individualized intervention services to young children with ASD based on quantitative metrics. However, existing robot-mediated systems tend to have limited adaptive capability that may impact individualization. Our current work seeks to bridge this gap by developing an adaptive and individualized robot-mediated technology for children with ASD. The system is composed of a humanoid robot with its vision augmented by a network of cameras for real-time head tracking using a distributed architecture. Based on the cues from the child’s head movement, the robot intelligently adapts itself in an individualized manner to generate prompts and reinforcements with potential to promote skills in the ASD core deficit area of early social orienting. The system was validated for feasibility, accuracy, and performance. Results from a pilot usability study involving six children with ASD and a control group of six typically developing (TD) children are presented. PMID:23221831

An Intelligent Agent Approach for Teaching Neural Networks Using LEGO[R] Handy Board Robots

ERIC Educational Resources Information Center

Imberman, Susan P.

2004-01-01

In this article we describe a project for an undergraduate artificial intelligence class. The project teaches neural networks using LEGO[R] handy board robots. Students construct robots with two motors and two photosensors. Photosensors provide readings that act as inputs for the neural network. Output values power the motors and maintain the…

General visual robot controller networks via artificial evolution

NASA Astrophysics Data System (ADS)

Cliff, David; Harvey, Inman; Husbands, Philip

1993-08-01

We discuss recent results from our ongoing research concerning the application of artificial evolution techniques (i.e., an extended form of genetic algorithm) to the problem of developing `neural' network controllers for visually guided robots. The robot is a small autonomous vehicle with extremely low-resolution vision, employing visual sensors which could readily be constructed from discrete analog components. In addition to visual sensing, the robot is equipped with a small number of mechanical tactile sensors. Activity from the sensors is fed to a recurrent dynamical artificial `neural' network, which acts as the robot controller, providing signals to motors governing the robot's motion. Prior to presentation of new results, this paper summarizes our rationale and past work, which has demonstrated that visually guided control networks can arise without any explicit specification that visual processing should be employed: the evolutionary process opportunistically makes use of visual information if it is available.

Cerebellum-inspired neural network solution of the inverse kinematics problem.

PubMed

Asadi-Eydivand, Mitra; Ebadzadeh, Mohammad Mehdi; Solati-Hashjin, Mehran; Darlot, Christian; Abu Osman, Noor Azuan

2015-12-01

The demand today for more complex robots that have manipulators with higher degrees of freedom is increasing because of technological advances. Obtaining the precise movement for a desired trajectory or a sequence of arm and positions requires the computation of the inverse kinematic (IK) function, which is a major problem in robotics. The solution of the IK problem leads robots to the precise position and orientation of their end-effector. We developed a bioinspired solution comparable with the cerebellar anatomy and function to solve the said problem. The proposed model is stable under all conditions merely by parameter determination, in contrast to recursive model-based solutions, which remain stable only under certain conditions. We modified the proposed model for the simple two-segmented arm to prove the feasibility of the model under a basic condition. A fuzzy neural network through its learning method was used to compute the parameters of the system. Simulation results show the practical feasibility and efficiency of the proposed model in robotics. The main advantage of the proposed model is its generalizability and potential use in any robot.

Safety Verification of a Fault Tolerant Reconfigurable Autonomous Goal-Based Robotic Control System

NASA Technical Reports Server (NTRS)

Braman, Julia M. B.; Murray, Richard M; Wagner, David A.

2007-01-01

Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called Mission Data System (MDS), developed at the Jet Propulsion Laboratory, takes a goal-based control approach. In this paper, a method for converting goal network control programs into linear hybrid systems is developed. The linear hybrid system can then be verified for safety in the presence of failures using existing symbolic model checkers. An example task is simulated in MDS and successfully verified using HyTech, a symbolic model checking software for linear hybrid systems.

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

PubMed Central

Dülger, L. Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

PubMed

Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

Jerk-level synchronous repetitive motion scheme with gradient-type and zeroing-type dynamics algorithms applied to dual-arm redundant robot system control

NASA Astrophysics Data System (ADS)

Chen, Dechao; Zhang, Yunong

2017-10-01

Dual-arm redundant robot systems are usually required to handle primary tasks, repetitively and synchronously in practical applications. In this paper, a jerk-level synchronous repetitive motion scheme is proposed to remedy the joint-angle drift phenomenon and achieve the synchronous control of a dual-arm redundant robot system. The proposed scheme is novelly resolved at jerk level, which makes the joint variables, i.e. joint angles, joint velocities and joint accelerations, smooth and bounded. In addition, two types of dynamics algorithms, i.e. gradient-type (G-type) and zeroing-type (Z-type) dynamics algorithms, for the design of repetitive motion variable vectors, are presented in detail with the corresponding circuit schematics. Subsequently, the proposed scheme is reformulated as two dynamical quadratic programs (DQPs) and further integrated into a unified DQP (UDQP) for the synchronous control of a dual-arm robot system. The optimal solution of the UDQP is found by the piecewise-linear projection equation neural network. Moreover, simulations and comparisons based on a six-degrees-of-freedom planar dual-arm redundant robot system substantiate the operation effectiveness and tracking accuracy of the robot system with the proposed scheme for repetitive motion and synchronous control.

Control of intelligent robots in space

NASA Technical Reports Server (NTRS)

Freund, E.; Buehler, CH.

1989-01-01

In view of space activities like International Space Station, Man-Tended-Free-Flyer (MTFF) and free flying platforms, the development of intelligent robotic systems is gaining increasing importance. The range of applications that have to be performed by robotic systems in space includes e.g., the execution of experiments in space laboratories, the service and maintenance of satellites and flying platforms, the support of automatic production processes or the assembly of large network structures. Some of these tasks will require the development of bi-armed or of multiple robotic systems including functional redundancy. For the development of robotic systems which are able to perform this variety of tasks a hierarchically structured modular concept of automation is required. This concept is characterized by high flexibility as well as by automatic specialization to the particular sequence of tasks that have to be performed. On the other hand it has to be designed such that the human operator can influence or guide the system on different levels of control supervision, and decision. This leads to requirements for the hardware and software concept which permit a range of application of the robotic systems from telemanipulation to autonomous operation. The realization of this goal requires strong efforts in the development of new methods, software and hardware concepts, and the integration into an automation concept.

Portable control device for networked mobile robots

DOEpatents

Feddema, John T.; Byrne, Raymond H.; Bryan, Jon R.; Harrington, John J.; Gladwell, T. Scott

2002-01-01

A handheld control device provides a way for controlling one or multiple mobile robotic vehicles by incorporating a handheld computer with a radio board. The device and software use a personal data organizer as the handheld computer with an additional microprocessor and communication device on a radio board for use in controlling one robot or multiple networked robots.

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Dynamic electronic institutions in agent oriented cloud robotic systems.

PubMed

Nagrath, Vineet; Morel, Olivier; Malik, Aamir; Saad, Naufal; Meriaudeau, Fabrice

2015-01-01

The dot-com bubble bursted in the year 2000 followed by a swift movement towards resource virtualization and cloud computing business model. Cloud computing emerged not as new form of computing or network technology but a mere remoulding of existing technologies to suit a new business model. Cloud robotics is understood as adaptation of cloud computing ideas for robotic applications. Current efforts in cloud robotics stress upon developing robots that utilize computing and service infrastructure of the cloud, without debating on the underlying business model. HTM5 is an OMG's MDA based Meta-model for agent oriented development of cloud robotic systems. The trade-view of HTM5 promotes peer-to-peer trade amongst software agents. HTM5 agents represent various cloud entities and implement their business logic on cloud interactions. Trade in a peer-to-peer cloud robotic system is based on relationships and contracts amongst several agent subsets. Electronic Institutions are associations of heterogeneous intelligent agents which interact with each other following predefined norms. In Dynamic Electronic Institutions, the process of formation, reformation and dissolution of institutions is automated leading to run time adaptations in groups of agents. DEIs in agent oriented cloud robotic ecosystems bring order and group intellect. This article presents DEI implementations through HTM5 methodology.

Patent Network Analysis and Quadratic Assignment Procedures to Identify the Convergence of Robot Technologies

PubMed Central

Lee, Woo Jin; Lee, Won Kyung

2016-01-01

Because of the remarkable developments in robotics in recent years, technological convergence has been active in this area. We focused on finding patterns of convergence within robot technology using network analysis of patents in both the USPTO and KIPO. To identify the variables that affect convergence, we used quadratic assignment procedures (QAP). From our analysis, we observed the patent network ecology related to convergence and found technologies that have great potential to converge with other robotics technologies. The results of our study are expected to contribute to setting up convergence based R&D policies for robotics, which can lead new innovation. PMID:27764196

Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions

PubMed Central

Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

2017-01-01

An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs) that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as “not,” “and,” and “or” simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human–robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as “true,” “false,” and “not” work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word “and,” which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word “or,” which required action generation that looked apparently random, was represented as an unstable space of the network's dynamical system. PMID:29311891

Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions.

PubMed

Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

2017-01-01

An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs) that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as "not," "and," and "or" simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human-robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as "true," "false," and "not" work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word "and," which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word "or," which required action generation that looked apparently random, was represented as an unstable space of the network's dynamical system.

Adaptive parallel logic networks

NASA Technical Reports Server (NTRS)

Martinez, Tony R.; Vidal, Jacques J.

1988-01-01

Adaptive, self-organizing concurrent systems (ASOCS) that combine self-organization with massive parallelism for such applications as adaptive logic devices, robotics, process control, and system malfunction management, are presently discussed. In ASOCS, an adaptive network composed of many simple computing elements operating in combinational and asynchronous fashion is used and problems are specified by presenting if-then rules to the system in the form of Boolean conjunctions. During data processing, which is a different operational phase from adaptation, the network acts as a parallel hardware circuit.

Data management for biofied building

NASA Astrophysics Data System (ADS)

Matsuura, Kohta; Mita, Akira

2015-03-01

Recently, Smart houses have been studied by many researchers to satisfy individual demands of residents. However, they are not feasible yet as they are very costly and require many sensors to be embedded into houses. Therefore, we suggest "Biofied Building". In Biofied Building, sensor agent robots conduct sensing, actuation, and control in their house. The robots monitor many parameters of human lives such as walking postures and emotion continuously. In this paper, a prototype network system and a data model for practical application for Biofied Building is pro-posed. In the system, functions of robots and servers are divided according to service flows in Biofield Buildings. The data model is designed to accumulate both the building data and the residents' data. Data sent from the robots and data analyzed in the servers are automatically registered into the database. Lastly, feasibility of this system is verified through lighting control simulation performed in an office space.

Obstacle negotiation control for a mobile robot suspended on overhead ground wires by optoelectronic sensors

NASA Astrophysics Data System (ADS)

Zheng, Li; Yi, Ruan

2009-11-01

Power line inspection and maintenance already benefit from developments in mobile robotics. This paper presents mobile robots capable of crossing obstacles on overhead ground wires. A teleoperated robot realizes inspection and maintenance tasks on power transmission line equipment. The inspection robot is driven by 11 motor with two arms, two wheels and two claws. The inspection robot is designed to realize the function of observation, grasp, walk, rolling, turn, rise, and decline. This paper is oriented toward 100% reliable obstacle detection and identification, and sensor fusion to increase the autonomy level. An embedded computer based on PC/104 bus is chosen as the core of control system. Visible light camera and thermal infrared Camera are both installed in a programmable pan-and-tilt camera (PPTC) unit. High-quality visual feedback rapidly becomes crucial for human-in-the-loop control and effective teleoperation. The communication system between the robot and the ground station is based on Mesh wireless networks by 700 MHz bands. An expert system programmed with Visual C++ is developed to implement the automatic control. Optoelectronic laser sensors and laser range scanner were installed in robot for obstacle-navigation control to grasp the overhead ground wires. A novel prototype with careful considerations on mobility was designed to inspect the 500KV power transmission lines. Results of experiments demonstrate that the robot can be applied to execute the navigation and inspection tasks.

A new HLA-based distributed control architecture for agricultural teams of robots in hybrid applications with real and simulated devices or environments.

PubMed

Nebot, Patricio; Torres-Sospedra, Joaquín; Martínez, Rafael J

2011-01-01

The control architecture is one of the most important part of agricultural robotics and other robotic systems. Furthermore its importance increases when the system involves a group of heterogeneous robots that should cooperate to achieve a global goal. A new control architecture is introduced in this paper for groups of robots in charge of doing maintenance tasks in agricultural environments. Some important features such as scalability, code reuse, hardware abstraction and data distribution have been considered in the design of the new architecture. Furthermore, coordination and cooperation among the different elements in the system is allowed in the proposed control system. By integrating a network oriented device server Player, Java Agent Development Framework (JADE) and High Level Architecture (HLA), the previous concepts have been considered in the new architecture presented in this paper. HLA can be considered the most important part because it not only allows the data distribution and implicit communication among the parts of the system but also allows to simultaneously operate with simulated and real entities, thus allowing the use of hybrid systems in the development of applications.

A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion

PubMed Central

Szczecinski, Nicholas S.; Hunt, Alexander J.; Quinn, Roger D.

2017-01-01

A dynamical model of an animal’s nervous system, or synthetic nervous system (SNS), is a potentially transformational control method. Due to increasingly detailed data on the connectivity and dynamics of both mammalian and insect nervous systems, controlling a legged robot with an SNS is largely a problem of parameter tuning. Our approach to this problem is to design functional subnetworks that perform specific operations, and then assemble them into larger models of the nervous system. In this paper, we present networks that perform addition, subtraction, multiplication, division, differentiation, and integration of incoming signals. Parameters are set within each subnetwork to produce the desired output by utilizing the operating range of neural activity, R, the gain of the operation, k, and bounds based on biological values. The assembly of large networks from functional subnetworks underpins our recent results with MantisBot. PMID:28848419

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

PubMed Central

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

2017-01-01

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning. PMID:28287468

Dissociated emergent-response system and fine-processing system in human neural network and a heuristic neural architecture for autonomous humanoid robots.

PubMed

Yan, Xiaodan

2010-01-01

The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots. Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns. Dissociation was within the primary sensory system, in that the olfactory cortex and the somatosensory cortex were strongly connected to the amygdala whereas the visual cortex and the auditory cortex were strongly connected with the frontal cortex. The posterior cingulate cortex (PCC) and the anterior cingulate cortex (ACC) were found to maintain constant communication with the primary sensory system, the frontal cortex, and the amygdala. Such neural architecture inspired the design of dissociated emergent-response system and fine-processing system in autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems. Such design can help autonomous robots to detect and respond quickly to danger, so as to maintain their sustainability and independence.

Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

PubMed Central

Chen, Qihong; Long, Rong; Quan, Shuhai

2014-01-01

This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206

From cineradiography to biorobots: an approach for designing robots to emulate and study animal locomotion.

PubMed

Karakasiliotis, K; Thandiackal, R; Melo, K; Horvat, T; Mahabadi, N K; Tsitkov, S; Cabelguen, J M; Ijspeert, A J

2016-06-01

Robots are increasingly used as scientific tools to investigate animal locomotion. However, designing a robot that properly emulates the kinematic and dynamic properties of an animal is difficult because of the complexity of musculoskeletal systems and the limitations of current robotics technology. Here, we propose a design process that combines high-speed cineradiography, optimization, dynamic scaling, three-dimensional printing, high-end servomotors and a tailored dry-suit to construct Pleurobot: a salamander-like robot that closely mimics its biological counterpart, Pleurodeles waltl Our previous robots helped us test and confirm hypotheses on the interaction between the locomotor neuronal networks of the limbs and the spine to generate basic swimming and walking gaits. With Pleurobot, we demonstrate a design process that will enable studies of richer motor skills in salamanders. In particular, we are interested in how these richer motor skills can be obtained by extending our spinal cord models with the addition of more descending pathways and more detailed limb central pattern generator networks. Pleurobot is a dynamically scaled amphibious salamander robot with a large number of actuated degrees of freedom (DOFs: 27 in total). Because of our design process, the robot can capture most of the animal's DOFs and range of motion, especially at the limbs. We demonstrate the robot's abilities by imposing raw kinematic data, extracted from X-ray videos, to the robot's joints for basic locomotor behaviours in water and on land. The robot closely matches the behaviour of the animal in terms of relative forward speeds and lateral displacements. Ground reaction forces during walking also resemble those of the animal. Based on our results, we anticipate that future studies on richer motor skills in salamanders will highly benefit from Pleurobot's design. © 2016 The Author(s).

From cineradiography to biorobots: an approach for designing robots to emulate and study animal locomotion

PubMed Central

Karakasiliotis, K.; Thandiackal, R.; Melo, K.; Horvat, T.; Mahabadi, N. K.; Tsitkov, S.; Cabelguen, J. M.; Ijspeert, A. J.

2016-01-01

Robots are increasingly used as scientific tools to investigate animal locomotion. However, designing a robot that properly emulates the kinematic and dynamic properties of an animal is difficult because of the complexity of musculoskeletal systems and the limitations of current robotics technology. Here, we propose a design process that combines high-speed cineradiography, optimization, dynamic scaling, three-dimensional printing, high-end servomotors and a tailored dry-suit to construct Pleurobot: a salamander-like robot that closely mimics its biological counterpart, Pleurodeles waltl. Our previous robots helped us test and confirm hypotheses on the interaction between the locomotor neuronal networks of the limbs and the spine to generate basic swimming and walking gaits. With Pleurobot, we demonstrate a design process that will enable studies of richer motor skills in salamanders. In particular, we are interested in how these richer motor skills can be obtained by extending our spinal cord models with the addition of more descending pathways and more detailed limb central pattern generator networks. Pleurobot is a dynamically scaled amphibious salamander robot with a large number of actuated degrees of freedom (DOFs: 27 in total). Because of our design process, the robot can capture most of the animal's DOFs and range of motion, especially at the limbs. We demonstrate the robot's abilities by imposing raw kinematic data, extracted from X-ray videos, to the robot's joints for basic locomotor behaviours in water and on land. The robot closely matches the behaviour of the animal in terms of relative forward speeds and lateral displacements. Ground reaction forces during walking also resemble those of the animal. Based on our results, we anticipate that future studies on richer motor skills in salamanders will highly benefit from Pleurobot's design. PMID:27358276

Microwave vision for robots

NASA Technical Reports Server (NTRS)

Lewandowski, Leon; Struckman, Keith

1994-01-01

Microwave Vision (MV), a concept originally developed in 1985, could play a significant role in the solution to robotic vision problems. Originally our Microwave Vision concept was based on a pattern matching approach employing computer based stored replica correlation processing. Artificial Neural Network (ANN) processor technology offers an attractive alternative to the correlation processing approach, namely the ability to learn and to adapt to changing environments. This paper describes the Microwave Vision concept, some initial ANN-MV experiments, and the design of an ANN-MV system that has led to a second patent disclosure in the robotic vision field.

Connectivity-Preserving Approach for Distributed Adaptive Synchronized Tracking of Networked Uncertain Nonholonomic Mobile Robots.

PubMed

Yoo, Sung Jin; Park, Bong Seok

2017-09-06

This paper addresses a distributed connectivity-preserving synchronized tracking problem of multiple uncertain nonholonomic mobile robots with limited communication ranges. The information of the time-varying leader robot is assumed to be accessible to only a small fraction of follower robots. The main contribution of this paper is to introduce a new distributed nonlinear error surface for dealing with both the synchronized tracking and the preservation of the initial connectivity patterns among nonholonomic robots. Based on this nonlinear error surface, the recursive design methodology is presented to construct the approximation-based local adaptive tracking scheme at the robot dynamic level. Furthermore, a technical lemma is established to analyze the stability and the connectivity preservation of the total closed-loop control system in the Lyapunov sense. An example is provided to illustrate the effectiveness of the proposed methodology.

Proceedings of the 1987 IEEE international conference on systems, man, and cybernetics. Volume 1

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

Not Available

1987-01-01

This book contains the proceedings of the IEE international conference on systems Man, and cybernetics. Topics include the following: robotics; knowledge base simulation; software systems, image and pattern recognition; neural networks; and image processing.

Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks.

PubMed

Hinaut, Xavier; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

2014-01-01

One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction.

Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks

PubMed Central

Hinaut, Xavier; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

2014-01-01

One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction. PMID:24834050

Design and Performance Evaluation of Real-time Endovascular Interventional Surgical Robotic System with High Accuracy.

PubMed

Wang, Kundong; Chen, Bing; Lu, Qingsheng; Li, Hongbing; Liu, Manhua; Shen, Yu; Xu, Zhuoyan

2018-05-15

Endovascular interventional surgery (EIS) is performed under a high radiation environment at the sacrifice of surgeons' health. This paper introduces a novel endovascular interventional surgical robot that aims to reduce radiation to surgeons and physical stress imposed by lead aprons during fluoroscopic X-ray guided catheter intervention. The unique mechanical structure allowed the surgeon to manipulate the axial and radial motion of the catheter and guide wire. Four catheter manipulators (to manipulate the catheter and guide wire), and a control console which consists of four joysticks, several buttons and two twist switches (to control the catheter manipulators) were presented. The entire robotic system was established on a master-slave control structure through CAN (Controller Area Network) bus communication, meanwhile, the slave side of this robotic system showed highly accurate control over velocity and displacement with PID controlling method. The robotic system was tested and passed in vitro and animal experiments. Through functionality evaluation, the manipulators were able to complete interventional surgical motion both independently and cooperatively. The robotic surgery was performed successfully in an adult female pig and demonstrated the feasibility of superior mesenteric and common iliac artery stent implantation. The entire robotic system met the clinical requirements of EIS. The results show that the system has the ability to imitate the movements of surgeons and to accomplish the axial and radial motions with consistency and high-accuracy. Copyright © 2018 John Wiley & Sons, Ltd.

Path planning on cellular nonlinear network using active wave computing technique

NASA Astrophysics Data System (ADS)

Yeniçeri, Ramazan; Yalçın, Müstak E.

2009-05-01

This paper introduces a simple algorithm to solve robot path finding problem using active wave computing techniques. A two-dimensional Cellular Neural/Nonlinear Network (CNN), consist of relaxation oscillators, has been used to generate active waves and to process the visual information. The network, which has been implemented on a Field Programmable Gate Array (FPGA) chip, has the feature of being programmed, controlled and observed by a host computer. The arena of the robot is modelled as the medium of the active waves on the network. Active waves are employed to cover the whole medium with their own dynamics, by starting from an initial point. The proposed algorithm is achieved by observing the motion of the wave-front of the active waves. Host program first loads the arena model onto the active wave generator network and command to start the generation. Then periodically pulls the network image from the generator hardware to analyze evolution of the active waves. When the algorithm is completed, vectorial data image is generated. The path from any of the pixel on this image to the active wave generating pixel is drawn by the vectors on this image. The robot arena may be a complicated labyrinth or may have a simple geometry. But, the arena surface always must be flat. Our Autowave Generator CNN implementation which is settled on the Xilinx University Program Virtex-II Pro Development System is operated by a MATLAB program running on the host computer. As the active wave generator hardware has 16, 384 neurons, an arena with 128 × 128 pixels can be modeled and solved by the algorithm. The system also has a monitor and network image is depicted on the monitor simultaneously.

Path optimisation of a mobile robot using an artificial neural network controller

NASA Astrophysics Data System (ADS)

Singh, M. K.; Parhi, D. R.

2011-01-01

This article proposed a novel approach for design of an intelligent controller for an autonomous mobile robot using a multilayer feed forward neural network, which enables the robot to navigate in a real world dynamic environment. The inputs to the proposed neural controller consist of left, right and front obstacle distance with respect to its position and target angle. The output of the neural network is steering angle. A four layer neural network has been designed to solve the path and time optimisation problem of mobile robots, which deals with the cognitive tasks such as learning, adaptation, generalisation and optimisation. A back propagation algorithm is used to train the network. This article also analyses the kinematic design of mobile robots for dynamic movements. The simulation results are compared with experimental results, which are satisfactory and show very good agreement. The training of the neural nets and the control performance analysis has been done in a real experimental setup.

FOCU:S--future operator control unit: soldier

NASA Astrophysics Data System (ADS)

O'Brien, Barry J.; Karan, Cem; Young, Stuart H.

2009-05-01

The U.S. Army Research Laboratory's (ARL) Computational and Information Sciences Directorate (CISD) has long been involved in autonomous asset control, specifically as it relates to small robots. Over the past year, CISD has been making strides in the implementation of three areas of small robot autonomy, namely platform autonomy, Soldier-robot interface, and tactical behaviors. It is CISD's belief that these three areas must be considered as a whole in order to provide Soldiers with useful capabilities. In addressing the Soldier-robot interface aspect, CISD has begun development on a unique dismounted controller called the Future Operator Control Unit: Soldier (FOCU:S) that is based on an Apple iPod Touch. The iPod Touch's small form factor, unique touch-screen input device, and the presence of general purpose computing applications such as a web browser combine to give this device the potential to be a disruptive technology. Setting CISD's implementation apart from other similar iPod or iPhone-based devices is the ARL software that allows multiple robotic platforms to be controlled from a single OCU. The FOCU:S uses the same Agile Computing Infrastructure (ACI) that all other assets in the ARL robotic control system use, enabling automated asset discovery on any type of network. Further, a custom ad hoc routing implementation allows the FOCU:S to communicate with the ARL ad hoc communications system and enables it to extend the range of the network. This paper will briefly describe the current robotic control architecture employed by ARL and provide short descriptions of existing capabilities. Further, the paper will discuss FOCU:S specific software developed for the iPod Touch, including unique capabilities enabled by the device's unique hardware.

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

NASA Technical Reports Server (NTRS)

Lea, Robert N. (Editor); Villarreal, James A. (Editor)

1991-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation.

PubMed

Fung, Wai-keung; Liu, Yun-hui

2003-12-01

Adaptive Resonance Theory (ART) networks are employed in robot behavior learning. Two of the difficulties in online robot behavior learning, namely, (1) exponential memory increases with time, (2) difficulty for operators to specify learning tasks accuracy and control learning attention before learning. In order to remedy the aforementioned difficulties, an adaptive categorization mechanism is introduced in ART networks for perceptual and action patterns categorization in this paper. A game-theoretic formulation of adaptive categorization for ART networks is proposed for vigilance parameter adaptation for category size control on the categories formed. The proposed vigilance parameter update rule can help improving categorization performance in the aspect of category number stability and solve the problem of selecting initial vigilance parameter prior to pattern categorization in traditional ART networks. Behavior learning using physical robot is conducted to demonstrate the effectiveness of the proposed adaptive categorization mechanism in ART networks.

An Advanced Orbiting Systems Approach to Quality of Service in Space-Based Intelligent Communication Networks

NASA Technical Reports Server (NTRS)

Riha, Andrew P.

2005-01-01

As humans and robotic technologies are deployed in future constellation systems, differing traffic services will arise, e.g., realtime and non-realtime. In order to provide a quality of service framework that would allow humans and robotic technologies to interoperate over a wide and dynamic range of interactions, a method of classifying data as realtime or non-realtime is needed. In our paper, we present an approach that leverages the Consultative Committee for Space Data Systems (CCSDS) Advanced Orbiting Systems (AOS) data link protocol. Specifically, we redefine the AOS Transfer Frame Replay Flag in order to provide an automated store-and-forward approach on a per-service basis for use in the next-generation Interplanetary Network. In addition to addressing the problem of intermittent connectivity and associated services, we propose a follow-on methodology for prioritizing data through further modification of the AOS Transfer Frame.

Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion

PubMed Central

Rieffel, John A.; Valero-Cuevas, Francisco J.; Lipson, Hod

2010-01-01

Traditional engineering approaches strive to avoid, or actively suppress, nonlinear dynamic coupling among components. Biological systems, in contrast, are often rife with these dynamics. Could there be, in some cases, a benefit to high degrees of dynamical coupling? Here we present a distributed robotic control scheme inspired by the biological phenomenon of tensegrity-based mechanotransduction. This emergence of morphology-as-information-conduit or ‘morphological communication’, enabled by time-sensitive spiking neural networks, presents a new paradigm for the decentralized control of large, coupled, modular systems. These results significantly bolster, both in magnitude and in form, the idea of morphological computation in robotic control. Furthermore, they lend further credence to ideas of embodied anatomical computation in biological systems, on scales ranging from cellular structures up to the tendinous networks of the human hand. PMID:19776146

Use of 3D vision for fine robot motion

NASA Technical Reports Server (NTRS)

Lokshin, Anatole; Litwin, Todd

1989-01-01

An integration of 3-D vision systems with robot manipulators will allow robots to operate in a poorly structured environment by visually locating targets and obstacles. However, by using computer vision for objects acquisition makes the problem of overall system calibration even more difficult. Indeed, in a CAD based manipulation a control architecture has to find an accurate mapping between the 3-D Euclidean work space and a robot configuration space (joint angles). If a stereo vision is involved, then one needs to map a pair of 2-D video images directly into the robot configuration space. Neural Network approach aside, a common solution to this problem is to calibrate vision and manipulator independently, and then tie them via common mapping into the task space. In other words, both vision and robot refer to some common Absolute Euclidean Coordinate Frame via their individual mappings. This approach has two major difficulties. First a vision system has to be calibrated over the total work space. And second, the absolute frame, which is usually quite arbitrary, has to be the same with a high degree of precision for both robot and vision subsystem calibrations. The use of computer vision to allow robust fine motion manipulation in a poorly structured world which is currently in progress is described along with the preliminary results and encountered problems.

A tele-operated mobile ultrasound scanner using a light-weight robot.

PubMed

Delgorge, Cécile; Courrèges, Fabien; Al Bassit, Lama; Novales, Cyril; Rosenberger, Christophe; Smith-Guerin, Natalie; Brù, Concepció; Gilabert, Rosa; Vannoni, Maurizio; Poisson, Gérard; Vieyres, Pierre

2005-03-01

This paper presents a new tele-operated robotic chain for real-time ultrasound image acquisition and medical diagnosis. This system has been developed in the frame of the Mobile Tele-Echography Using an Ultralight Robot European Project. A light-weight six degrees-of-freedom serial robot, with a remote center of motion, has been specially designed for this application. It holds and moves a real probe on a distant patient according to the expert gesture and permits an image acquisition using a standard ultrasound device. The combination of mechanical structure choice for the robot and dedicated control law, particularly nearby the singular configuration allows a good path following and a robotized gesture accuracy. The choice of compression techniques for image transmission enables a compromise between flow and quality. These combined approaches, for robotics and image processing, enable the medical specialist to better control the remote ultrasound probe holder system and to receive stable and good quality ultrasound images to make a diagnosis via any type of communication link from terrestrial to satellite. Clinical tests have been performed since April 2003. They used both satellite or Integrated Services Digital Network lines with a theoretical bandwidth of 384 Kb/s. They showed the tele-echography system helped to identify 66% of lesions and 83% of symptomatic pathologies.

Open core control software for surgical robots

PubMed Central

Kozuka, Hiroaki; Kim, Hyung Wook; Takesue, Naoyuki; Vladimirov, B.; Sakaguchi, Masamichi; Tokuda, Junichi; Hata, Nobuhiko; Chinzei, Kiyoyuki; Fujimoto, Hideo

2010-01-01

Object In these days, patients and doctors in operation room are surrounded by many medical devices as resulting from recent advancement of medical technology. However, these cutting-edge medical devices are working independently and not collaborating with each other, even though the collaborations between these devices such as navigation systems and medical imaging devices are becoming very important for accomplishing complex surgical tasks (such as a tumor removal procedure while checking the tumor location in neurosurgery). On the other hand, several surgical robots have been commercialized, and are becoming common. However, these surgical robots are not open for collaborations with external medical devices in these days. A cutting-edge “intelligent surgical robot” will be possible in collaborating with surgical robots, various kinds of sensors, navigation system and so on. On the other hand, most of the academic software developments for surgical robots are “home-made” in their research institutions and not open to the public. Therefore, open source control software for surgical robots can be beneficial in this field. From these perspectives, we developed Open Core Control software for surgical robots to overcome these challenges. Materials and methods In general, control softwares have hardware dependencies based on actuators, sensors and various kinds of internal devices. Therefore, these control softwares cannot be used on different types of robots without modifications. However, the structure of the Open Core Control software can be reused for various types of robots by abstracting hardware dependent parts. In addition, network connectivity is crucial for collaboration between advanced medical devices. The OpenIGTLink is adopted in Interface class which plays a role to communicate with external medical devices. At the same time, it is essential to maintain the stable operation within the asynchronous data transactions through network. In the Open Core Control software, several techniques for this purpose were introduced. Virtual fixture is well known technique as a “force guide” for supporting operators to perform precise manipulation by using a master–slave robot. The virtual fixture for precise and safety surgery was implemented on the system to demonstrate an idea of high-level collaboration between a surgical robot and a navigation system. The extension of virtual fixture is not a part of the Open Core Control system, however, the function such as virtual fixture cannot be realized without a tight collaboration between cutting-edge medical devices. By using the virtual fixture, operators can pre-define an accessible area on the navigation system, and the area information can be transferred to the robot. In this manner, the surgical console generates the reflection force when the operator tries to get out from the pre-defined accessible area during surgery. Results The Open Core Control software was implemented on a surgical master–slave robot and stable operation was observed in a motion test. The tip of the surgical robot was displayed on a navigation system by connecting the surgical robot with a 3D position sensor through the OpenIGTLink. The accessible area was pre-defined before the operation, and the virtual fixture was displayed as a “force guide” on the surgical console. In addition, the system showed stable performance in a duration test with network disturbance. Conclusion In this paper, a design of the Open Core Control software for surgical robots and the implementation of virtual fixture were described. The Open Core Control software was implemented on a surgical robot system and showed stable performance in high-level collaboration works. The Open Core Control software is developed to be a widely used platform of surgical robots. Safety issues are essential for control software of these complex medical devices. It is important to follow the global specifications such as a FDA requirement “General Principles of Software Validation” or IEC62304. For following these regulations, it is important to develop a self-test environment. Therefore, a test environment is now under development to test various interference in operation room such as a noise of electric knife by considering safety and test environment regulations such as ISO13849 and IEC60508. The Open Core Control software is currently being developed software in open-source manner and available on the Internet. A communization of software interface is becoming a major trend in this field. Based on this perspective, the Open Core Control software can be expected to bring contributions in this field. PMID:20033506

USAR Robot Communication Using ZigBee Technology

NASA Astrophysics Data System (ADS)

Tsui, Charles; Carnegie, Dale; Pan, Qing Wei

This paper reports the successful development of an automatic routing wireless network for USAR (urban search and rescue) robots in an artificial rubble environment. The wireless network was formed using ZigBee modules and each module was attached to a micro-controller in order to model a wireless USAR robot. Proof of concept experiments were carried out by deploying the networked robots into artificial rubble. The rubble was simulated by connecting holes and trenches that were dug in 50 cm deep soil. The simulated robots were placed in the bottom of the holes. The holes and trenches were then covered up by various building materials and soil to simulate a real rubble environment. Experiments demonstrated that a monitoring computer placed 10 meters outside the rubble can establish proper communication with all robots inside the artificial rubble environment.

Evolution of a radio communication relay system

NASA Astrophysics Data System (ADS)

Nguyen, Hoa G.; Pezeshkian, Narek; Hart, Abraham; Burmeister, Aaron; Holz, Kevin; Neff, Joseph; Roth, Leif

2013-05-01

Providing long-distance non-line-of-sight control for unmanned ground robots has long been recognized as a problem, considering the nature of the required high-bandwidth radio links. In the early 2000s, the DARPA Mobile Autonomous Robot Software (MARS) program funded the Space and Naval Warfare Systems Center (SSC) Pacific to demonstrate a capability for autonomous mobile communication relaying on a number of Pioneer laboratory robots. This effort also resulted in the development of ad hoc networking radios and software that were later leveraged in the development of a more practical and logistically simpler system, the Automatically Deployed Communication Relays (ADCR). Funded by the Joint Ground Robotics Enterprise and internally by SSC Pacific, several generations of ADCR systems introduced increasingly more capable hardware and software for automatic maintenance of communication links through deployment of static relay nodes from mobile robots. This capability was finally tapped in 2010 to fulfill an urgent need from theater. 243 kits of ruggedized, robot-deployable communication relays were produced and sent to Afghanistan to extend the range of EOD and tactical ground robots in 2012. This paper provides a summary of the evolution of the radio relay technology at SSC Pacific, and then focuses on the latest two stages, the Manually-Deployed Communication Relays and the latest effort to automate the deployment of these ruggedized and fielded relay nodes.

A new neural net approach to robot 3D perception and visuo-motor coordination

NASA Technical Reports Server (NTRS)

Lee, Sukhan

1992-01-01

A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

RoCoMAR: robots' controllable mobility aided routing and relay architecture for mobile sensor networks.

PubMed

Le, Duc Van; Oh, Hoon; Yoon, Seokhoon

2013-07-05

In a practical deployment, mobile sensor network (MSN) suffers from a low performance due to high node mobility, time-varying wireless channel properties, and obstacles between communicating nodes. In order to tackle the problem of low network performance and provide a desired end-to-end data transfer quality, in this paper we propose a novel ad hoc routing and relaying architecture, namely RoCoMAR (Robots' Controllable Mobility Aided Routing) that uses robotic nodes' controllable mobility. RoCoMAR repeatedly performs link reinforcement process with the objective of maximizing the network throughput, in which the link with the lowest quality on the path is identified and replaced with high quality links by placing a robotic node as a relay at an optimal position. The robotic node resigns as a relay if the objective is achieved or no more gain can be obtained with a new relay. Once placed as a relay, the robotic node performs adaptive link maintenance by adjusting its position according to the movements of regular nodes. The simulation results show that RoCoMAR outperforms existing ad hoc routing protocols for MSN in terms of network throughput and end-to-end delay.

RoCoMAR: Robots' Controllable Mobility Aided Routing and Relay Architecture for Mobile Sensor Networks

PubMed Central

Van Le, Duc; Oh, Hoon; Yoon, Seokhoon

2013-01-01

In a practical deployment, mobile sensor network (MSN) suffers from a low performance due to high node mobility, time-varying wireless channel properties, and obstacles between communicating nodes. In order to tackle the problem of low network performance and provide a desired end-to-end data transfer quality, in this paper we propose a novel ad hoc routing and relaying architecture, namely RoCoMAR (Robots' Controllable Mobility Aided Routing) that uses robotic nodes' controllable mobility. RoCoMAR repeatedly performs link reinforcement process with the objective of maximizing the network throughput, in which the link with the lowest quality on the path is identified and replaced with high quality links by placing a robotic node as a relay at an optimal position. The robotic node resigns as a relay if the objective is achieved or no more gain can be obtained with a new relay. Once placed as a relay, the robotic node performs adaptive link maintenance by adjusting its position according to the movements of regular nodes. The simulation results show that RoCoMAR outperforms existing ad hoc routing protocols for MSN in terms of network throughput and end-to-end delay. PMID:23881134

Neural network-based landmark detection for mobile robot

NASA Astrophysics Data System (ADS)

Sekiguchi, Minoru; Okada, Hiroyuki; Watanabe, Nobuo

1996-03-01

The mobile robot can essentially have only the relative position data for the real world. However, there are many cases that the robot has to know where it is located. In those cases, the useful method is to detect landmarks in the real world and adjust its position using detected landmarks. In this point of view, it is essential to develop a mobile robot that can accomplish the path plan successfully using natural or artificial landmarks. However, artificial landmarks are often difficult to construct and natural landmarks are very complicated to detect. In this paper, the method of acquiring landmarks by using the sensor data from the mobile robot necessary for planning the path is described. The landmark we discuss here is the natural one and is composed of the compression of sensor data from the robot. The sensor data is compressed and memorized by using five layered neural network that is called a sand glass model. The input and output data that neural network should learn is the sensor data of the robot that are exactly the same. Using the intermediate output data of the network, a compressed data is obtained, which expresses a landmark data. If the sensor data is ambiguous or enormous, it is easy to detect the landmark because the data is compressed and classified by the neural network. Using the backward three layers, the compressed landmark data is expanded to original data at some level. The studied neural network categorizes the detected sensor data to the known landmark.

Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems.

PubMed

Chang, Yeong-Chan

2005-12-01

This paper addresses the problem of designing adaptive fuzzy-based (or neural network-based) robust controls for a large class of uncertain nonlinear time-varying systems. This class of systems can be perturbed by plant uncertainties, unmodeled perturbations, and external disturbances. Nonlinear H(infinity) control technique incorporated with adaptive control technique and VSC technique is employed to construct the intelligent robust stabilization controller such that an H(infinity) control is achieved. The problem of the robust tracking control design for uncertain robotic systems is employed to demonstrate the effectiveness of the developed robust stabilization control scheme. Therefore, an intelligent robust tracking controller for uncertain robotic systems in the presence of high-degree uncertainties can easily be implemented. Its solution requires only to solve a linear algebraic matrix inequality and a satisfactorily transient and asymptotical tracking performance is guaranteed. A simulation example is made to confirm the performance of the developed control algorithms.

Aperiodic linear networked control considering variable channel delays: application to robots coordination.

PubMed

Santos, Carlos; Espinosa, Felipe; Santiso, Enrique; Mazo, Manuel

2015-05-27

One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

Distributed and Modular CAN-Based Architecture for Hardware Control and Sensor Data Integration

PubMed Central

Losada, Diego P.; Fernández, Joaquín L.; Paz, Enrique; Sanz, Rafael

2017-01-01

In this article, we present a CAN-based (Controller Area Network) distributed system to integrate sensors, actuators and hardware controllers in a mobile robot platform. With this work, we provide a robust, simple, flexible and open system to make hardware elements or subsystems communicate, that can be applied to different robots or mobile platforms. Hardware modules can be connected to or disconnected from the CAN bus while the system is working. It has been tested in our mobile robot Rato, based on a RWI (Real World Interface) mobile platform, to replace the old sensor and motor controllers. It has also been used in the design of two new robots: BellBot and WatchBot. Currently, our hardware integration architecture supports different sensors, actuators and control subsystems, such as motor controllers and inertial measurement units. The integration architecture was tested and compared with other solutions through a performance analysis of relevant parameters such as transmission efficiency and bandwidth usage. The results conclude that the proposed solution implements a lightweight communication protocol for mobile robot applications that avoids transmission delays and overhead. PMID:28467381

Design and Control of Compliant Tensegrity Robots Through Simulation and Hardware Validation

NASA Technical Reports Server (NTRS)

Caluwaerts, Ken; Despraz, Jeremie; Iscen, Atil; Sabelhaus, Andrew P.; Bruce, Jonathan; Schrauwen, Benjamin; Sunspiral, Vytas

2014-01-01

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center has developed and validated two different software environments for the analysis, simulation, and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ("tensile-integrity") structures have unique physical properties which make them ideal for interaction with uncertain environments. Yet these characteristics, such as variable structural compliance, and global multi-path load distribution through the tension network, make design and control of bio-inspired tensegrity robots extremely challenging. This work presents the progress in using these two tools in tackling the design and control challenges. The results of this analysis includes multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures. The current hardware prototype of a six-bar tensegrity, code-named ReCTeR, is presented in the context of this validation.

Distributed and Modular CAN-Based Architecture for Hardware Control and Sensor Data Integration.

PubMed

Losada, Diego P; Fernández, Joaquín L; Paz, Enrique; Sanz, Rafael

2017-05-03

In this article, we present a CAN-based (Controller Area Network) distributed system to integrate sensors, actuators and hardware controllers in a mobile robot platform. With this work, we provide a robust, simple, flexible and open system to make hardware elements or subsystems communicate, that can be applied to different robots or mobile platforms. Hardware modules can be connected to or disconnected from the CAN bus while the system is working. It has been tested in our mobile robot Rato, based on a RWI (Real World Interface) mobile platform, to replace the old sensor and motor controllers. It has also been used in the design of two new robots: BellBot and WatchBot. Currently, our hardware integration architecture supports different sensors, actuators and control subsystems, such as motor controllers and inertial measurement units. The integration architecture was tested and compared with other solutions through a performance analysis of relevant parameters such as transmission efficiency and bandwidth usage. The results conclude that the proposed solution implements a lightweight communication protocol for mobile robot applications that avoids transmission delays and overhead.

Guaranteeing Spoof-Resilient Multi-Robot Networks

DTIC Science & Technology

2015-05-12

particularly challenging attack on this assumption is the so-called “Sybil attack.” In a Sybil attack a malicious agent can generate (or spoof) a large...cybersecurity in general multi-node networks (e.g. a wired LAN), the same is not true for multi- robot networks [14, 28], leaving them largely vulnerable...key passing or cryptographic authen- tication is difficult to maintain due to the highly dynamic and distributed nature of multi-robot teams where

A soft biomimetic tongue: model reconstruction and motion tracking

NASA Astrophysics Data System (ADS)

Lu, Xuanming; Xu, Weiliang; Li, Xiaoning

2016-04-01

A bioinspired robotic tongue which is actuated by a network of compressed air is proposed for the purpose of mimicking the movements of human tongue. It can be applied in the fields such as medical science and food engineering. The robotic tongue is made of two kinds of silicone rubber Ecoflex 0030 and PDMS with the shape simplified from real human tongue. In order to characterize the robotic tongue, a series of experiments were carried out. Laser scan was applied to reconstruct the static model of robotic tongue when it was under pressurization. After each scan, the robotic tongue was scattered into dense points in the same 3D coordinate system and the coordinates of each point were recorded. Motion tracking system (OptiTrack) was used to track and record the whole process of deformation dynamically during the loading and unloading phase. In the experiments, five types of deformation were achieved including roll-up, roll-down, elongation, groove and twist. Utilizing the discrete points generated by laser scan, the accurate parameterized outline of robotic tongue under different pressure was obtained, which could help demonstrate the static characteristic of robotic tongue. The precise deformation process under one pressure was acquired through the OptiTrack system which contains a series of digital cameras, markers on the robotic tongue and a set of hardware and software for data processing. By means of tracking and recording different process of deformation under different pressure, the dynamic characteristic of robotic tongue could be achieved.

Knowledge assistant for robotic environmental characterization

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

Feddema, J.; Rivera, J.; Tucker, S.

1996-08-01

A prototype sensor fusion framework called the {open_quotes}Knowledge Assistant{close_quotes} has been developed and tested on a gantry robot at Sandia National Laboratories. This Knowledge Assistant guides the robot operator during the planning, execution, and post analysis stages of the characterization process. During the planning stage, the Knowledge Assistant suggests robot paths and speeds based on knowledge of sensors available and their physical characteristics. During execution, the Knowledge Assistant coordinates the collection of data through a data acquisition {open_quotes}specialist.{close_quotes} During execution and postanalysis, the Knowledge Assistant sends raw data to other {open_quotes}specialists,{close_quotes} which include statistical pattern recognition software, a neural network,more » and model-based search software. After the specialists return their results, the Knowledge Assistant consolidates the information and returns a report to the robot control system where the sensed objects and their attributes (e.g., estimated dimensions, weight, material composition, etc.) are displayed in the world model. This report highlights the major components of this system.« less

Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

NASA Technical Reports Server (NTRS)

Lima, Pedro; Beard, Randal

1992-01-01

The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.

Cognitive-Developmental Learning for a Humanoid Robot: A Caregiver’s Gift

DTIC Science & Technology

2004-05-01

system . We propose a real- time algorithm to infer depth and build 3-dimensional coarse maps for objects through the analysis of cues provided by an... system is well defined at the boundary of these regions (although the derivatives are not). A time domain analysis is presented for a piece-linear... Analysis of Multivariable Systems ......................... 266 D.3.1 Networks of Multiple Neural Oscillators ................. 266 D.3.2 Networks of

An intelligent space for mobile robot localization using a multi-camera system.

PubMed

Rampinelli, Mariana; Covre, Vitor Buback; de Queiroz, Felippe Mendonça; Vassallo, Raquel Frizera; Bastos-Filho, Teodiano Freire; Mazo, Manuel

2014-08-15

This paper describes an intelligent space, whose objective is to localize and control robots or robotic wheelchairs to help people. Such an intelligent space has 11 cameras distributed in two laboratories and a corridor. The cameras are fixed in the environment, and image capturing is done synchronously. The system was programmed as a client/server with TCP/IP connections, and a communication protocol was defined. The client coordinates the activities inside the intelligent space, and the servers provide the information needed for that. Once the cameras are used for localization, they have to be properly calibrated. Therefore, a calibration method for a multi-camera network is also proposed in this paper. A robot is used to move a calibration pattern throughout the field of view of the cameras. Then, the captured images and the robot odometry are used for calibration. As a result, the proposed algorithm provides a solution for multi-camera calibration and robot localization at the same time. The intelligent space and the calibration method were evaluated under different scenarios using computer simulations and real experiments. The results demonstrate the proper functioning of the intelligent space and validate the multi-camera calibration method, which also improves robot localization.

An Intelligent Space for Mobile Robot Localization Using a Multi-Camera System

PubMed Central

Rampinelli, Mariana.; Covre, Vitor Buback.; de Queiroz, Felippe Mendonça.; Vassallo, Raquel Frizera.; Bastos-Filho, Teodiano Freire.; Mazo, Manuel.

2014-01-01

This paper describes an intelligent space, whose objective is to localize and control robots or robotic wheelchairs to help people. Such an intelligent space has 11 cameras distributed in two laboratories and a corridor. The cameras are fixed in the environment, and image capturing is done synchronously. The system was programmed as a client/server with TCP/IP connections, and a communication protocol was defined. The client coordinates the activities inside the intelligent space, and the servers provide the information needed for that. Once the cameras are used for localization, they have to be properly calibrated. Therefore, a calibration method for a multi-camera network is also proposed in this paper. A robot is used to move a calibration pattern throughout the field of view of the cameras. Then, the captured images and the robot odometry are used for calibration. As a result, the proposed algorithm provides a solution for multi-camera calibration and robot localization at the same time. The intelligent space and the calibration method were evaluated under different scenarios using computer simulations and real experiments. The results demonstrate the proper functioning of the intelligent space and validate the multi-camera calibration method, which also improves robot localization. PMID:25196009

Cracking the egg: virtual embryogenesis of real robots.

PubMed

Cussat-Blanc, Sylvain; Pollack, Jordan

2014-01-01

All multicellular living beings are created from a single cell. A developmental process, called embryogenesis, takes this first fertilized cell down a complex path of reproduction, migration, and specialization into a complex organism adapted to its environment. In most cases, the first steps of the embryogenesis take place in a protected environment such as in an egg or in utero. Starting from this observation, we propose a new approach to the generation of real robots, strongly inspired by living systems. Our robots are composed of tens of specialized cells, grown from a single cell using a bio-inspired virtual developmental process. Virtual cells, controlled by gene regulatory networks, divide, migrate, and specialize to produce the robot's body plan (morphology), and then the robot is manually built from this plan. Because the robot is as easy to assemble as Lego, the building process could be easily automated.

Principles and advantages of robotics in urologic surgery.

PubMed

Renda, Antonio; Vallancien, Guy

2003-04-01

Although the available minimally invasive surgical techniques (ie, laparoscopy) have clear advantages, these procedures continue to cause problems for patients. Surgical tools are limited by set axes of movement, restricting the degree of freedom available to the surgeon. In addition, depth perception is lost with the use of two-dimensional viewing systems. As surgeons view a "virtual" target on a television screen, they are hampered by decreased sensory input and a concurrent loss of dexterity. The development of robotic assistance systems in recent years could be the key to overcoming these difficulties. Using robotic systems, surgeons can experience a more natural and ergonomic surgical "feel." Surgical assistance, dexterity and precision enhancement, systems networking, and image-guided therapy are among the benefits offered by surgical robots. In return, the surgeon gains a shorter learning curve, reduced fatigue, and the opportunity to perform complex procedures that would be difficult using conventional laparoscopy. With the development of image-guided technology, robotic systems will become useful tools for surgical training and simulation. Remote surgery is not a routine procedure, but several teams are working on this and experiencing good results. However, economic concerns are the major drawbacks of these systems; before remote surgery becomes routinely feasible, the clinical benefits must be balanced with high investment and running costs.

Rendezvous with connectivity preservation for multi-robot systems with an unknown leader

NASA Astrophysics Data System (ADS)

Dong, Yi

2018-02-01

This paper studies the leader-following rendezvous problem with connectivity preservation for multi-agent systems composed of uncertain multi-robot systems subject to external disturbances and an unknown leader, both of which are generated by a so-called exosystem with parametric uncertainty. By combining internal model design, potential function technique and adaptive control, two distributed control strategies are proposed to maintain the connectivity of the communication network, to achieve the asymptotic tracking of all the followers to the output of the unknown leader system, as well as to reject unknown external disturbances. It is also worth to mention that the uncertain parameters in the multi-robot systems and exosystem are further allowed to belong to unknown and unbounded sets when applying the second fully distributed control law containing a dynamic gain inspired by high-gain adaptive control or self-tuning regulator.

Medical telerobotic systems: current status and future trends.

PubMed

Avgousti, Sotiris; Christoforou, Eftychios G; Panayides, Andreas S; Voskarides, Sotos; Novales, Cyril; Nouaille, Laurence; Pattichis, Constantinos S; Vieyres, Pierre

2016-08-12

Teleoperated medical robotic systems allow procedures such as surgeries, treatments, and diagnoses to be conducted across short or long distances while utilizing wired and/or wireless communication networks. This study presents a systematic review of the relevant literature between the years 2004 and 2015, focusing on medical teleoperated robotic systems which have witnessed tremendous growth over the examined period. A thorough insight of telerobotics systems discussing design concepts, enabling technologies (namely robotic manipulation, telecommunications, and vision systems), and potential applications in clinical practice is provided, while existing limitations and future trends are also highlighted. A representative paradigm of the short-distance case is the da Vinci Surgical System which is described in order to highlight relevant issues. The long-distance telerobotics concept is exemplified through a case study on diagnostic ultrasound scanning. Moreover, the present review provides a classification into short- and long-distance telerobotic systems, depending on the distance from which they are operated. Telerobotic systems are further categorized with respect to their application field. For the reviewed systems are also examined their engineering characteristics and the employed robotics technology. The current status of the field, its significance, the potential, as well as the challenges that lie ahead are thoroughly discussed.

Integration of task level planning and diagnosis for an intelligent robot

NASA Technical Reports Server (NTRS)

Gerstenfeld, Arthur

1988-01-01

The use of robots in the future must go beyond present applications and will depend on the ability of a robot to adapt to a changing environment and to deal with unexpected scenarios (i.e., picking up parts that are not exactly where they were expected to be). The objective of this research is to demonstrate the feasibility of incorporating high level planning into a robot enabling it to deal with anomalous situations in order to minimize the need for constant human instruction. The heuristics can be used by a robot to apply information about previous actions towards accomplishing future objectives more efficiently. The system uses a decision network that represents the plan for accomplishing a task. This enables the robot to modify its plan based on results of previous actions. The system serves as a method for minimizing the need for constant human instruction in telerobotics. This paper describes the integration of expert systems and simulation as a valuable tool that goes far beyond this project. Simulation can be expected to be used increasingly as both hardware and software improve. Similarly, the ability to merge an expert system with simulation means that we can add intelligence to the system. A malfunctioning space satellite is described. The expert system uses a series of heuristics in order to guide the robot to the proper location. This is part of task level planning. The final part of the paper suggests directions for future research. Having shown the feasibility of an expert system embedded in a simulation, the paper then discusses how the system can be integrated with the MSFC graphics system.

Robustness of a distributed neural network controller for locomotion in a hexapod robot

NASA Technical Reports Server (NTRS)

Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

1992-01-01

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

Improving Cognitive Skills of the Industrial Robot

NASA Astrophysics Data System (ADS)

Bezák, Pavol

2015-08-01

At present, there are plenty of industrial robots that are programmed to do the same repetitive task all the time. Industrial robots doing such kind of job are not able to understand whether the action is correct, effective or good. Object detection, manipulation and grasping is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. In this paper, the proposal of an intelligent humanoid hand object detection and grasping model is presented assuming that the object properties are known. The control is simulated in the Matlab Simulink/ SimMechanics, Neural Network Toolbox and Computer Vision System Toolbox.

Optimization of the computational load of a hypercube supercomputer onboard a mobile robot.

PubMed

Barhen, J; Toomarian, N; Protopopescu, V

1987-12-01

A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of singleneuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as prec xdence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.

Optimization of the computational load of a hypercube supercomputer onboard a mobile robot

NASA Technical Reports Server (NTRS)

Barhen, Jacob; Toomarian, N.; Protopopescu, V.

1987-01-01

A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of single-neuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as precedence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.

Upper Torso Control for HOAP-2 Using Neural Networks

NASA Technical Reports Server (NTRS)

Sandoval, Steven P.

2005-01-01

Humanoid robots have similar physical builds and motion patterns as humans. Not only does this provide a suitable operating environment for the humanoid but it also opens up many research doors on how humans function. The overall objective is replacing humans operating in unsafe environments. A first target application is assembly of structures for future lunar-planetary bases. The initial development platform is a Fujitsu HOAP-2 humanoid robot. The goal for the project is to demonstrate the capability of a HOAP-2 to autonomously construct a cubic frame using provided tubes and joints. This task will require the robot to identify several items, pick them up, transport them to the build location, then properly assemble the structure. The ability to grasp and assemble the pieces will require improved motor control and the addition of tactile feedback sensors. In recent years, learning-based control is becoming more and more popular; for implementing this method we will be using the Adaptive Neural Fuzzy Inference System (ANFIS). When using neural networks for control, no complex models of the system must be constructed in advance-only input/output relationships are required to model the system.

Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots.

PubMed

Ampatzis, Christos; Tuci, Elio; Trianni, Vito; Christensen, Anders Lyhne; Dorigo, Marco

2009-01-01

This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.

A Hybrid Robotic Control System Using Neuroblastoma Cultures

NASA Astrophysics Data System (ADS)

Ferrández, J. M.; Lorente, V.; Cuadra, J. M.; Delapaz, F.; Álvarez-Sánchez, José Ramón; Fernández, E.

The main objective of this work is to analyze the computing capabilities of human neuroblastoma cultured cells and to define connection schemes for controlling a robot behavior. Multielectrode Array (MEA) setups have been designed for direct culturing neural cells over silicon or glass substrates, providing the capability to stimulate and record simultaneously populations of neural cells. This paper describes the process of growing human neuroblastoma cells over MEA substrates and tries to modulate the natural physiologic responses of these cells by tetanic stimulation of the culture. We show that the large neuroblastoma networks developed in cultured MEAs are capable of learning: establishing numerous and dynamic connections, with modifiability induced by external stimuli and we propose an hybrid system for controlling a robot to avoid obstacles.

Towards multi-platform software architecture for Collaborative Teleoperation

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

Domingues, Christophe; Otmane, Samir; Davesne, Frederic

2009-03-05

Augmented Reality (AR) can provide to a Human Operator (HO) a real help in achieving complex tasks, such as remote control of robots and cooperative teleassistance. Using appropriate augmentations, the HO can interact faster, safer and easier with the remote real world. In this paper, we present an extension of an existing distributed software and network architecture for collaborative teleoperation based on networked human-scaled mixed reality and mobile platform. The first teleoperation system was composed by a VR application and a Web application. However the 2 systems cannot be used together and it is impossible to control a distant robotmore » simultaneously. Our goal is to update the teleoperation system to permit a heterogeneous collaborative teleoperation between the 2 platforms. An important feature of this interface is based on the use of different Virtual Reality platforms and different Mobile platforms to control one or many robots.« less

A bio-inspired auditory perception model for amplitude-frequency clustering (keynote Paper)

NASA Astrophysics Data System (ADS)

Arena, Paolo; Fortuna, Luigi; Frasca, Mattia; Ganci, Gaetana; Patane, Luca

2005-06-01

In this paper a model for auditory perception is introduced. This model is based on a network of integrate-and-fire and resonate-and-fire neurons and is aimed to control the phonotaxis behavior of a roving robot. The starting point is the model of phonotaxis in Gryllus Bimaculatus: the model consists of four integrate-and-fire neurons and is able of discriminating the calling song of male cricket and orienting the robot towards the sound source. This paper aims to extend the model to include an amplitude-frequency clustering. The proposed spiking network shows different behaviors associated with different characteristics of the input signals (amplitude and frequency). The behavior implemented on the robot is similar to the cricket behavior, where some frequencies are associated with the calling song of male crickets, while other ones indicate the presence of predators. Therefore, the whole model for auditory perception is devoted to control different responses (attractive or repulsive) depending on the input characteristics. The performance of the control system has been evaluated with several experiments carried out on a roving robot.

Addressing the Movement of a Freescale Robotic Car Using Neural Network

NASA Astrophysics Data System (ADS)

Horváth, Dušan; Cuninka, Peter

2016-12-01

This article deals with the management of a Freescale small robotic car along the predefined guide line. Controlling of the direction of movement of the robot is performed by neural networks, and scales (memory) of neurons are calculated by Hebbian learning from the truth tables as learning with a teacher. Reflexive infrared sensors serves as inputs. The results are experiments, which are used to compare two methods of mobile robot control - tracking lines.

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

NASA Astrophysics Data System (ADS)

Meng, Qinggang; Lee, M. H.

2007-03-01

Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.

Integrated communication and control systems. I - Analysis

NASA Technical Reports Server (NTRS)

Halevi, Yoram; Ray, Asok

1988-01-01

The paper presents the results of an ICCS analysis focusing on discrete-time control systems subject to time-varying delays. The present analytical technique is applicable to integrated dynamic systems such as those encountered in advanced aircraft, spacecraft, and the real-time control of robots and machine tools via a high-speed network within an autonomous manufacturing environment. The significance of data latency and missynchronization between individual system components in ICCS networks is discussed in view of the time-varying delays.

A small, cheap, and portable reconnaissance robot

NASA Astrophysics Data System (ADS)

Kenyon, Samuel H.; Creary, D.; Thi, Dan; Maynard, Jeffrey

2005-05-01

While there is much interest in human-carriable mobile robots for defense/security applications, existing examples are still too large/heavy, and there are not many successful small human-deployable mobile ground robots, especially ones that can survive being thrown/dropped. We have developed a prototype small short-range teleoperated indoor reconnaissance/surveillance robot that is semi-autonomous. It is self-powered, self-propelled, spherical, and meant to be carried and thrown by humans into indoor, yet relatively unstructured, dynamic environments. The robot uses multiple channels for wireless control and feedback, with the potential for inter-robot communication, swarm behavior, or distributed sensor network capabilities. The primary reconnaissance sensor for this prototype is visible-spectrum video. This paper focuses more on the software issues, both the onboard intelligent real time control system and the remote user interface. The communications, sensor fusion, intelligent real time controller, etc. are implemented with onboard microcontrollers. We based the autonomous and teleoperation controls on a simple finite state machine scripting layer. Minimal localization and autonomous routines were designed to best assist the operator, execute whatever mission the robot may have, and promote its own survival. We also discuss the advantages and pitfalls of an inexpensive, rapidly-developed semi-autonomous robotic system, especially one that is spherical, and the importance of human-robot interaction as considered for the human-deployment and remote user interface.

LCOGT: A World-Wide Network of Robotic Telescopes

NASA Astrophysics Data System (ADS)

Brown, T.

2013-05-01

Las Cumbres Observatory Global Telescope (LCOGT) is an organization dedicated to time-domain astronomy. To carry out the necessary observations in fields such as supernovae, extrasolar planets, small solar-system bodies, and pulsating stars, we have developed and are now deploying a set of robotic optical telescopes at sites around the globe. In this talk I will concentrate on the core of this network, consisting of up to 15 identical 1m telescopes deployed across multiple sites in both the northern and southern hemispheres. I will summarize the technical and performance aspect of these telescopes, including both their imaging and their anticipated spectroscopic capabilities. But I will also delve into the network organization, including communication among telescopes (to assure that observations are properly carried out), interactions among the institutions and scientists who will use the network (to optimize the scientific returns), and our funding model (which until now has relied entirely on one private donor, but will soon require funding from outside sources, if the full potential of the network is to be achieved).

Lunar Relay Satellite Network for Space Exploration: Architecture, Technologies and Challenges

NASA Technical Reports Server (NTRS)

Bhasin, Kul B.; Hackenberg, Anthony W.; Slywczak, Richard A.; Bose, Prasanta; Bergamo, Marcos; Hayden, Jeffrey L.

2006-01-01

NASA is planning a series of short and long duration human and robotic missions to explore the Moon and then Mars. A key objective of these missions is to grow, through a series of launches, a system of systems infrastructure with the capability for safe and sustainable autonomous operations at minimum cost while maximizing the exploration capabilities and science return. An incremental implementation process will enable a buildup of the communication, navigation, networking, computing, and informatics architectures to support human exploration missions in the vicinities and on the surfaces of the Moon and Mars. These architectures will support all space and surface nodes, including other orbiters, lander vehicles, humans in spacesuits, robots, rovers, human habitats, and pressurized vehicles. This paper describes the integration of an innovative MAC and networking technology with an equally innovative position-dependent, data routing, network technology. The MAC technology provides the relay spacecraft with the capability to autonomously discover neighbor spacecraft and surface nodes, establish variable-rate links and communicate simultaneously with multiple in-space and surface clients at varying and rapidly changing distances while making optimum use of the available power. The networking technology uses attitude sensors, a time synchronization protocol and occasional orbit-corrections to maintain awareness of its instantaneous position and attitude in space as well as the orbital or surface location of its communication clients. A position-dependent data routing capability is used in the communication relay satellites to handle the movement of data among any of multiple clients (including Earth) that may be simultaneously in view; and if not in view, the relay will temporarily store the data from a client source and download it when the destination client comes into view. The integration of the MAC and data routing networking technologies would enable a relay satellite system to provide end-to-end communication services for robotic and human missions in the vicinity, or on the surface of the Moon with a minimum of Earth-based operational support.

New Control Paradigms for Resources Saving: An Approach for Mobile Robots Navigation.

PubMed

Socas, Rafael; Dormido, Raquel; Dormido, Sebastián

2018-01-18

In this work, an event-based control scheme is presented. The proposed system has been developed to solve control problems appearing in the field of Networked Control Systems (NCS). Several models and methodologies have been proposed to measure different resources consumptions. The use of bandwidth, computational load and energy resources have been investigated. This analysis shows how the parameters of the system impacts on the resources efficiency. Moreover, the proposed system has been compared with its equivalent discrete-time solution. In the experiments, an application of NCS for mobile robots navigation has been set up and its resource usage efficiency has been analysed.

New Control Paradigms for Resources Saving: An Approach for Mobile Robots Navigation

PubMed Central

2018-01-01

In this work, an event-based control scheme is presented. The proposed system has been developed to solve control problems appearing in the field of Networked Control Systems (NCS). Several models and methodologies have been proposed to measure different resources consumptions. The use of bandwidth, computational load and energy resources have been investigated. This analysis shows how the parameters of the system impacts on the resources efficiency. Moreover, the proposed system has been compared with its equivalent discrete-time solution. In the experiments, an application of NCS for mobile robots navigation has been set up and its resource usage efficiency has been analysed. PMID:29346321

Event detection and localization for small mobile robots using reservoir computing.

PubMed

Antonelo, E A; Schrauwen, B; Stroobandt, D

2008-08-01

Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.

Dual adaptive dynamic control of mobile robots using neural networks.

PubMed

Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

2009-02-01

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

Development of a force-reflecting robotic platform for cardiac catheter navigation.

PubMed

Park, Jun Woo; Choi, Jaesoon; Pak, Hui-Nam; Song, Seung Joon; Lee, Jung Chan; Park, Yongdoo; Shin, Seung Min; Sun, Kyung

2010-11-01

Electrophysiological catheters are used for both diagnostics and clinical intervention. To facilitate more accurate and precise catheter navigation, robotic cardiac catheter navigation systems have been developed and commercialized. The authors have developed a novel force-reflecting robotic catheter navigation system. The system is a network-based master-slave configuration having a 3-degree of freedom robotic manipulator for operation with a conventional cardiac ablation catheter. The master manipulator implements a haptic user interface device with force feedback using a force or torque signal either measured with a sensor or estimated from the motor current signal in the slave manipulator. The slave manipulator is a robotic motion control platform on which the cardiac ablation catheter is mounted. The catheter motions-forward and backward movements, rolling, and catheter tip bending-are controlled by electromechanical actuators located in the slave manipulator. The control software runs on a real-time operating system-based workstation and implements the master/slave motion synchronization control of the robot system. The master/slave motion synchronization response was assessed with step, sinusoidal, and arbitrarily varying motion commands, and showed satisfactory performance with insignificant steady-state motion error. The current system successfully implemented the motion control function and will undergo safety and performance evaluation by means of animal experiments. Further studies on the force feedback control algorithm and on an active motion catheter with an embedded actuation mechanism are underway. © 2010, Copyright the Authors. Artificial Organs © 2010, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Performance Modeling of Network-Attached Storage Device Based Hierarchical Mass Storage Systems

NASA Technical Reports Server (NTRS)

Menasce, Daniel A.; Pentakalos, Odysseas I.

1995-01-01

Network attached storage devices improve I/O performance by separating control and data paths and eliminating host intervention during the data transfer phase. Devices are attached to both a high speed network for data transfer and to a slower network for control messages. Hierarchical mass storage systems use disks to cache the most recently used files and a combination of robotic and manually mounted tapes to store the bulk of the files in the file system. This paper shows how queuing network models can be used to assess the performance of hierarchical mass storage systems that use network attached storage devices as opposed to host attached storage devices. Simulation was used to validate the model. The analytic model presented here can be used, among other things, to evaluate the protocols involved in 1/0 over network attached devices.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots.

PubMed

Duarte, Miguel; Costa, Vasco; Gomes, Jorge; Rodrigues, Tiago; Silva, Fernando; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

PubMed Central

Duarte, Miguel; Costa, Vasco; Gomes, Jorge; Rodrigues, Tiago; Silva, Fernando; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers. PMID:26999614

Information-theoretic decomposition of embodied and situated systems.

PubMed

Da Rold, Federico

2018-07-01

The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artificial neural network learn a wall-following task through artificial evolution. At the end of the evolutionary process, time series are recorded from perceptual and motor neurons of selected robots. Information-theoretic measures are estimated on pairings of variables to unveil nonlinear interactions that structure the agent-environment system. Specifically, the mutual information is utilized to quantify the degree of dependence and the transfer entropy to detect the direction of the information flow. Furthermore, the system is analyzed with the local form of such measures, thus capturing the underlying dynamics of information. Results show that different measures are interdependent and complementary in uncovering aspects of the robots' interaction with the environment, as well as characteristics of the functional neural structure. Therefore, the set of information-theoretic measures provides a decomposition of the system, capturing the intricacy of nonlinear relationships that characterize robots' behavior and neural dynamics. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Structure, Design, and Closed-Loop Motion Control of a Differential Drive Soft Robot.

PubMed

Wu, Pang; Jiangbei, Wang; Yanqiong, Fei

2018-02-01

This article presents the structure, design, and motion control of an inchworm inspired pneumatic soft robot, which can perform differential movement. This robot mainly consists of two columns of pneumatic multi-airbags (actuators), one sensor, one baseboard, front feet, and rear feet. According to the different inflation time of left and right actuators, the robot can perform both linear and turning movements. The actuators of this robot are composed of multiple airbags, and the design of the airbags is analyzed. To deal with the nonlinear performance of the soft robot, we use radial basis function neural networks to train the turning ability of this robot on three different surfaces and create a mathematical model among coefficient of friction, deflection angle, and inflation time. Then, we establish the closed-loop automatic control model using three-axis electronic compass sensor. Finally, the automatic control model is verified by linear and turning movement experiments. According to the experiment, the robot can finish the linear and turning movements under the closed-loop control system.

Dynamic Routing and Coordination in Multi-Agent Networks

DTIC Science & Technology

2016-06-10

SECURITY CLASSIFICATION OF: Supported by this project, we designed innovative routing, planning and coordination strategies for robotic networks and...tasks partitioned among robots , in what order are they to be performed, and along which deterministic routes or according to which stochastic rules do...individual robots move. The fundamental novelties and our recent breakthroughs supported by this project are manifold: (1) the application 1

Fused smart sensor network for multi-axis forward kinematics estimation in industrial robots.

PubMed

Rodriguez-Donate, Carlos; Osornio-Rios, Roque Alfredo; Rivera-Guillen, Jesus Rooney; Romero-Troncoso, Rene de Jesus

2011-01-01

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint's angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot.

OWL-Net: A global network of robotic telescopes for satellite observation

NASA Astrophysics Data System (ADS)

Park, Jang-Hyun; Yim, Hong-Suh; Choi, Young-Jun; Jo, Jung Hyun; Moon, Hong-Kyu; Park, Young-Sik; Bae, Young-Ho; Park, Sun-Youp; Roh, Dong-Goo; Cho, Sungki; Choi, Eun-Jung; Kim, Myung-Jin; Choi, Jin

2018-07-01

The OWL-Net (Optical Wide-field patroL Network) is composed of 0.5-m wide-field optical telescopes spread over the globe (Mongolia, Morocco, Israel, South Korea, and USA). All the observing stations are identical, operated in a fully robotic manner, and controlled by the headquarters located in Daejeon, Korea. The main objective of the OWL-Net is to obtain the orbital information of Korean LEO and GEO satellites using purely optical means and to maintain their orbital elements. The aperture size of the mirror is 0.5 m in the Ritchey-Chretien configuration, and its field of view is 1.1 deg on the CCD sensor. The telescope is equipped with an electrically cooled 4 K CCD camera with a 9-μm pixel size, and its pixel scale is 1 arcsec/pixel. A chopper wheel with variable speed is adopted to obtain multiple points in a single shot. Each observatory is equipped with a heavy-duty environment monitoring system for robust robotic observation. The headquarters has components for status monitoring, scheduling, network operation, orbit calculation, and database management. The test-phase operation of the whole system began in early 2017, although test runs for individual sites began in 2015. Although the OWL-Net has 7 observation modes for artificial satellites and astronomical objects, we are concentrating on a few modes for LEO satellites and calibration during the early phase. Some early results and analysis for system performance will be presented, and their implications will be discussed.

Proceedings 3rd NASA/IEEE Workshop on Formal Approaches to Agent-Based Systems (FAABS-III)

NASA Technical Reports Server (NTRS)

Hinchey, Michael (Editor); Rash, James (Editor); Truszkowski, Walt (Editor); Rouff, Christopher (Editor)

2004-01-01

These preceedings contain 18 papers and 4 poster presentation, covering topics such as: multi-agent systems, agent-based control, formalism, norms, as well as physical and biological models of agent-based systems. Some applications presented in the proceedings include systems analysis, software engineering, computer networks and robot control.

Evaluation of a Home Biomonitoring Autonomous Mobile Robot.

PubMed

Dorronzoro Zubiete, Enrique; Nakahata, Keigo; Imamoglu, Nevrez; Sekine, Masashi; Sun, Guanghao; Gomez, Isabel; Yu, Wenwei

2016-01-01

Increasing population age demands more services in healthcare domain. It has been shown that mobile robots could be a potential solution to home biomonitoring for the elderly. Through our previous studies, a mobile robot system that is able to track a subject and identify his daily living activities has been developed. However, the system has not been tested in any home living scenarios. In this study we did a series of experiments to investigate the accuracy of activity recognition of the mobile robot in a home living scenario. The daily activities tested in the evaluation experiment include watching TV and sleeping. A dataset recorded by a distributed distance-measuring sensor network was used as a reference to the activity recognition results. It was shown that the accuracy is not consistent for all the activities; that is, mobile robot could achieve a high success rate in some activities but a poor success rate in others. It was found that the observation position of the mobile robot and subject surroundings have high impact on the accuracy of the activity recognition, due to the variability of the home living daily activities and their transitional process. The possibility of improvement of recognition accuracy has been shown too.

System of launchable mesoscale robots for distributed sensing

NASA Astrophysics Data System (ADS)

Yesin, Kemal B.; Nelson, Bradley J.; Papanikolopoulos, Nikolaos P.; Voyles, Richard M.; Krantz, Donald G.

1999-08-01

A system of launchable miniature mobile robots with various sensors as payload is used for distributed sensing. The robots are projected to areas of interest either by a robot launcher or by a human operator using standard equipment. A wireless communication network is used to exchange information with the robots. Payloads such as a MEMS sensor for vibration detection, a microphone and an active video module are used mainly to detect humans. The video camera provides live images through a wireless video transmitter and a pan-tilt mechanism expands the effective field of view. There are strict restrictions on total volume and power consumption of the payloads due to the small size of the robot. Emerging technologies are used to address these restrictions. In this paper, we describe the use of microrobotic technologies to develop active vision modules for the mesoscale robot. A single chip CMOS video sensor is used along with a miniature lens that is approximately the size of a sugar cube. The device consumes 100 mW; about 5 times less than the power consumption of a comparable CCD camera. Miniature gearmotors 3 mm in diameter are used to drive the pan-tilt mechanism. A miniature video transmitter is used to transmit analog video signals from the camera.

Best facilitated cortical activation during different stepping, treadmill, and robot-assisted walking training paradigms and speeds: A functional near-infrared spectroscopy neuroimaging study.

PubMed

Kim, Ha Yeon; Yang, Sung Phil; Park, Gyu Lee; Kim, Eun Joo; You, Joshua Sung Hyun

2016-01-01

Robot-assisted and treadmill-gait training are promising neurorehabilitation techniques, with advantages over conventional gait training, but the neural substrates underpinning locomotor control remain unknown particularly during different gait training modes and speeds. The present optical imaging study compared cortical activities during conventional stepping walking (SW), treadmill walking (TW), and robot-assisted walking (RW) at different speeds. Fourteen healthy subjects (6 women, mean age 30.06, years ± 4.53) completed three walking training modes (SW, TW, and RW) at various speeds (self-selected, 1.5, 2.0, 2.5, and 3.0  km/h). A functional near-infrared spectroscopy (fNIRS) system determined cerebral hemodynamic changes associated with cortical locomotor network areas in the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), prefrontal cortex (PFC), and sensory association cortex (SAC). There was increased cortical activation in the SMC, PMC, and SMA during different walking training modes. More global locomotor network activation was observed during RW than TW or SW. As walking speed increased, multiple locomotor network activations were observed, and increased activation power spectrum. This is the first empirical evidence highlighting the neural substrates mediating dynamic locomotion for different gait training modes and speeds. Fast, robot-assisted gait training best facilitated cortical activation associated with locomotor control.

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

NASA Astrophysics Data System (ADS)

Jha, Devesh K.; Chattopadhyay, Pritthi; Sarkar, Soumik; Ray, Asok

2016-05-01

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.

Integration of robotic resources into FORCEnet

NASA Astrophysics Data System (ADS)

Nguyen, Chinh; Carroll, Daniel; Nguyen, Hoa

2006-05-01

The Networked Intelligence, Surveillance, and Reconnaissance (NISR) project integrates robotic resources into Composeable FORCEnet to control and exploit unmanned systems over extremely long distances. The foundations are built upon FORCEnet-the U.S. Navy's process to define C4ISR for net-centric operations-and the Navy Unmanned Systems Common Control Roadmap to develop technologies and standards for interoperability, data sharing, publish-and-subscribe methodology, and software reuse. The paper defines the goals and boundaries for NISR with focus on the system architecture, including the design tradeoffs necessary for unmanned systems in a net-centric model. Special attention is given to two specific scenarios demonstrating the integration of unmanned ground and water surface vehicles into the open-architecture web-based command-and-control information-management system of Composeable FORCEnet. Planned spiral development for NISR will improve collaborative control, expand robotic sensor capabilities, address multiple domains including underwater and aerial platforms, and extend distributive communications infrastructure for battlespace optimization for unmanned systems in net-centric operations.

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1

NASA Technical Reports Server (NTRS)

Lea, Robert N. (Editor); Villarreal, James (Editor)

1991-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Houston, Clear Lake. The workshop was held April 11 to 13 at the Johnson Space Flight Center. Technical topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

Knowledge assistant: A sensor fusion framework for robotic environmental characterization

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

Feddema, J.T.; Rivera, J.J.; Tucker, S.D.

1996-12-01

A prototype sensor fusion framework called the {open_quotes}Knowledge Assistant{close_quotes} has been developed and tested on a gantry robot at Sandia National Laboratories. This Knowledge Assistant guides the robot operator during the planning, execution, and post analysis stages of the characterization process. During the planning stage, the Knowledge Assistant suggests robot paths and speeds based on knowledge of sensors available and their physical characteristics. During execution, the Knowledge Assistant coordinates the collection of data through a data acquisition {open_quotes}specialist.{close_quotes} During execution and post analysis, the Knowledge Assistant sends raw data to other {open_quotes}specialists,{close_quotes} which include statistical pattern recognition software, a neuralmore » network, and model-based search software. After the specialists return their results, the Knowledge Assistant consolidates the information and returns a report to the robot control system where the sensed objects and their attributes (e.g. estimated dimensions, weight, material composition, etc.) are displayed in the world model. This paper highlights the major components of this system.« less

NASA's International Lunar Network Anchor Nodes and Robotic Lunar Lander Project Update

NASA Technical Reports Server (NTRS)

Cohen, Barbara A.; Bassler, Julie A.; Ballard, Benjamin; Chavers, Greg; Eng, Doug S.; Hammond, Monica S.; Hill, Larry A.; Harris, Danny W.; Hollaway, Todd A.; Kubota, Sanae;

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

PubMed

Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

2016-05-01

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive artificial neural network for autonomous robot control

NASA Technical Reports Server (NTRS)

Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

A real-time spiking cerebellum model for learning robot control.

PubMed

Carrillo, Richard R; Ros, Eduardo; Boucheny, Christian; Coenen, Olivier J-M D

2008-01-01

We describe a neural network model of the cerebellum based on integrate-and-fire spiking neurons with conductance-based synapses. The neuron characteristics are derived from our earlier detailed models of the different cerebellar neurons. We tested the cerebellum model in a real-time control application with a robotic platform. Delays were introduced in the different sensorimotor pathways according to the biological system. The main plasticity in the cerebellar model is a spike-timing dependent plasticity (STDP) at the parallel fiber to Purkinje cell connections. This STDP is driven by the inferior olive (IO) activity, which encodes an error signal using a novel probabilistic low frequency model. We demonstrate the cerebellar model in a robot control system using a target-reaching task. We test whether the system learns to reach different target positions in a non-destructive way, therefore abstracting a general dynamics model. To test the system's ability to self-adapt to different dynamical situations, we present results obtained after changing the dynamics of the robotic platform significantly (its friction and load). The experimental results show that the cerebellar-based system is able to adapt dynamically to different contexts.

Training a Network of Electronic Neurons for Control of a Mobile Robot

NASA Astrophysics Data System (ADS)

Vromen, T. G. M.; Steur, E.; Nijmeijer, H.

An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input-output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.

Autonomous learning in humanoid robotics through mental imagery.

PubMed

Di Nuovo, Alessandro G; Marocco, Davide; Di Nuovo, Santo; Cangelosi, Angelo

2013-05-01

In this paper we focus on modeling autonomous learning to improve performance of a humanoid robot through a modular artificial neural networks architecture. A model of a neural controller is presented, which allows a humanoid robot iCub to autonomously improve its sensorimotor skills. This is achieved by endowing the neural controller with a secondary neural system that, by exploiting the sensorimotor skills already acquired by the robot, is able to generate additional imaginary examples that can be used by the controller itself to improve the performance through a simulated mental training. Results and analysis presented in the paper provide evidence of the viability of the approach proposed and help to clarify the rational behind the chosen model and its implementation. Copyright © 2012 Elsevier Ltd. All rights reserved.

A preliminary cyber-physical security assessment of the Robot Operating System (ROS)

NASA Astrophysics Data System (ADS)

McClean, Jarrod; Stull, Christopher; Farrar, Charles; Mascareñas, David

2013-05-01

Over the course of the last few years, the Robot Operating System (ROS) has become a highly popular software framework for robotics research. ROS has a very active developer community and is widely used for robotics research in both academia and government labs. The prevalence and modularity of ROS cause many people to ask the question: "What prevents ROS from being used in commercial or government applications?" One of the main problems that is preventing this increased use of ROS in these applications is the question of characterizing its security (or lack thereof). In the summer of 2012, a crowd sourced cyber-physical security contest was launched at the cyber security conference DEF CON 20 to begin the process of characterizing the security of ROS. A small-scale, car-like robot was configured as a cyber-physical security "honeypot" running ROS. DEFFCON-20 attendees were invited to find exploits and vulnerabilities in the robot while network traffic was collected. The results of this experiment provided some interesting insights and opened up many security questions pertaining to deployed robotic systems. The Federal Aviation Administration is tasked with opening up the civil airspace to commercial drones by September 2015 and driverless cars are already legal for research purposes in a number of states. Given the integration of these robotic devices into our daily lives, the authors pose the following question: "What security exploits can a motivated person with little-to-no experience in cyber security execute, given the wide availability of free cyber security penetration testing tools such as Metasploit?" This research focuses on applying common, low-cost, low-overhead, cyber-attacks on a robot featuring ROS. This work documents the effectiveness of those attacks.

Optimization-based Approach to Cross-layer Resource Management in Wireless Networked Control Systems

DTIC Science & Technology

2013-05-01

interest from both academia and industry [37], finding applications in un- manned robotic vehicles, automated highways and factories, smart homes and...is stable when the scaler varies slowly. The algorithm is further extended to utilize the slack resource in the network, which leads to the...model . . . . . . . . . . . . . . . . 66 Optimal sampling rate allocation formulation . . . . . 67 Price-based algorithm

From self-observation to imitation: visuomotor association on a robotic hand.

PubMed

Chaminade, Thierry; Oztop, Erhan; Cheng, Gordon; Kawato, Mitsuo

2008-04-15

Being at the crux of human cognition and behaviour, imitation has become the target of investigations ranging from experimental psychology and neurophysiology to computational sciences and robotics. It is often assumed that the imitation is innate, but it has more recently been argued, both theoretically and experimentally, that basic forms of imitation could emerge as a result of self-observation. Here, we tested this proposal on a realistic experimental platform, comprising an associative network linking a 16 degrees of freedom robotic hand and a simple visual system. We report that this minimal visuomotor association is sufficient to bootstrap basic imitation. Our results indicate that crucial features of human imitation, such as generalization to new actions, may emerge from a connectionist associative network. Therefore, we suggest that a behaviour as complex as imitation could be, at the neuronal level, founded on basic mechanisms of associative learning, a notion supported by a recent proposal on the developmental origin of mirror neurons. Our approach can be applied to the development of realistic cognitive architectures for humanoid robots as well as to shed new light on the cognitive processes at play in early human cognitive development.

Status of the NASA Micro Pulse Lidar Network (MPLNET): overview of the network and future plans, new version 3 data products, and the polarized MPL

NASA Astrophysics Data System (ADS)

Welton, Ellsworth J.; Stewart, Sebastian A.; Lewis, Jasper R.; Belcher, Larry R.; Campbell, James R.; Lolli, Simone

2018-04-01

The NASA Micro Pulse Lidar Network (MPLNET) is a global federated network of Micro-Pulse Lidars (MPL) co-located with the NASA Aerosol Robotic Network (AERONET). MPLNET began in 2000, and there are currently 17 long-term sites, numerous field campaigns, and more planned sites on the way. We have developed a new Version 3 processing system including the deployment of polarized MPLs across the network. Here we provide an overview of Version 3, the polarized MPL, and current and future plans.

Fused Smart Sensor Network for Multi-Axis Forward Kinematics Estimation in Industrial Robots

PubMed Central

Rodriguez-Donate, Carlos; Osornio-Rios, Roque Alfredo; Rivera-Guillen, Jesus Rooney; de Jesus Romero-Troncoso, Rene

2011-01-01

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint’s angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot. PMID:22163850

Indirect iterative learning control for a discrete visual servo without a camera-robot model.

PubMed

Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

2007-08-01

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

Terminal Sliding Mode-Based Consensus Tracking Control for Networked Uncertain Mechanical Systems on Digraphs.

PubMed

Chen, Gang; Song, Yongduan; Guan, Yanfeng

2018-03-01

This brief investigates the finite-time consensus tracking control problem for networked uncertain mechanical systems on digraphs. A new terminal sliding-mode-based cooperative control scheme is developed to guarantee that the tracking errors converge to an arbitrarily small bound around zero in finite time. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network is used at each node to approximate the local unknown dynamics. The control schemes are implemented in a fully distributed manner. The proposed control method eliminates some limitations in the existing terminal sliding-mode-based consensus control methods and extends the existing analysis methods to the case of directed graphs. Simulation results on networked robot manipulators are provided to show the effectiveness of the proposed control algorithms.

The Solaris-Panoptes Global Network of Robotic Telescopes and the Borowiec Satellite Laser Ranging System for SST: A Progress Report

NASA Astrophysics Data System (ADS)

Konacki, M.; Lejba, P.; Sybilski, P.; Pawłaszek, R.; Kozłowski, S.; Suchodolski, T.; Słonina, M.; Litwicki, M.; Sybilska, A.; Rogowska, B.; Kolb, U.; Burwitz, V.; Baader, J.; Groot, P.; Bloemen, S.; Ratajczak, M.; Hełminiak, K.; Borek, R.; Chodosiewicz, P.; Chimicz, A.

We present an update on the preparation of our assets that consists of a robotic network of eight optical telescopes and a laser ranging station for regular services in the SST domain. We report the development of new optical assets that include a double telescope system, Panoptes-1AB, and a new astrograph on our Solaris-3 telescope at the Siding Spring Observatory, Australia. Progress in the software development necessary for smooth SST operation includes a web based portal and an XML Azure Queue scheduling for the network giving easy access to our sensors. Astrometry24.net our new prototype cloud service for fast astrometry, streak detection and measurement with precision and performance results is also described. In the laser domain, for more than a year, Space Research Centre Borowiec laser station has regularly tracked space debris cooperative and uncooperative targets. The efforts of the stations’ staff have been focused on the tracking of typical rocket bodies from the LEO regime. Additionally, a second independent laser system fully dedicated to SST activities is under development. It will allow for an increased pace of operation of our consortium in the global SST laser domain.

Inverse kinematics problem in robotics using neural networks

NASA Technical Reports Server (NTRS)

Choi, Benjamin B.; Lawrence, Charles

1992-01-01

In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.

A Sustained Proximity Network for Multi-Mission Lunar Exploration

NASA Technical Reports Server (NTRS)

Soloff, Jason A.; Noreen, Gary; Deutsch, Leslie; Israel, David

2005-01-01

Tbe Vision for Space Exploration calls for an aggressive sequence of robotic missions beginning in 2008 to prepare for a human return to the Moon by 2020, with the goal of establishing a sustained human presence beyond low Earth orbit. A key enabler of exploration is reliable, available communication and navigation capabilities to support both human and robotic missions. An adaptable, sustainable communication and navigation architecture has been developed by Goddard Space Flight Center and the Jet Propulsion Laboratory to support human and robotic lunar exploration through the next two decades. A key component of the architecture is scalable deployment, with the infrastructure evolving as needs emerge, allowing NASA and its partner agencies to deploy an interoperable communication and navigation system in an evolutionary way, enabling cost effective, highly adaptable systems throughout the lunar exploration program.

[Minimally invasive surgery and robotic surgery: surgery 4.0?].

PubMed

Feußner, H; Wilhelm, D

2016-03-01

Surgery can only maintain its role in a highly competitive environment if results are continuously improved, accompanied by further reduction of the interventional trauma for patients and with justifiable costs. Significant impulse to achieve this goal was expected from minimally invasive surgery and, in particular, robotic surgery; however, a real breakthrough has not yet been achieved. Accordingly, the new strategic approach of cognitive surgery is required to optimize the provision of surgical treatment. A full scale integration of all modules utilized in the operating room (OR) into a comprehensive network and the development of systems with technical cognition are needed to upgrade the current technical environment passively controlled by the surgeon into an active collaborative support system (surgery 4.0). Only then can the true potential of minimally invasive surgery and robotic surgery be exploited.

Development of a neuromorphic control system for a lightweight humanoid robot

NASA Astrophysics Data System (ADS)

Folgheraiter, Michele; Keldibek, Amina; Aubakir, Bauyrzhan; Salakchinov, Shyngys; Gini, Giuseppina; Mauro Franchi, Alessio; Bana, Matteo

2017-03-01

A neuromorphic control system for a lightweight middle size humanoid biped robot built using 3D printing techniques is proposed. The control architecture consists of different modules capable to learn and autonomously reproduce complex periodic trajectories. Each module is represented by a chaotic Recurrent Neural Network (RNN) with a core of dynamic neurons randomly and sparsely connected with fixed synapses. A set of read-out units with adaptable synapses realize a linear combination of the neurons output in order to reproduce the target signals. Different experiments were conducted to find out the optimal initialization for the RNN’s parameters. From simulation results, using normalized signals obtained from the robot model, it was proven that all the instances of the control module can learn and reproduce the target trajectories with an average RMS error of 1.63 and variance 0.74.

NASA Tech Briefs, January 2013

NASA Technical Reports Server (NTRS)

2013-01-01

Topics include: Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector; Surface-Enhanced Raman Scattering Using Silica Whispering-Gallery Mode Resonators; 3D Hail Size Distribution Interpolation/Extrapolation Algorithm; Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels; Artificial Intelligence Software for Assessing Postural Stability; Transformers: Shape-Changing Space Systems Built with Robotic Textiles; Fibrillar Adhesive for Climbing Robots; Using Pre-Melted Phase Change Material to Keep Payloads in Space Warm for Hours without Power; Development of a Centrifugal Technique for the Microbial Bioburden Analysis of Freon (CFC-11); Microwave Sinterator Freeform Additive Construction System (MS-FACS); DSP/FPGA Design for a High-Speed Programmable S-Band Space Transceiver; On-Chip Power-Combining for High-Power Schottky Diode-Based Frequency Multipliers; FPGA Vision Data Architecture; Memory Circuit Fault Simulator; Ultra-Compact Transputer-Based Controller for High-Level, Multi-Axis Coordination; Regolith Advanced Surface Systems Operations Robot Excavator; Magnetically Actuated Seal; Hybrid Electrostatic/Flextensional Mirror for Lightweight, Large-Aperture, and Cryogenic Space Telescopes; System for Contributing and Discovering Derived Mission and Science Data; Remote Viewer for Maritime Robotics Software; Stackfile Database; Reachability Maps for In Situ Operations; JPL Space Telecommunications Radio System Operating Environment; RFI-SIM: RFI Simulation Package; ION Configuration Editor; Dtest Testing Software; IMPaCT - Integration of Missions, Programs, and Core Technologies; Integrated Systems Health Management (ISHM) Toolkit; Wind-Driven Wireless Networked System of Mobile Sensors for Mars Exploration; In Situ Solid Particle Generator; Analysis of the Effects of Streamwise Lift Distribution on Sonic Boom Signature; Rad-Tolerant, Thermally Stable, High-Speed Fiber-Optic Network for Harsh Environments; Towed Subsurface Optical Communications Buoy; High-Collection-Efficiency Fluorescence Detection Cell; Ultra-Compact, Superconducting Spectrometer-on-a-Chip at Submillimeter Wavelengths; UV Resonant Raman Spectrometer with Multi-Line Laser Excitation; Medicine Delivery Device with Integrated Sterilization and Detection; Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME; Airborne Tomographic Swath Ice Sounding Processing System; flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter; Estimating Torque Imparted on Spacecraft Using Telemetry; PowderSim: Lagrangian Discrete and Mesh-Free Continuum Simulation Code for Cohesive Soils; Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences; Metric Learning to Enhance Hyperspectral Image Segmentation; Basic Operational Robotics Instructional System; Sheet Membrane Spacesuit Water Membrane Evaporator; Advanced Materials and Manufacturing for Low-Cost, High-Performance Liquid Rocket Combustion Chambers; Motor Qualification for Long-Duration Mars Missions.

A physical model of sensorimotor interactions during locomotion

NASA Astrophysics Data System (ADS)

Klein, Theresa J.; Lewis, M. Anthony

2012-08-01

In this paper, we describe the development of a bipedal robot that models the neuromuscular architecture of human walking. The body is based on principles derived from human muscular architecture, using muscles on straps to mimic agonist/antagonist muscle action as well as bifunctional muscles. Load sensors in the straps model Golgi tendon organs. The neural architecture is a central pattern generator (CPG) composed of a half-center oscillator combined with phase-modulated reflexes that is simulated using a spiking neural network. We show that the interaction between the reflex system, body dynamics and CPG results in a walking cycle that is entrained to the dynamics of the system. We also show that the CPG helped stabilize the gait against perturbations relative to a purely reflexive system, and compared the joint trajectories to human walking data. This robot represents a complete physical, or ‘neurorobotic’, model of the system, demonstrating the usefulness of this type of robotics research for investigating the neurophysiological processes underlying walking in humans and animals.

Artificial pheromone for path selection by a foraging swarm of robots.

PubMed

Campo, Alexandre; Gutiérrez, Alvaro; Nouyan, Shervin; Pinciroli, Carlo; Longchamp, Valentin; Garnier, Simon; Dorigo, Marco

2010-11-01

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.

Grounding Action Words in the Sensorimotor Interaction with the World: Experiments with a Simulated iCub Humanoid Robot

PubMed Central

Marocco, Davide; Cangelosi, Angelo; Fischer, Kerstin; Belpaeme, Tony

2010-01-01

This paper presents a cognitive robotics model for the study of the embodied representation of action words. The present research will present how an iCub humanoid robot can learn the meaning of action words (i.e. words that represent dynamical events that happen in time) by physically interacting with the environment and linking the effects of its own actions with the behavior observed on the objects before and after the action. The control system of the robot is an artificial neural network trained to manipulate an object through a Back-Propagation-Through-Time algorithm. We will show that in the presented model the grounding of action words relies directly to the way in which an agent interacts with the environment and manipulates it. PMID:20725503

Control Architecture for Robotic Agent Command and Sensing

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance; Aghazarian, Hrand; Estlin, Tara; Gaines, Daniel

2008-01-01

Control Architecture for Robotic Agent Command and Sensing (CARACaS) is a recent product of a continuing effort to develop architectures for controlling either a single autonomous robotic vehicle or multiple cooperating but otherwise autonomous robotic vehicles. CARACaS is potentially applicable to diverse robotic systems that could include aircraft, spacecraft, ground vehicles, surface water vessels, and/or underwater vessels. CARACaS incudes an integral combination of three coupled agents: a dynamic planning engine, a behavior engine, and a perception engine. The perception and dynamic planning en - gines are also coupled with a memory in the form of a world model. CARACaS is intended to satisfy the need for two major capabilities essential for proper functioning of an autonomous robotic system: a capability for deterministic reaction to unanticipated occurrences and a capability for re-planning in the face of changing goals, conditions, or resources. The behavior engine incorporates the multi-agent control architecture, called CAMPOUT, described in An Architecture for Controlling Multiple Robots (NPO-30345), NASA Tech Briefs, Vol. 28, No. 11 (November 2004), page 65. CAMPOUT is used to develop behavior-composition and -coordination mechanisms. Real-time process algebra operators are used to compose a behavior network for any given mission scenario. These operators afford a capability for producing a formally correct kernel of behaviors that guarantee predictable performance. By use of a method based on multi-objective decision theory (MODT), recommendations from multiple behaviors are combined to form a set of control actions that represents their consensus. In this approach, all behaviors contribute simultaneously to the control of the robotic system in a cooperative rather than a competitive manner. This approach guarantees a solution that is good enough with respect to resolution of complex, possibly conflicting goals within the constraints of the mission to be accomplished by the vehicle(s).

Evaluation of teleoperated surgical robots in an enclosed undersea environment.

PubMed

Doarn, Charles R; Anvari, Mehran; Low, Thomas; Broderick, Timothy J

2009-05-01

The ability to support surgical care in an extreme environment is a significant issue for both military medicine and space medicine. Telemanipulation systems, those that can be remotely operated from a distant site, have been used extensively by the National Aeronautics and Space Administration (NASA) for a number of years. These systems, often called telerobots, have successfully been applied to surgical interventions. A further extension is to operate these robotic systems over data communication networks where robotic slave and master are separated by a great distance. NASA utilizes the National Oceanographic and Atmospheric Administration (NOAA) Aquarius underwater habitat as an analog environment for research and technology evaluation missions, known as NASA Extreme Environment Mission Operations (NEEMO). Three NEEMO missions have provided an opportunity to evaluate teleoperated surgical robotics by astronauts and surgeons. Three robotic systems were deployed to the habitat for evaluation during NEEMO 7, 9, and 12. These systems were linked via a telecommunications link to various sites for remote manipulation. Researchers in the habitat conducted a variety of tests to evaluate performance and applicability in extreme environments. Over three different NEEMO missions, components of the Automated Endoscopic System for Optimal Positioning (AESOP), the M7 Surgical System, and the RAVEN were deployed and evaluated. A number of factors were evaluated, including communication latency and semiautonomous functions. The M7 was modified to permit a remote surgeon the ability to insert a needle into simulated tissue with ultrasound guidance, resulting in the world's first semi-autonomous supervisory-controlled medical task. The deployment and operation of teleoperated surgical systems and semi-autonomous, supervisory-controlled tasks were successfully conducted.

SVR versus neural-fuzzy network controllers for the sagittal balance of a biped robot.

PubMed

Ferreira, João P; Crisóstomo, Manuel M; Coimbra, A Paulo

2009-12-01

The real-time balance control of an eight-link biped robot using a zero moment point (ZMP) dynamic model is difficult due to the processing time of the corresponding equations. To overcome this limitation, two alternative intelligent computing control techniques were compared: one based on support vector regression (SVR) and another based on a first-order Takagi-Sugeno-Kang (TSK)-type neural-fuzzy (NF) network. Both methods use the ZMP error and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods. The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is similar to a human gait that was acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs about 50 times faster.

Motor modules in robot-aided walking

PubMed Central

2012-01-01

Background It is hypothesized that locomotion is achieved by means of rhythm generating networks (central pattern generators) and muscle activation generating networks. This modular organization can be partly identified from the analysis of the muscular activity by means of factorization algorithms. The activity of rhythm generating networks is described by activation signals whilst the muscle intervention generating network is represented by motor modules (muscle synergies). In this study, we extend the analysis of modular organization of walking to the case of robot-aided locomotion, at varying speed and body weight support level. Methods Non Negative Matrix Factorization was applied on surface electromyographic signals of 8 lower limb muscles of healthy subjects walking in gait robotic trainer at different walking velocities (1 to 3km/h) and levels of body weight support (0 to 30%). Results The muscular activity of volunteers could be described by low dimensionality (4 modules), as for overground walking. Moreover, the activation signals during robot-aided walking were bursts of activation timed at specific phases of the gait cycle, underlying an impulsive controller, as also observed in overground walking. This modular organization was consistent across the investigated speeds, body weight support level, and subjects. Conclusions These results indicate that walking in a Lokomat robotic trainer is achieved by similar motor modules and activation signals as overground walking and thus supports the use of robotic training for re-establishing natural walking patterns. PMID:23043818

Planning in subsumption architectures

NASA Technical Reports Server (NTRS)

Chalfant, Eugene C.

1994-01-01

A subsumption planner using a parallel distributed computational paradigm based on the subsumption architecture for control of real-world capable robots is described. Virtual sensor state space is used as a planning tool to visualize the robot's anticipated effect on its environment. Decision sequences are generated based on the environmental situation expected at the time the robot must commit to a decision. Between decision points, the robot performs in a preprogrammed manner. A rudimentary, domain-specific partial world model contains enough information to extrapolate the end results of the rote behavior between decision points. A collective network of predictors operates in parallel with the reactive network forming a recurrrent network which generates plans as a hierarchy. Details of a plan segment are generated only when its execution is imminent. The use of the subsumption planner is demonstrated by a simple maze navigation problem.

Park Smart

NASA Technical Reports Server (NTRS)

1999-01-01

The Parking Garage Automation System (PGAS) is based on a technology developed by a NASA-sponsored project called Robot sensorSkin(TM). Merritt Systems, Inc., of Orlando, Florida, teamed up with NASA to improve robots working with critical flight hardware at Kennedy Space Center in Florida. The system, containing smart sensor modules and flexible printed circuit board skin, help robots to steer clear of obstacles using a proximity sensing system. Advancements in the sensor designs are being applied to various commercial applications, including the PGAS. The system includes a smartSensor(TM) network installed around and within public parking garages to autonomously guide motorists to open facilities, and once within, to free parking spaces. The sensors use non-invasive reflective-ultrasonic technology for high accuracy, high reliability, and low maintenance. The system is remotely programmable: it can be tuned to site-specific requirements, has variable range capability, and allows remote configuration, monitoring, and diagnostics. The sensors are immune to interference from metallic construction materials, such as rebar and steel beams. Inside the garage, smart routing signs mounted overhead or on poles in front of each row of parking spots guide the motorist precisely to free spaces.

Cooperative Autonomous Robots for Reconnaissance

DTIC Science & Technology

2009-03-06

REPORT Cooperative Autonomous Robots for Reconnaissance 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: Collaborating mobile robots equipped with WiFi ...Cooperative Autonomous Robots for Reconnaissance Report Title ABSTRACT Collaborating mobile robots equipped with WiFi transceivers are configured as a mobile...equipped with WiFi transceivers are configured as a mobile ad-hoc network. Algorithms are developed to take advantage of the distributed processing

Different-Level Simultaneous Minimization Scheme for Fault Tolerance of Redundant Manipulator Aided with Discrete-Time Recurrent Neural Network

PubMed Central

Jin, Long; Liao, Bolin; Liu, Mei; Xiao, Lin; Guo, Dongsheng; Yan, Xiaogang

2017-01-01

By incorporating the physical constraints in joint space, a different-level simultaneous minimization scheme, which takes both the robot kinematics and robot dynamics into account, is presented and investigated for fault-tolerant motion planning of redundant manipulator in this paper. The scheme is reformulated as a quadratic program (QP) with equality and bound constraints, which is then solved by a discrete-time recurrent neural network. Simulative verifications based on a six-link planar redundant robot manipulator substantiate the efficacy and accuracy of the presented acceleration fault-tolerant scheme, the resultant QP and the corresponding discrete-time recurrent neural network. PMID:28955217

Digital Signal Processing and Control for the Study of Gene Networks

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun

2016-04-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Digital Signal Processing and Control for the Study of Gene Networks.

PubMed

Shin, Yong-Jun

2016-04-22

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Digital Signal Processing and Control for the Study of Gene Networks

PubMed Central

Shin, Yong-Jun

2016-01-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks. PMID:27102828

Neural Network-Based Landmark Recognition and Navigation with IAMRs. Understanding the Principles of Thought and Behavior.

ERIC Educational Resources Information Center

Doty, Keith L.

1999-01-01

Research on neural networks and hippocampal function demonstrating how mammals construct mental maps and develop navigation strategies is being used to create Intelligent Autonomous Mobile Robots (IAMRs). Such robots are able to recognize landmarks and navigate without "vision." (SK)

On the Role of Sensory Feedbacks in Rowat–Selverston CPG to Improve Robot Legged Locomotion

PubMed Central

Amrollah, Elmira; Henaff, Patrick

2010-01-01

This paper presents the use of Rowat and Selverston-type of central pattern generator (CPG) to control locomotion. It focuses on the role of afferent exteroceptive and proprioceptive signals in the dynamic phase synchronization in CPG legged robots. The sensori-motor neural network architecture is evaluated to control a two-joint planar robot leg that slips on a rail. Then, the closed loop between the CPG and the mechanical system allows to study the modulation of rhythmic patterns and the effect of the sensing loop via sensory neurons during the locomotion task. Firstly simulations show that the proposed architecture easily allows to modulate rhythmic patterns of the leg, and therefore the velocity of the robot. Secondly, simulations show that sensori-feedbacks from foot/ground contact of the leg make the hip velocity smoother and larger. The results show that the Rowat–Selverston-type CPG with sensory feedbacks is an effective choice for building adaptive neural CPGs for legged robots. PMID:21228904

From biological and social network metaphors to coupled bio-social wireless networks

PubMed Central

Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

2010-01-01

Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

Neural self-tuning adaptive control of non-minimum phase system

NASA Technical Reports Server (NTRS)

Ho, Long T.; Bialasiewicz, Jan T.; Ho, Hai T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

Remote secure observing for the Faulkes Telescopes

NASA Astrophysics Data System (ADS)

Smith, Robert J.; Steele, Iain A.; Marchant, Jonathan M.; Fraser, Stephen N.; Mucke-Herzberg, Dorothea

2004-09-01

Since the Faulkes Telescopes are to be used by a wide variety of audiences, both powerful engineering level and simple graphical interfaces exist giving complete remote and robotic control of the telescope over the internet. Security is extremely important to protect the health of both humans and equipment. Data integrity must also be carefully guarded for images being delivered directly into the classroom. The adopted network architecture is described along with the variety of security and intrusion detection software. We use a combination of SSL, proxies, IPSec, and both Linux iptables and Cisco IOS firewalls to ensure only authenticated and safe commands are sent to the telescopes. With an eye to a possible future global network of robotic telescopes, the system implemented is capable of scaling linearly to any moderate (of order ten) number of telescopes.

A Face Attention Technique for a Robot Able to Interpret Facial Expressions

NASA Astrophysics Data System (ADS)

Simplício, Carlos; Prado, José; Dias, Jorge

Automatic facial expressions recognition using vision is an important subject towards human-robot interaction. Here is proposed a human face focus of attention technique and a facial expressions classifier (a Dynamic Bayesian Network) to incorporate in an autonomous mobile agent whose hardware is composed by a robotic platform and a robotic head. The focus of attention technique is based on the symmetry presented by human faces. By using the output of this module the autonomous agent keeps always targeting the human face frontally. In order to accomplish this, the robot platform performs an arc centered at the human; thus the robotic head, when necessary, moves synchronized. In the proposed probabilistic classifier the information is propagated, from the previous instant, in a lower level of the network, to the current instant. Moreover, to recognize facial expressions are used not only positive evidences but also negative.

Reactive navigation for autonomous guided vehicle using neuro-fuzzy techniques

NASA Astrophysics Data System (ADS)

Cao, Jin; Liao, Xiaoqun; Hall, Ernest L.

1999-08-01

A Neuro-fuzzy control method for navigation of an Autonomous Guided Vehicle robot is described. Robot navigation is defined as the guiding of a mobile robot to a desired destination or along a desired path in an environment characterized by as terrain and a set of distinct objects, such as obstacles and landmarks. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Neural network and fuzzy logic control techniques can improve real-time control performance for mobile robot due to its high robustness and error-tolerance ability. For a mobile robot to navigate automatically and rapidly, an important factor is to identify and classify mobile robots' currently perceptual environment. In this paper, a new approach of the current perceptual environment feature identification and classification, which are based on the analysis of the classifying neural network and the Neuro- fuzzy algorithm, is presented. The significance of this work lies in the development of a new method for mobile robot navigation.

A Space Station robot walker and its shared control software

NASA Technical Reports Server (NTRS)

Xu, Yangsheng; Brown, Ben; Aoki, Shigeru; Yoshida, Tetsuji

1994-01-01

In this paper, we first briefly overview the update of the self-mobile space manipulator (SMSM) configuration and testbed. The new robot is capable of projecting cameras anywhere interior or exterior of the Space Station Freedom (SSF), and will be an ideal tool for inspecting connectors, structures, and other facilities on SSF. Experiments have been performed under two gravity compensation systems and a full-scale model of a segment of SSF. This paper presents a real-time shared control architecture that enables the robot to coordinate autonomous locomotion and teleoperation input for reliable walking on SSF. Autonomous locomotion can be executed based on a CAD model and off-line trajectory planning, or can be guided by a vision system with neural network identification. Teleoperation control can be specified by a real-time graphical interface and a free-flying hand controller. SMSM will be a valuable assistant for astronauts in inspection and other EVA missions.

Mobile robotic sensors for perimeter detection and tracking.

PubMed

Clark, Justin; Fierro, Rafael

2007-02-01

Mobile robot/sensor networks have emerged as tools for environmental monitoring, search and rescue, exploration and mapping, evaluation of civil infrastructure, and military operations. These networks consist of many sensors each equipped with embedded processors, wireless communication, and motion capabilities. This paper describes a cooperative mobile robot network capable of detecting and tracking a perimeter defined by a certain substance (e.g., a chemical spill) in the environment. Specifically, the contributions of this paper are twofold: (i) a library of simple reactive motion control algorithms and (ii) a coordination mechanism for effectively carrying out perimeter-sensing missions. The decentralized nature of the methodology implemented could potentially allow the network to scale to many sensors and to reconfigure when adding/deleting sensors. Extensive simulation results and experiments verify the validity of the proposed cooperative control scheme.

A Gradient Optimization Approach to Adaptive Multi-Robot Control

DTIC Science & Technology

2009-09-01

implemented for deploying a group of three flying robots with downward facing cameras to monitor an environment on the ground. Thirdly, the multi-robot...theoretically proven, and implemented on multi-robot platforms. Thesis Supervisor: Daniela Rus Title: Professor of Electrical Engineering and Computer...often nonlinear, and they are coupled through a network which changes over time. Thirdly, implementing multi-robot controllers requires maintaining mul

Temporal Heterogeneity and the Value of Slowness in Robotic Systems

DTIC Science & Technology

2015-11-01

DIMENSIONS OF HETEROGENEITY By now, we have become reasonably good at designing distributed control strategies for teams of networked agents in order...possible is the recent emergence of a relatively mature theory of how to coordinate control decisions across teams of networked agents. In fact...Loris, illustrated in Figure 2. Figure 2: Slow mammals that serve as bio-inspiration for SlowBot Behavior [Wikipedia] Top: Tree

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

PubMed Central

Falotico, Egidio; Vannucci, Lorenzo; Ambrosano, Alessandro; Albanese, Ugo; Ulbrich, Stefan; Vasquez Tieck, Juan Camilo; Hinkel, Georg; Kaiser, Jacques; Peric, Igor; Denninger, Oliver; Cauli, Nino; Kirtay, Murat; Roennau, Arne; Klinker, Gudrun; Von Arnim, Axel; Guyot, Luc; Peppicelli, Daniel; Martínez-Cañada, Pablo; Ros, Eduardo; Maier, Patrick; Weber, Sandro; Huber, Manuel; Plecher, David; Röhrbein, Florian; Deser, Stefan; Roitberg, Alina; van der Smagt, Patrick; Dillman, Rüdiger; Levi, Paul; Laschi, Cecilia; Knoll, Alois C.; Gewaltig, Marc-Oliver

2017-01-01

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments. PMID:28179882

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform.

PubMed

Falotico, Egidio; Vannucci, Lorenzo; Ambrosano, Alessandro; Albanese, Ugo; Ulbrich, Stefan; Vasquez Tieck, Juan Camilo; Hinkel, Georg; Kaiser, Jacques; Peric, Igor; Denninger, Oliver; Cauli, Nino; Kirtay, Murat; Roennau, Arne; Klinker, Gudrun; Von Arnim, Axel; Guyot, Luc; Peppicelli, Daniel; Martínez-Cañada, Pablo; Ros, Eduardo; Maier, Patrick; Weber, Sandro; Huber, Manuel; Plecher, David; Röhrbein, Florian; Deser, Stefan; Roitberg, Alina; van der Smagt, Patrick; Dillman, Rüdiger; Levi, Paul; Laschi, Cecilia; Knoll, Alois C; Gewaltig, Marc-Oliver

2017-01-01

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain-body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 "Neurorobotics" of the Human Brain Project (HBP). At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.

Image Mapping and Visual Attention on the Sensory Ego-Sphere

NASA Technical Reports Server (NTRS)

Fleming, Katherine Achim; Peters, Richard Alan, II

2012-01-01

The Sensory Ego-Sphere (SES) is a short-term memory for a robot in the form of an egocentric, tessellated, spherical, sensory-motor map of the robot s locale. Visual attention enables fast alignment of overlapping images without warping or position optimization, since an attentional point (AP) on the composite typically corresponds to one on each of the collocated regions in the images. Such alignment speeds analysis of the multiple images of the area. Compositing and attention were performed two ways and compared: (1) APs were computed directly on the composite and not on the full-resolution images until the time of retrieval; and (2) the attentional operator was applied to all incoming imagery. It was found that although the second method was slower, it produced consistent and, thereby, more useful APs. The SES is an integral part of a control system that will enable a robot to learn new behaviors based on its previous experiences, and that will enable it to recombine its known behaviors in such a way as to solve related, but novel, task problems with apparent creativity. The approach is to combine sensory-motor data association and dimensionality reduction to learn navigation and manipulation tasks as sequences of basic behaviors that can be implemented with a small set of closed-loop controllers. Over time, the aggregate of behaviors and their transition probabilities form a stochastic network. Then given a task, the robot finds a path in the network that leads from its current state to the goal. The SES provides a short-term memory for the cognitive functions of the robot, association of sensory and motor data via spatio-temporal coincidence, direction of the attention of the robot, navigation through spatial localization with respect to known or discovered landmarks, and structured data sharing between the robot and human team members, the individuals in multi-robot teams, or with a C3 center.

Artificial evolution: a new path for artificial intelligence?

PubMed

Husbands, P; Harvey, I; Cliff, D; Miller, G

1997-06-01

Recently there have been a number of proposals for the use of artificial evolution as a radically new approach to the development of control systems for autonomous robots. This paper explains the artificial evolution approach, using work at Sussex to illustrate it. The paper revolves around a case study on the concurrent evolution of control networks and visual sensor morphologies for a mobile robot. Wider intellectual issues surrounding the work are discussed, as is the use of more abstract evolutionary simulations as a new potentially useful tool in theoretical biology.

Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task.

PubMed

Belkaid, Marwen; Cuperlier, Nicolas; Gaussier, Philippe

2017-01-01

Emotions play a significant role in internal regulatory processes. In this paper, we advocate four key ideas. First, novelty detection can be grounded in the sensorimotor experience and allow higher order appraisal. Second, cognitive processes, such as those involved in self-assessment, influence emotional states by eliciting affects like boredom and frustration. Third, emotional processes such as those triggered by self-assessment influence attentional processes. Last, close emotion-cognition interactions implement an efficient feedback loop for the purpose of top-down behavior regulation. The latter is what we call 'Emotional Metacontrol'. We introduce a model based on artificial neural networks. This architecture is used to control a robotic system in a visual search task. The emotional metacontrol intervenes to bias the robot visual attention during active object recognition. Through a behavioral and statistical analysis, we show that this mechanism increases the robot performance and fosters the exploratory behavior to avoid deadlocks.

A comprehensive overview of the applications of artificial life.

PubMed

Kim, Kyung-Joong; Cho, Sung-Bae

2006-01-01

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

Generation of Adaptive Gait Patterns for Quadruped Robot with CPG Network including Motor Dynamic Model

NASA Astrophysics Data System (ADS)

Son, Yurak; Kamano, Takuya; Yasuno, Takashi; Suzuki, Takayuki; Harada, Hironobu

This paper describes the generation of adaptive gait patterns using new Central Pattern Generators (CPGs) including motor dynamic models for a quadruped robot under various environment. The CPGs act as the flexible oscillators of the joints and make the desired angle of the joints. The CPGs are mutually connected each other, and the sets of their coupling parameters are adjusted by genetic algorithm so that the quadruped robot can realize the stable and adequate gait patterns. As a result of generation, the suitable CPG networks for not only a walking straight gait pattern but also rotation gait patterns are obtained. Experimental results demonstrate that the proposed CPG networks are effective to automatically adjust the adaptive gait patterns for the tested quadruped robot under various environment. Furthermore, the target tracking control based on image processing is achieved by combining the generated gait patterns.

Analyzing the multiple-target-multiple-agent scenario using optimal assignment algorithms

NASA Astrophysics Data System (ADS)

Kwok, Kwan S.; Driessen, Brian J.; Phillips, Cynthia A.; Tovey, Craig A.

1997-09-01

This work considers the problem of maximum utilization of a set of mobile robots with limited sensor-range capabilities and limited travel distances. The robots are initially in random positions. A set of robots properly guards or covers a region if every point within the region is within the effective sensor range of at least one vehicle. We wish to move the vehicles into surveillance positions so as to guard or cover a region, while minimizing the maximum distance traveled by any vehicle. This problem can be formulated as an assignment problem, in which we must optimally decide which robot to assign to which slot of a desired matrix of grid points. The cost function is the maximum distance traveled by any robot. Assignment problems can be solved very efficiently. Solution times for one hundred robots took only seconds on a silicon graphics crimson workstation. The initial positions of all the robots can be sampled by a central base station and their newly assigned positions communicated back to the robots. Alternatively, the robots can establish their own coordinate system with the origin fixed at one of the robots and orientation determined by the compass bearing of another robot relative to this robot. This paper presents example solutions to the multiple-target-multiple-agent scenario using a matching algorithm. Two separate cases with one hundred agents in each were analyzed using this method. We have found these mobile robot problems to be a very interesting application of network optimization methods, and we expect this to be a fruitful area for future research.

A neuro-inspired spike-based PID motor controller for multi-motor robots with low cost FPGAs.

PubMed

Jimenez-Fernandez, Angel; Jimenez-Moreno, Gabriel; Linares-Barranco, Alejandro; Dominguez-Morales, Manuel J; Paz-Vicente, Rafael; Civit-Balcells, Anton

2012-01-01

In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

A Neuro-Inspired Spike-Based PID Motor Controller for Multi-Motor Robots with Low Cost FPGAs

PubMed Central

Jimenez-Fernandez, Angel; Jimenez-Moreno, Gabriel; Linares-Barranco, Alejandro; Dominguez-Morales, Manuel J.; Paz-Vicente, Rafael; Civit-Balcells, Anton

2012-01-01

In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control. PMID:22666004

A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System.

PubMed

Wu, Defeng; Chen, Tianfei; Li, Aiguo

2016-08-30

A robot-based three-dimensional (3D) measurement system is presented. In the presented system, a structured light vision sensor is mounted on the arm of an industrial robot. Measurement accuracy is one of the most important aspects of any 3D measurement system. To improve the measuring accuracy of the structured light vision sensor, a novel sensor calibration approach is proposed to improve the calibration accuracy. The approach is based on a number of fixed concentric circles manufactured in a calibration target. The concentric circle is employed to determine the real projected centres of the circles. Then, a calibration point generation procedure is used with the help of the calibrated robot. When enough calibration points are ready, the radial alignment constraint (RAC) method is adopted to calibrate the camera model. A multilayer perceptron neural network (MLPNN) is then employed to identify the calibration residuals after the application of the RAC method. Therefore, the hybrid pinhole model and the MLPNN are used to represent the real camera model. Using a standard ball to validate the effectiveness of the presented technique, the experimental results demonstrate that the proposed novel calibration approach can achieve a highly accurate model of the structured light vision sensor.

Contact-force distribution optimization and control for quadruped robots using both gradient and adaptive neural networks.

PubMed

Li, Zhijun; Ge, Shuzhi Sam; Liu, Sibang

2014-08-01

This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.

Intelligent control and cooperation for mobile robots

NASA Astrophysics Data System (ADS)

Stingu, Petru Emanuel

The topic discussed in this work addresses the current research being conducted at the Automation & Robotics Research Institute in the areas of UAV quadrotor control and heterogenous multi-vehicle cooperation. Autonomy can be successfully achieved by a robot under the following conditions: the robot has to be able to acquire knowledge about the environment and itself, and it also has to be able to reason under uncertainty. The control system must react quickly to immediate challenges, but also has to slowly adapt and improve based on accumulated knowledge. The major contribution of this work is the transfer of the ADP algorithms from the purely theoretical environment to the complex real-world robotic platforms that work in real-time and in uncontrolled environments. Many solutions are adopted from those present in nature because they have been proven to be close to optimal in very different settings. For the control of a single platform, reinforcement learning algorithms are used to design suboptimal controllers for a class of complex systems that can be conceptually split in local loops with simpler dynamics and relatively weak coupling to the rest of the system. Optimality is enforced by having a global critic but the curse of dimensionality is avoided by using local actors and intelligent pre-processing of the information used for learning the optimal controllers. The system model is used for constructing the structure of the control system, but on top of that the adaptive neural networks that form the actors use the knowledge acquired during normal operation to get closer to optimal control. In real-world experiments, efficient learning is a strong requirement for success. This is accomplished by using an approximation of the system model to focus the learning for equivalent configurations of the state space. Due to the availability of only local data for training, neural networks with local activation functions are implemented. For the control of a formation of robots subjected to dynamic communication constraints, game theory is used in addition to reinforcement learning. The nodes maintain an extra set of state variables about all the other nodes that they can communicate to. The more important are trust and predictability. They are a way to incorporate knowledge acquired in the past into the control decisions taken by each node. The trust variable provides a simple mechanism for the implementation of reinforcement learning. For robot formations, potential field based control algorithms are used to generate the control commands. The formation structure changes due to the environment and due to the decisions of the nodes. It is a problem of building a graph and coalitions by having distributed decisions but still reaching an optimal behavior globally.

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

NASA Technical Reports Server (NTRS)

Culbert, Christopher J. (Editor)

1993-01-01

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

Fuzzy Petri nets to model vision system decisions within a flexible manufacturing system

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.; Buck, A. A.; Smith, R.

1994-10-01

The paper presents a Petri net approach to modelling, monitoring and control of the behavior of an FMS cell. The FMS cell described comprises a pick and place robot, vision system, CNC-milling machine and 3 conveyors. The work illustrates how the block diagrams in a hierarchical structure can be used to describe events at different levels of abstraction. It focuses on Fuzzy Petri nets (Fuzzy logic with Petri nets) including an artificial neural network (Fuzzy Neural Petri nets) to model and control vision system decisions and robot sequences within an FMS cell. This methodology can be used as a graphical modelling tool to monitor and control the imprecise, vague and uncertain situations, and determine the quality of the output product of an FMS cell.

Information at the edge of chaos in fluid neural networks

NASA Astrophysics Data System (ADS)

Solé, Ricard V.; Miramontes, Octavio

1995-01-01

Fluid neural networks, defined as neural nets of mobile elements with random activation, are studied by means of several approaches. They are proposed as a theoretical framework for a wide class of systems as insect societies, collectives of robots or the immune system. The critical properties of this model are also analysed, showing the existence of a critical boundary in parameter space where maximum information transfer occurs. In this sense, this boundary is in fact an example of the “edge of chaos” in systems like those described in our approach. Recent experiments with ant colonies seem to confirm our result.

Mixed reality framework for collective motion patterns of swarms with delay coupling

NASA Astrophysics Data System (ADS)

Szwaykowska, Klementyna; Schwartz, Ira

The formation of coherent patterns in swarms of interacting self-propelled autonomous agents is an important subject for many applications within the field of distributed robotic systems. However, there are significant logistical challenges associated with testing fully distributed systems in real-world settings. In this paper, we provide a rigorous theoretical justification for the use of mixed-reality experiments as a stepping stone to fully physical testing of distributed robotic systems. We also model and experimentally realize a mixed-reality large-scale swarm of delay-coupled agents. Our analyses, assuming agents communicating over an Erdos-Renyi network, demonstrate the existence of stable coherent patterns that can be achieved only with delay coupling and that are robust to decreasing network connectivity and heterogeneity in agent dynamics. We show how the bifurcation structure for emergence of different patterns changes with heterogeneity in agent acceleration capabilities and limited connectivity in the network as a function of coupling strength and delay. Our results are verified through simulation as well as preliminary experimental results of delay-induced pattern formation in a mixed-reality swarm. K. S. was a National Research Council postdoctoral fellow. I.B.S was supported by the U.S. Naval Research Laboratory funding (N0001414WX00023) and office of Naval Research (N0001414WX20610).

Mobile robots exploration through cnn-based reinforcement learning.

PubMed

Tai, Lei; Liu, Ming

2016-01-01

Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information.

Modeling Synergies in Large Human-Machine Networked Systems

DTIC Science & Technology

2013-09-25

Help Through Adaptive Autonomy”, CMU http://www.cs.cmu.edu/~softagents/theses/Kane_MS_thesis_2010.pdf Lingzhi Luo, MS in Robotics May 2012 Thesis...Affecting Situation Awareness in Unmanned Aerial Vehicles”, In Proceedings of AIAA, 2009. 165. Lingzhi Luo, Nilanjan Chakraborty, and Katia Sycara

Scalable Mobile Ad Hoc Network (MANET) to Enhance Situational Awareness in Distributed Small Unit Operations

DTIC Science & Technology

2015-06-01

raspberry pi , robotic operation system (ros), arduino 15. NUMBER OF PAGES 123 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT...51  2.  Raspberry Pi ...52  Figure 21.  The Raspberry Pi B+ model, from [24

Neural-Network Control Of Prosthetic And Robotic Hands

NASA Technical Reports Server (NTRS)

Buckley, Theresa M.

1991-01-01

Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

A Spiking Neural Network in sEMG Feature Extraction.

PubMed

Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor

2015-11-03

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

Adaptive Formation Control of Electrically Driven Nonholonomic Mobile Robots With Limited Information.

PubMed

Bong Seok Park; Jin Bae Park; Yoon Ho Choi

2011-08-01

We present a leader-follower-based adaptive formation control method for electrically driven nonholonomic mobile robots with limited information. First, an adaptive observer is developed under the condition that the velocity measurement is not available. With the proposed adaptive observer, the formation control part is designed to achieve the desired formation and guarantee the collision avoidance. In addition, neural network is employed to compensate the actuator saturation, and the projection algorithm is used to estimate the velocity information of the leader. It is shown, by using the Lyapunov theory, that all errors of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the performance of the proposed control system.

Neural network system for purposeful behavior based on foveal visual preprocessor

NASA Astrophysics Data System (ADS)

Golovan, Alexander V.; Shevtsova, Natalia A.; Klepatch, Arkadi A.

1996-10-01

Biologically plausible model of the system with an adaptive behavior in a priori environment and resistant to impairment has been developed. The system consists of input, learning, and output subsystems. The first subsystems classifies input patterns presented as n-dimensional vectors in accordance with some associative rule. The second one being a neural network determines adaptive responses of the system to input patterns. Arranged neural groups coding possible input patterns and appropriate output responses are formed during learning by means of negative reinforcement. Output subsystem maps a neural network activity into the system behavior in the environment. The system developed has been studied by computer simulation imitating a collision-free motion of a mobile robot. After some learning period the system 'moves' along a road without collisions. It is shown that in spite of impairment of some neural network elements the system functions reliably after relearning. Foveal visual preprocessor model developed earlier has been tested to form a kind of visual input to the system.

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

PubMed Central

Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng

2016-01-01

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints. PMID:27579033

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation.

PubMed

Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng; Kuo, Chung-Hsien

2016-01-01

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

PubMed Central

Hunt, Alexander; Szczecinski, Nicholas; Quinn, Roger

2017-01-01

Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal's body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems. PMID:28420977

Video-based convolutional neural networks for activity recognition from robot-centric videos

NASA Astrophysics Data System (ADS)

Ryoo, M. S.; Matthies, Larry

2016-05-01

In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

The navigation system of the JPL robot

NASA Technical Reports Server (NTRS)

Thompson, A. M.

1977-01-01

The control structure of the JPL research robot and the operations of the navigation subsystem are discussed. The robot functions as a network of interacting concurrent processes distributed among several computers and coordinated by a central executive. The results of scene analysis are used to create a segmented terrain model in which surface regions are classified by traversibility. The model is used by a path planning algorithm, PATH, which uses tree search methods to find the optimal path to a goal. In PATH, the search space is defined dynamically as a consequence of node testing. Maze-solving and the use of an associative data base for context dependent node generation are also discussed. Execution of a planned path is accomplished by a feedback guidance process with automatic error recovery.

Adaptive Control Strategies for Flexible Robotic Arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1996-01-01

The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.

Modular Neuronal Assemblies Embodied in a Closed-Loop Environment: Toward Future Integration of Brains and Machines

PubMed Central

Tessadori, Jacopo; Bisio, Marta; Martinoia, Sergio; Chiappalone, Michela

2012-01-01

Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3–8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: “random” and “modular” populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction. PMID:23248586

Fire Extinguisher Robot Using Ultrasonic Camera and Wi-Fi Network Controlled with Android Smartphone

NASA Astrophysics Data System (ADS)

Siregar, B.; Purba, H. A.; Efendi, S.; Fahmi, F.

2017-03-01

Fire disasters can occur anytime and result in high losses. It is often that fire fighters cannot access the source of fire due to the damage of building and very high temperature, or even due to the presence of explosive materials. With such constraints and high risk in the handling of the fire, a technological breakthrough that can help fighting the fire is necessary. Our paper proposed the use of robots to extinguish the fire that can be controlled from a specified distance in order to reduce the risk. A fire extinguisher robot was assembled with the intention to extinguish the fire by using a water pump as actuators. The robot movement was controlled using Android smartphones via Wi-fi networks utilizing Wi-fi module contained in the robot. User commands were sent to the microcontroller on the robot and then translated into robotic movement. We used ATMega8 as main microcontroller in the robot. The robot was equipped with cameras and ultrasonic sensors. The camera played role in giving feedback to user and in finding the source of fire. Ultrasonic sensors were used to avoid collisions during movement. Feedback provided by camera on the robot displayed on a screen of smartphone. In lab, testing environment the robot can move following the user command such as turn right, turn left, forward and backward. The ultrasonic sensors worked well that the robot can be stopped at a distance of less than 15 cm. In the fire test, the robot can perform the task properly to extinguish the fire.

A neural learning classifier system with self-adaptive constructivism for mobile robot control.

PubMed

Hurst, Jacob; Bull, Larry

2006-01-01

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

PubMed Central

Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub

2015-01-01

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases. PMID:26528986

A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots.

PubMed

Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub

2015-10-30

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.

Telerobotic Excavator Designed to Compete in NASA's Lunabotics Mining Competition

NASA Technical Reports Server (NTRS)

Nash, Rodney; Santin, Cara; Yousef, Ahmed; Nguyen, Thien; Helferty, John; Pillapakkam, Shriram

2011-01-01

The second annual NASA Lunabotics Mining competition is to be held in May 23-28, 2011. The goal of the competition is for teams of university level students to design, build, test and compete with a fully integrated lunar excavator on a simulated lunar surface. Our team, named Lunar Solutions I, will be representing Temple University's College of Engineering in the competition. The team's main goal was to build a robot which is able to compete with other teams, and ultimately win the competition. The main challenge of the competition was to build a wireless robot that can excavate and collect a minimum of 10 kilograms of the regolith material within 15 minutes. The robot must also be designed to operate in conditions similar to those found on the lunar surface. The design of the lunar excavator is constrained by a set of requirements determined by NASA and detailed in the competition's rulebook. The excavator must have the ability to communicate with the "main base" wirelessly, and over a Wi-Fi network. Human operators are located at a remote site approximately 60 meters away from the simulated lunar surface upon which the robot must excavate the lunar regolith surface. During the competition, the robot will operate in a separate area from the control room in an area referred to as the "Lunarena." From the control room, the operators will have to control the robot using visual feedback from cameras placed both within the arena and on the robot. Using this visual feedback the human operators control the robots movement using both keyboard and joystick commands. In order to place in the competition, a minimum of 10 kg of regolith material has to be excavated, collected, and dumped into a specific location. For that reason, the robot must be provided with an effective and powerful excavation system. Our excavator uses tracks for the drive system. After performing extensive research and trade studies, we concluded that tracks would be the most effective method for transporting the excavator. When designing the excavation system, we analyzed several design options from the previous year's competition. We decided to use a front loader to collect the material, rather than a conveyer belt system or auger. Many of the designs from last year's competition used a conveyer belt mechanism to mine regolith and dump it into a temporary storage bin place on the robot. Using the front end loader approach allowed us to combine the scooping system and storage unit, which meant that the excavation system required less space.

Bio-inspired grasp control in a robotic hand with massive sensorial input.

PubMed

Ascari, Luca; Bertocchi, Ulisse; Corradi, Paolo; Laschi, Cecilia; Dario, Paolo

2009-02-01

The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature's approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware-software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial-temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.

Cargo launch vehicles to low earth orbit

NASA Technical Reports Server (NTRS)

Austin, Robert E.

1990-01-01

There are two primary space transportation capabilities required to support both base programs and expanded mission requirements: earth-to-orbit (ETO) transportation systems and space transfer vehicle systems. Existing and new ETO vehicles required to support mission requirements, and planned robotic missions, along with currently planned ETO vehicles are provided. Lunar outposts, Mars' outposts, base and expanded model, ETO vehicles, advanced avionics technologies, expert systems, network architecture and operations systems, and technology transfer are discussed.

Architecting the Communication and Navigation Networks for NASA's Space Exploration Systems

NASA Technical Reports Server (NTRS)

Bhassin, Kul B.; Putt, Chuck; Hayden, Jeffrey; Tseng, Shirley; Biswas, Abi; Kennedy, Brian; Jennings, Esther H.; Miller, Ron A.; Hudiburg, John; Miller, Dave;

Educational Robotics: Open Questions and New Challenges

ERIC Educational Resources Information Center

Alimisis, Dimitris

2013-01-01

This paper investigates the current situation in the field of educational robotics and identifies new challenges and trends focusing on the use of robotic technologies as a tool that will support creativity and other 21st-century learning skills. Finally, conclusions and proposals are presented for promoting cooperation and networking of…

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

NASA Technical Reports Server (NTRS)

Culbert, Christopher J. (Editor)

1993-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

Intelligent, self-contained robotic hand

DOEpatents

Krutik, Vitaliy; Doo, Burt; Townsend, William T.; Hauptman, Traveler; Crowell, Adam; Zenowich, Brian; Lawson, John

2007-01-30

A robotic device has a base and at least one finger having at least two links that are connected in series on rotary joints with at least two degrees of freedom. A brushless motor and an associated controller are located at each joint to produce a rotational movement of a link. Wires for electrical power and communication serially connect the controllers in a distributed control network. A network operating controller coordinates the operation of the network, including power distribution. At least one, but more typically two to five, wires interconnect all the controllers through one or more joints. Motor sensors and external world sensors monitor operating parameters of the robotic hand. The electrical signal output of the sensors can be input anywhere on the distributed control network. V-grooves on the robotic hand locate objects precisely and assist in gripping. The hand is sealed, immersible and has electrical connections through the rotary joints for anodizing in a single dunk without masking. In various forms, this intelligent, self-contained, dexterous hand, or combinations of such hands, can perform a wide variety of object gripping and manipulating tasks, as well as locomotion and combinations of locomotion and gripping.

Process for anodizing a robotic device

DOEpatents

Townsend, William T [Weston, MA

2011-11-08

A robotic device has a base and at least one finger having at least two links that are connected in series on rotary joints with at least two degrees of freedom. A brushless motor and an associated controller are located at each joint to produce a rotational movement of a link. Wires for electrical power and communication serially connect the controllers in a distributed control network. A network operating controller coordinates the operation of the network, including power distribution. At least one, but more typically two to five, wires interconnect all the controllers through one or more joints. Motor sensors and external world sensors monitor operating parameters of the robotic hand. The electrical signal output of the sensors can be input anywhere on the distributed control network. V-grooves on the robotic hand locate objects precisely and assist in gripping. The hand is sealed, immersible and has electrical connections through the rotary joints for anodizing in a single dunk without masking. In various forms, this intelligent, self-contained, dexterous hand, or combinations of such hands, can perform a wide variety of object gripping and manipulating tasks, as well as locomotion and combinations of locomotion and gripping.

Neural Networks for Signal Processing and Control

NASA Astrophysics Data System (ADS)

Hesselroth, Ted Daniel

Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual cortex by the application of lateral interactions during the learning phase. The organization of the mature network is compared to that found in the macaque monkey by several analytical tests. The capacity of the network to process images is investigated. By a method of reconstructing the input images in terms of V1 activities, the simulations show that images can be faithfully represented in V1 by the proposed network. The signal-to-noise ratio of the image is improved by the representation, and compression ratios of well over two-hundred are possible. Lateral interactions between V1 neurons sharpen their orientational tuning. We further study the dynamics of the processing, showing that the rate of decrease of the error of the reconstruction is maximized for the receptive fields used. Lastly, we employ a Fokker-Planck equation for a more detailed prediction of the error value vs. time. The Fokker-Planck equation for an underdamped system with a driving force is derived, yielding an energy-dependent diffusion coefficient which is the integral of the spectral densities of the force and the velocity of the system. The theory is applied to correlated noise activation and resonant activation. Simulation results for the error of the network vs time are compared to the solution of the Fokker-Planck equation.

Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed

PubMed Central

Garnier, Simon; Combe, Maud; Jost, Christian; Theraulaz, Guy

2013-01-01

Interactions between individuals and the structure of their environment play a crucial role in shaping self-organized collective behaviors. Recent studies have shown that ants crossing asymmetrical bifurcations in a network of galleries tend to follow the branch that deviates the least from their incoming direction. At the collective level, the combination of this tendency and the pheromone-based recruitment results in a greater likelihood of selecting the shortest path between the colony's nest and a food source in a network containing asymmetrical bifurcations. It was not clear however what the origin of this behavioral bias is. Here we propose that it results from a simple interaction between the behavior of the ants and the geometry of the network, and that it does not require the ability to measure the angle of the bifurcation. We tested this hypothesis using groups of ant-like robots whose perceptual and cognitive abilities can be fully specified. We programmed them only to lay down and follow light trails, avoid obstacles and move according to a correlated random walk, but not to use more sophisticated orientation methods. We recorded the behavior of the robots in networks of galleries presenting either only symmetrical bifurcations or a combination of symmetrical and asymmetrical bifurcations. Individual robots displayed the same pattern of branch choice as individual ants when crossing a bifurcation, suggesting that ants do not actually measure the geometry of the bifurcations when travelling along a pheromone trail. Finally at the collective level, the group of robots was more likely to select one of the possible shorter paths between two designated areas when moving in an asymmetrical network, as observed in ants. This study reveals the importance of the shape of trail networks for foraging in ants and emphasizes the underestimated role of the geometrical properties of transportation networks in general. PMID:23555202

Telescope networking and user support via Remote Telescope Markup Language

NASA Astrophysics Data System (ADS)

Hessman, Frederic V.; Pennypacker, Carlton R.; Romero-Colmenero, Encarni; Tuparev, Georg

2004-09-01

Remote Telescope Markup Language (RTML) is an XML-based interface/document format designed to facilitate the exchange of astronomical observing requests and results between investigators and observatories as well as within networks of observatories. While originally created to support simple imaging telescope requests (Versions 1.0-2.1), RTML Version 3.0 now supports a wide range of applications, from request preparation, exposure calculation, spectroscopy, and observation reports to remote telescope scheduling, target-of-opportunity observations and telescope network administration. The elegance of RTML is that all of this is made possible using a public XML Schema which provides a general-purpose, easily parsed, and syntax-checked medium for the exchange of astronomical and user information while not restricting or otherwise constraining the use of the information at either end. Thus, RTML can be used to connect heterogeneous systems and their users without requiring major changes in existing local resources and procedures. Projects as very different as a number of advanced amateur observatories, the global Hands-On Universe project, the MONET network (robotic imaging), the STELLA consortium (robotic spectroscopy), and the 11-m Southern African Large Telescope are now using or intending to use RTML in various forms and for various purposes.

Conceptual second-generation lunar equipment

NASA Technical Reports Server (NTRS)

1990-01-01

The spring 1990 Introduction to Design class was asked to conceptually design second-generation lunar vehicles and equipment as a semester design project. The basic assumption made in designing second-generation lunar vehicles and equipment was that a network of permanent lunar bases already existed. The designs were to facilitate the transportation of personnel and materials. The eight topics to choose from included flying vehicles, ground-based vehicles, robotic arms, and life support systems. Two teams of two or three members competed on each topic and results were exhibited at a formal presentation. A clean-propellant powered lunar flying transport vehicle, an extra-vehicular activity life support system, a pressurized lunar rover for greater distances, and a robotic arm design project are discussed.

Behavioral Mapless Navigation Using Rings

NASA Technical Reports Server (NTRS)

Monroe, Randall P.; Miller, Samuel A.; Bradley, Arthur T.

2012-01-01

This paper presents work on the development and implementation of a novel approach to robotic navigation. In this system, map-building and localization for obstacle avoidance are discarded in favor of moment-by-moment behavioral processing of the sonar sensor data. To accomplish this, we developed a network of behaviors that communicate through the passing of rings, data structures that are similar in form to the sonar data itself and express the decisions of each behavior. Through the use of these rings, behaviors can moderate each other, conflicting impulses can be mediated, and designers can easily connect modules to create complex emergent navigational techniques. We discuss the development of a number of these modules and their successful use as a navigation system in the Trinity omnidirectional robot.

Novel Design of a Soft Lightweight Pneumatic Continuum Robot Arm with Decoupled Variable Stiffness and Positioning.

PubMed

Giannaccini, Maria Elena; Xiang, Chaoqun; Atyabi, Adham; Theodoridis, Theo; Nefti-Meziani, Samia; Davis, Steve

2018-02-01

Soft robot arms possess unique capabilities when it comes to adaptability, flexibility, and dexterity. In addition, soft systems that are pneumatically actuated can claim high power-to-weight ratio. One of the main drawbacks of pneumatically actuated soft arms is that their stiffness cannot be varied independently from their end-effector position in space. The novel robot arm physical design presented in this article successfully decouples its end-effector positioning from its stiffness. An experimental characterization of this ability is coupled with a mathematical analysis. The arm combines the light weight, high payload to weight ratio and robustness of pneumatic actuation with the adaptability and versatility of variable stiffness. Light weight is a vital component of the inherent safety approach to physical human-robot interaction. To characterize the arm, a neural network analysis of the curvature of the arm for different input pressures is performed. The curvature-pressure relationship is also characterized experimentally.

Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task

PubMed Central

Cuperlier, Nicolas; Gaussier, Philippe

2017-01-01

Emotions play a significant role in internal regulatory processes. In this paper, we advocate four key ideas. First, novelty detection can be grounded in the sensorimotor experience and allow higher order appraisal. Second, cognitive processes, such as those involved in self-assessment, influence emotional states by eliciting affects like boredom and frustration. Third, emotional processes such as those triggered by self-assessment influence attentional processes. Last, close emotion-cognition interactions implement an efficient feedback loop for the purpose of top-down behavior regulation. The latter is what we call ‘Emotional Metacontrol’. We introduce a model based on artificial neural networks. This architecture is used to control a robotic system in a visual search task. The emotional metacontrol intervenes to bias the robot visual attention during active object recognition. Through a behavioral and statistical analysis, we show that this mechanism increases the robot performance and fosters the exploratory behavior to avoid deadlocks. PMID:28934291

Novel Design of a Soft Lightweight Pneumatic Continuum Robot Arm with Decoupled Variable Stiffness and Positioning

PubMed Central

Xiang, Chaoqun; Atyabi, Adham; Theodoridis, Theo; Nefti-Meziani, Samia; Davis, Steve

2018-01-01

Abstract Soft robot arms possess unique capabilities when it comes to adaptability, flexibility, and dexterity. In addition, soft systems that are pneumatically actuated can claim high power-to-weight ratio. One of the main drawbacks of pneumatically actuated soft arms is that their stiffness cannot be varied independently from their end-effector position in space. The novel robot arm physical design presented in this article successfully decouples its end-effector positioning from its stiffness. An experimental characterization of this ability is coupled with a mathematical analysis. The arm combines the light weight, high payload to weight ratio and robustness of pneumatic actuation with the adaptability and versatility of variable stiffness. Light weight is a vital component of the inherent safety approach to physical human-robot interaction. To characterize the arm, a neural network analysis of the curvature of the arm for different input pressures is performed. The curvature-pressure relationship is also characterized experimentally. PMID:29412080

Intelligent Vision Systems Independent Research and Development (IR&D) 2006

NASA Technical Reports Server (NTRS)

Patrick, Clinton; Chavis, Katherine

2006-01-01

This report summarizes results in conduct of research sponsored by the 2006 Independent Research and Development (IR&D) program at Marshall Space Flight Center (MSFC) at Redstone Arsenal, Alabama. The focus of this IR&D is neural network (NN) technology provided by Imagination Engines, Incorporated (IEI) of St. Louis, Missouri. The technology already has many commercial, military, and governmental applications, and a rapidly growing list of other potential spin-offs. The goal for this IR&D is implementation and demonstration of the technology for autonomous robotic operations, first in software and ultimately in one or more hardware realizations. Testing is targeted specifically to the MSFC Flat Floor, but may also include other robotic platforms at MSFC, as time and funds permit. For the purpose of this report, the NN technology will be referred to by IEI's designation for a subset configuration of its patented technology suite: Self-Training Autonomous Neural Network Object (STANNO).

Estimation of Visual Maps with a Robot Network Equipped with Vision Sensors

PubMed Central

Gil, Arturo; Reinoso, Óscar; Ballesta, Mónica; Juliá, Miguel; Payá, Luis

2010-01-01

In this paper we present an approach to the Simultaneous Localization and Mapping (SLAM) problem using a team of autonomous vehicles equipped with vision sensors. The SLAM problem considers the case in which a mobile robot is equipped with a particular sensor, moves along the environment, obtains measurements with its sensors and uses them to construct a model of the space where it evolves. In this paper we focus on the case where several robots, each equipped with its own sensor, are distributed in a network and view the space from different vantage points. In particular, each robot is equipped with a stereo camera that allow the robots to extract visual landmarks and obtain relative measurements to them. We propose an algorithm that uses the measurements obtained by the robots to build a single accurate map of the environment. The map is represented by the three-dimensional position of the visual landmarks. In addition, we consider that each landmark is accompanied by a visual descriptor that encodes its visual appearance. The solution is based on a Rao-Blackwellized particle filter that estimates the paths of the robots and the position of the visual landmarks. The validity of our proposal is demonstrated by means of experiments with a team of real robots in a office-like indoor environment. PMID:22399930

Estimation of visual maps with a robot network equipped with vision sensors.

PubMed

Gil, Arturo; Reinoso, Óscar; Ballesta, Mónica; Juliá, Miguel; Payá, Luis

2010-01-01

In this paper we present an approach to the Simultaneous Localization and Mapping (SLAM) problem using a team of autonomous vehicles equipped with vision sensors. The SLAM problem considers the case in which a mobile robot is equipped with a particular sensor, moves along the environment, obtains measurements with its sensors and uses them to construct a model of the space where it evolves. In this paper we focus on the case where several robots, each equipped with its own sensor, are distributed in a network and view the space from different vantage points. In particular, each robot is equipped with a stereo camera that allow the robots to extract visual landmarks and obtain relative measurements to them. We propose an algorithm that uses the measurements obtained by the robots to build a single accurate map of the environment. The map is represented by the three-dimensional position of the visual landmarks. In addition, we consider that each landmark is accompanied by a visual descriptor that encodes its visual appearance. The solution is based on a Rao-Blackwellized particle filter that estimates the paths of the robots and the position of the visual landmarks. The validity of our proposal is demonstrated by means of experiments with a team of real robots in a office-like indoor environment.

A magnetic compatible supernumerary robotic finger for functional magnetic resonance imaging (fMRI) acquisitions: Device description and preliminary results.

PubMed

Hussain, Irfan; Santarnecchi, Emiliano; Leo, Andrea; Ricciardi, Emiliano; Rossi, Simone; Prattichizzo, Domenico

2017-07-01

The Supernumerary robotic limbs are a recently introduced class of wearable robots that, differently from traditional prostheses and exoskeletons, aim at adding extra effectors (i.e., arms, legs, or fingers) to the human user, rather than substituting or enhancing the natural ones. However, it is still undefined whether the use of supernumerary robotic limbs could specifically lead to neural modifications in brain dynamics. The illusion of owning the part of body has been already proven in many experimental observations, such as those relying on multisensory integration (e.g., rubber hand illusion), prosthesis and even on virtual reality. In this paper we present a description of a novel magnetic compatible supernumerary robotic finger together with preliminary observations from two functional magnetic resonance imaging (fMRI) experiments, in which brain activity was measured before and after a period of training with the robotic device, and during the use of the novel MRI-compatible version of the supernumerary robotic finger. Results showed that the usage of the MR-compatible robotic finger is safe and does not produce artifacts on MRI images. Moreover, the training with the supernumerary robotic finger recruits a network of motor-related cortical regions (i.e. primary and supplementary motor areas), hence the same motor network of a fully physiological voluntary motor gestures.

AstroNet: A Tool Set for Simultaneous, Multi-Site Observations of Astronomical Objects

NASA Technical Reports Server (NTRS)

Chakrabarti, Supriya

1995-01-01

Earth-based, fully automatic "robotic" telescopes have been in routine operation for a number of years. As their number grows and their distribution becomes global, increasing attention is being given to forming networks of various sorts that will allow them, as a group, to make observations 24 hours a day in both hemispheres. We have suggested that telescopes based in space be part of this network. We further suggested that any telescope on this network be capable of asking, almost in real time, that other robotic telescopes perform support observations for them. When a target of opportunity required support observations, the system would determine which telescope(s) in the network would be most appropriate to make the observations and formulate a request to do so. Because the network would be comprised of telescopes located in widely distributed regions, this system would guarantee continuity of observations This report summarizes our efforts under this contract. We proposed to develop a set of data collection and display tools to aid simultaneous observation of astronomical targets from a number of observing sites. We planned to demonstrate the usefulness of this toolset for simultaneous multi-site observation of astronomical targets. Possible candidates or the proposed demonstration included the Extreme Ultraviolet Explorer (EUVE), International Ultraviolet Explorer (IUE), and ALEXIS, sounding rocket experiments. Ground-based observatories operated by the University of California, Berkeley, the Jet Propulsion Laboratory, and Fairborn Observatory in Mesa, Arizona were to be used to demonstrate the proposed concept. Although the demonstration was to have involved astronomical investigations, the tools were to have been applicable to a large number of scientific disciplines. The software tools and systems developed as a result of the work were to have been made available to the scientific community.

The 1991 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1991-01-01

The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications.

Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes

PubMed Central

Sheng, Weihua; Junior, Francisco Erivaldo Fernandes; Li, Shaobo

2018-01-01

Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy. PMID:29757211

Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes.

PubMed

Yang, Guanci; Yang, Jing; Sheng, Weihua; Junior, Francisco Erivaldo Fernandes; Li, Shaobo

2018-05-12

Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy.

Design and implementation of a software package to control a network of robotic observatories

NASA Astrophysics Data System (ADS)

Tuparev, G.; Nicolova, I.; Zlatanov, B.; Mihova, D.; Popova, I.; Hessman, F. V.

2006-09-01

We present a description of a reusable software package able to control a large, heterogeneous network of fully and semi-robotic observatories initially developed to run the MONET network of two 1.2 m telescopes. Special attention is given to the design of a robust, long-term observation scheduler which also allows the trading of observation time and facilities within various networks. The handling of the ``Phase I&II" project-development process, the time-accounting between complex organizational structures, and usability issues for making the package accessible not only to professional astronomers, but also to amateurs and high-school students is discussed. A simple RTML-based solution to link multiple networks is demonstrated.

Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms

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

Kurt Derr; Milos Manic

A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersionmore » of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.« less

Robust Kalman filtering cooperated Elman neural network learning for vision-sensing-based robotic manipulation with global stability.

PubMed

Zhong, Xungao; Zhong, Xunyu; Peng, Xiafu

2013-10-08

In this paper, a global-state-space visual servoing scheme is proposed for uncalibrated model-independent robotic manipulation. The scheme is based on robust Kalman filtering (KF), in conjunction with Elman neural network (ENN) learning techniques. The global map relationship between the vision space and the robotic workspace is learned using an ENN. This learned mapping is shown to be an approximate estimate of the Jacobian in global space. In the testing phase, the desired Jacobian is arrived at using a robust KF to improve the ENN learning result so as to achieve robotic precise convergence of the desired pose. Meanwhile, the ENN weights are updated (re-trained) using a new input-output data pair vector (obtained from the KF cycle) to ensure robot global stability manipulation. Thus, our method, without requiring either camera or model parameters, avoids the corrupted performances caused by camera calibration and modeling errors. To demonstrate the proposed scheme's performance, various simulation and experimental results have been presented using a six-degree-of-freedom robotic manipulator with eye-in-hand configurations.

Trust-based Task Assignment in Military Tactical Networks

DTIC Science & Technology

2012-06-01

Decentralized   Auctions  for  Robust   Task   Allocation ,“  IEEE Trans. on Robotics, vol. 25, no. 4, pp. 912‐926, Aug. 2009.  [10] B.B. Choudhury, B.B. Biswal, and D...Choi et al. [9]  and Choudhury et al. [10] investigated market‐ based   task   allocation  algorithms for multi‐robot systems. Choi et  al. [9] proposed a... consensus ‐ based  bundle algorithm for rapid conflict‐free matching between  tasks  and robots.  Choudhury et al. [10] conducted an empirical study on a

CPG-inspired workspace trajectory generation and adaptive locomotion control for quadruped robots.

PubMed

Liu, Chengju; Chen, Qijun; Wang, Danwei

2011-06-01

This paper deals with the locomotion control of quadruped robots inspired by the biological concept of central pattern generator (CPG). A control architecture is proposed with a 3-D workspace trajectory generator and a motion engine. The workspace trajectory generator generates adaptive workspace trajectories based on CPGs, and the motion engine realizes joint motion imputes. The proposed architecture is able to generate adaptive workspace trajectories online by tuning the parameters of the CPG network to adapt to various terrains. With feedback information, a quadruped robot can walk through various terrains with adaptive joint control signals. A quadruped platform AIBO is used to validate the proposed locomotion control system. The experimental results confirm the effectiveness of the proposed control architecture. A comparison by experiments shows the superiority of the proposed method against the traditional CPG-joint-space control method.

Achieving "organic compositionality" through self-organization: reviews on brain-inspired robotics experiments.

PubMed

Tani, Jun; Nishimoto, Ryunosuke; Paine, Rainer W

2008-05-01

The current paper examines how compositional structures can self-organize in given neuro-dynamical systems when robot agents are forced to learn multiple goal-directed behaviors simultaneously. Firstly, we propose a basic model accounting for the roles of parietal-premotor interactions for representing skills for goal-directed behaviors. The basic model had been implemented in a set of robotics experiments employing different neural network architectures. The comparative reviews among those experimental results address the issues of local vs distributed representations in representing behavior and the effectiveness of level structures associated with different sensory-motor articulation mechanisms. It is concluded that the compositional structures can be acquired "organically" by achieving generalization in learning and by capturing the contextual nature of skilled behaviors under specific conditions. Furthermore, the paper discusses possible feedback for empirical neuroscience studies in the future.

Automatic learning rate adjustment for self-supervising autonomous robot control

NASA Technical Reports Server (NTRS)

Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

PubMed

Li, Zhijun; Su, Chun-Yi

2013-09-01

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

Control of Synchronization Regimes in Networks of Mobile Interacting Agents

NASA Astrophysics Data System (ADS)

Perez-Diaz, Fernando; Zillmer, Ruediger; Groß, Roderich

2017-05-01

We investigate synchronization in a population of mobile pulse-coupled agents with a view towards implementations in swarm-robotics systems and mobile sensor networks. Previous theoretical approaches dealt with range and nearest-neighbor interactions. In the latter case, a synchronization-hindering regime for intermediate agent mobility is found. We investigate the robustness of this intermediate regime under practical scenarios. We show that synchronization in the intermediate regime can be predicted by means of a suitable metric of the phase response curve. Furthermore, we study more-realistic K -nearest-neighbor and cone-of-vision interactions, showing that it is possible to control the extent of the synchronization-hindering region by appropriately tuning the size of the neighborhood. To assess the effect of noise, we analyze the propagation of perturbations over the network and draw an analogy between the response in the hindering regime and stable chaos. Our findings reveal the conditions for the control of clock or activity synchronization of agents with intermediate mobility. In addition, the emergence of the intermediate regime is validated experimentally using a swarm of physical robots interacting with cone-of-vision interactions.

Automatic control system generation for robot design validation

NASA Technical Reports Server (NTRS)

Bacon, James A. (Inventor); English, James D. (Inventor)

2012-01-01

The specification and drawings present a new method, system and software product for and apparatus for generating a robotic validation system for a robot design. The robotic validation system for the robot design of a robotic system is automatically generated by converting a robot design into a generic robotic description using a predetermined format, then generating a control system from the generic robotic description and finally updating robot design parameters of the robotic system with an analysis tool using both the generic robot description and the control system.

BGen: A UML Behavior Network Generator Tool

NASA Technical Reports Server (NTRS)

Huntsberger, Terry; Reder, Leonard J.; Balian, Harry

2010-01-01

BGen software was designed for autogeneration of code based on a graphical representation of a behavior network used for controlling automatic vehicles. A common format used for describing a behavior network, such as that used in the JPL-developed behavior-based control system, CARACaS ["Control Architecture for Robotic Agent Command and Sensing" (NPO-43635), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 40] includes a graph with sensory inputs flowing through the behaviors in order to generate the signals for the actuators that drive and steer the vehicle. A computer program to translate Unified Modeling Language (UML) Freeform Implementation Diagrams into a legacy C implementation of Behavior Network has been developed in order to simplify the development of C-code for behavior-based control systems. UML is a popular standard developed by the Object Management Group (OMG) to model software architectures graphically. The C implementation of a Behavior Network is functioning as a decision tree.

Sparse Measurement Systems: Applications, Analysis, Algorithms and Design

ERIC Educational Resources Information Center

Narayanaswamy, Balakrishnan

2011-01-01

This thesis deals with "large-scale" detection problems that arise in many real world applications such as sensor networks, mapping with mobile robots and group testing for biological screening and drug discovery. These are problems where the values of a large number of inputs need to be inferred from noisy observations and where the…

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent.

PubMed

Moioli, Renan C; Vargas, Patricia A; Husbands, Phil

2012-09-01

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain-body- environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

Mobile Robot Navigation and Obstacle Avoidance in Unstructured Outdoor Environments

DTIC Science & Technology

2017-12-01

to pull information from the network, it subscribes to a specific topic and is able to receive the messages that are published to that topic. In order...total artificial potential field is characterized “as the sum of an attractive potential pulling the robot toward the goal…and a repulsive potential...of robot laser_max = 20; % robot laser view horizon goaldist = 0.5; % distance metric for reaching goal goali = 1

Experimental validation of docking and capture using space robotics testbeds

NASA Technical Reports Server (NTRS)

Spofford, John; Schmitz, Eric; Hoff, William

1991-01-01

This presentation describes the application of robotic and computer vision systems to validate docking and capture operations for space cargo transfer vehicles. Three applications are discussed: (1) air bearing systems in two dimensions that yield high quality free-flying, flexible, and contact dynamics; (2) validation of docking mechanisms with misalignment and target dynamics; and (3) computer vision technology for target location and real-time tracking. All the testbeds are supported by a network of engineering workstations for dynamic and controls analyses. Dynamic simulation of multibody rigid and elastic systems are performed with the TREETOPS code. MATRIXx/System-Build and PRO-MATLAB/Simulab are the tools for control design and analysis using classical and modern techniques such as H-infinity and LQG/LTR. SANDY is a general design tool to optimize numerically a multivariable robust compensator with a user-defined structure. Mathematica and Macsyma are used to derive symbolically dynamic and kinematic equations.

MONET: a MOnitoring NEtwork of Telescopes

NASA Astrophysics Data System (ADS)

Hessman, F. V.; Beuermann, K.

2002-01-01

MONET is a planned network of two 1m-class robotic telescopes which will be used for various photometric monitoring projects -- variable stars, planet searches, AGN's, GRB's -- as well as by school children in Germany and over the world. The two host partners, the Univ. of Texas' McDonald Observatory and the South African Astronomical Observatory, will operate the telescopes in exchange for observing time on the network. MONET will be one of the first robotic telescope networks offering 1-m class telescopes, complete coverage of the sky, good longitude coverage for long observing sequences on objects near the celestial equator, and a heavy educational emphasis.

Lander Technologies

NASA Technical Reports Server (NTRS)

Chavers, Greg

2015-01-01

Since 2006 NASA has been formulating robotic missions to the lunar surface through programs and projects like the Robotic Lunar Exploration Program, Lunar Precursor Robotic Program, and International Lunar Network. All of these were led by NASA Marshall Space Flight Center (MSFC). Due to funding shortfalls, the lunar missions associated with these efforts, the designs, were not completed. From 2010 to 2013, the Robotic Lunar Lander Development Activity was funded by the Science Mission Directorate (SMD) to develop technologies that would enable and enhance robotic lunar surface missions at lower costs. In 2013, a requirements-driven, low-cost robotic lunar lander concept was developed for the Resource Prospector Mission. Beginning in 2014, The Advanced Exploration Systems funded the lander team and established the MSFC, Johnson Space Center, Applied Physics Laboratory, and the Jet Propulsion Laboratory team with MSFC leading the project. The lander concept to place a 300-kg rover on the lunar surface has been described in the New Technology Report Case Number MFS-33238-1. A low-cost lander concept for placing a robotic payload on the lunar surface is shown in figures 1 and 2. The NASA lander team has developed several lander concepts using common hardware and software to allow the lander to be configured for a specific mission need. In addition, the team began to transition lander expertise to United States (U.S.) industry to encourage the commercialization of space, specifically the lunar surface. The Lunar Cargo Transportation and Landing by Soft Touchdown (CATALYST) initiative was started and the NASA lander team listed above is partnering with three competitively selected U.S. companies (Astrobotic, Masten Space Systems, and Moon Express) to develop, test, and operate their lunar landers.

An artificial neural network controller based on MPSO-BFGS hybrid optimization for spherical flying robot

NASA Astrophysics Data System (ADS)

Liu, Xiaolin; Li, Lanfei; Sun, Hanxu

2017-12-01

Spherical flying robot can perform various tasks in the complex and varied environment to reduce labor costs. However, it is difficult to guarantee the stability of the spherical flying robot in the case of strong coupling and time-varying disturbance. In this paper, an artificial neural network controller (ANNC) based on MPSO-BFGS hybrid optimization algorithm is proposed. The MPSO algorithm is used to optimize the initial weights of the controller to avoid the local optimal solution. The BFGS algorithm is introduced to improve the convergence ability of the network. We use Lyapunov method to analyze the stability of ANNC. The controller is simulated under the condition of nonlinear coupling disturbance. The experimental results show that the proposed controller can obtain the expected value in shoter time compared with the other considered methods.

A design philosophy for multi-layer neural networks with applications to robot control

NASA Technical Reports Server (NTRS)

Vadiee, Nader; Jamshidi, MO

1989-01-01

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances

PubMed Central

Soltoggio, Andrea; Lemme, Andre; Reinhart, Felix; Steil, Jochen J.

2013-01-01

Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms. PMID:23565092

Deployment and early experience with remote-presence patient care in a community hospital.

PubMed

Petelin, J B; Nelson, M E; Goodman, J

2007-01-01

The introduction of the RP6 (InTouch Health, Santa Barbara, CA, USA) remote-presence "robot" appears to offer a useful telemedicine device. The authors describe the deployment and early experience with the RP6 in a community hospital and provided a live demonstration of the system on April 16, 2005 during the Emerging Technologies Session of the 2005 SAGES Meeting in Fort Lauderdale, Florida. The RP6 is a 5-ft 4-in. tall, 215-pound robot that can be remotely controlled from an appropriately configured computer located anywhere on the Internet (i.e., on this planet). The system is composed of a control station (a computer at the central station), a mechanical robot, a wireless network (at the remote facility: the hospital), and a high-speed Internet connection at both the remote (hospital) and central locations. The robot itself houses a rechargeable power supply. Its hardware and software allows communication over the Internet with the central station, interpretation of commands from the central station, and conversion of the commands into mechanical and nonmechanical actions at the remote location, which are communicated back to the central station over the Internet. The RP6 system allows the central party (e.g., physician) to control the movements of the robot itself, see and hear at the remote location (hospital), and be seen and heard at the remote location (hospital) while not physically there. Deployment of the RP6 system at the hospital was accomplished in less than a day. The wireless network at the institution was already in place. The control station setup time ranged from 1 to 4 h and was dependent primarily on the quality of the Internet connection (bandwidth) at the remote locations. Patients who visited with the RP6 on their discharge day could be discharged more than 4 h earlier than with conventional visits, thereby freeing up hospital beds on a busy med-surg floor. Patient visits during "off hours" (nights and weekends) were three times more efficient than conventional visits during these times (20 min per visit vs 40-min round trip travel + 20-min visit). Patients and nursing personnel both expressed tremendous satisfaction with the remote-presence interaction. The authors' early experience suggests a significant benefit to patients, hospitals, and physicians with the use of RP6. The implications for future development are enormous.

The BOOTES-5 telescope at San Pedro Martir National Astronomical Observatory, Mexico

NASA Astrophysics Data System (ADS)

Hiriart, D.; Valdez, J.; Martínez, B.; García, B.; Cordova, A.; Colorado, E.; Guisa, G.; Ochoa, J. L.; Nuñez, J. M.; Ceseña, U.; Cunniffe, R.; Murphy, D.; Lee, W.; Park, Il H.; Castro-Tirado, A. J.

2016-12-01

BOOTES-5 is the fifth robotic observatory of the international network of robotic telescopes BOOTES (Burst Observer and Optical Transient Exploring Optical System). It is located at the National Astronomical Observatory at Sierra San Pedro Martir, Baja California, Mexico. It was dedicated on November 26, 2015 and it is in the process of testing. Its main scientific objective is the observation and monitoring of the optic counterparts of gamma-ray bursts as quickly as possible once they have been detected from space or other ground-based observatories. BOOTES-5 fue nombrado Telescopio Javier Gorosabel en memoria del astrónomo español Javier Gorosabel Urkia.

RTML: remote telescope markup language and you

NASA Astrophysics Data System (ADS)

Hessman, F. V.

2001-12-01

In order to coordinate the use of robotic and remotely operated telescopes in networks -- like Göttingen's MOnitoring NEtwork of Telescopes (MONET) -- a standard format for the exchange of observing requests and reports is needed. I describe the benefits of Remote Telescope Markup Language (RTML), an XML-based protocol originally developed by the Hands-On Universe Project, which is being used and further developed by several robotic telescope projects and firms.

Teleoperation in surgical robotics--network latency effects on surgical performance.

PubMed

Lum, Mitchell J H; Rosen, Jacob; King, Hawkeye; Friedman, Diana C W; Lendvay, Thomas S; Wright, Andrew S; Sinanan, Mika N; Hannaford, Blake

2009-01-01

A teleoperated surgical robotic system allows surgical procedures to be conducted across long distances while utilizing wired and wireless communication with a wide spectrum of performance that may affect the outcome. An open architecture portable surgical robotic system (Raven) was developed for both open and minimally invasive surgery. The system has been the subject of an intensive telesurgical experimental protocol aimed at exploring the boundaries of the system and surgeon performance during a series of field experiments in extreme environments (desert and underwater) teleportation between US, Europe, and Japan as well as lab experiments under synthetic fixed time delay. One standard task (block transfer emulating tissue manipulation) of the Fundamentals of Laparoscopic Surgery (FLS) training kit was used for the experimental protocol. Network characterization indicated a typical time delay in the range of 16-172 ms in field experiments. The results of the lab experiments showed that the completion time of the task as well as the length of the tool tip trajectory significantly increased (alpha< 0.02) as time delay increased in the range of 0-0.5 sec increased. For teleoperation with a time delay of 0.25s and 0.5s the task completion time was lengthened by a factor of 1.45 and 2.04 with respect to no time delay, whereas the length of the tools' trajectory was increased by a factor of 1.28 and 1.53 with respect to no time delay. There were no statistical differences between experienced surgeons and non-surgeons in the number of errors (block drooping) as well as the completion time and the tool tip path length at different time delays.

Volunteers Oriented Interface Design for the Remote Navigation of Rescue Robots at Large-Scale Disaster Sites

NASA Astrophysics Data System (ADS)

Yang, Zhixiao; Ito, Kazuyuki; Saijo, Kazuhiko; Hirotsune, Kazuyuki; Gofuku, Akio; Matsuno, Fumitoshi

This paper aims at constructing an efficient interface being similar to those widely used in human daily life, to fulfill the need of many volunteer rescuers operating rescue robots at large-scale disaster sites. The developed system includes a force feedback steering wheel interface and an artificial neural network (ANN) based mouse-screen interface. The former consists of a force feedback steering control and a six monitors’ wall. It provides a manual operation like driving cars to navigate a rescue robot. The latter consists of a mouse and a camera’s view displayed in a monitor. It provides a semi-autonomous operation by mouse clicking to navigate a rescue robot. Results of experiments show that a novice volunteer can skillfully navigate a tank rescue robot through both interfaces after 20 to 30 minutes of learning their operation respectively. The steering wheel interface has high navigating speed in open areas, without restriction of terrains and surface conditions of a disaster site. The mouse-screen interface is good at exact navigation in complex structures, while bringing little tension to operators. The two interfaces are designed to switch into each other at any time to provide a combined efficient navigation method.

Electro-actuated hydrogel walkers with dual responsive legs.

PubMed

Morales, Daniel; Palleau, Etienne; Dickey, Michael D; Velev, Orlin D

2014-03-07

Stimuli responsive polyelectrolyte hydrogels may be useful for soft robotics because of their ability to transform chemical energy into mechanical motion without the use of external mechanical input. Composed of soft and biocompatible materials, gel robots can easily bend and fold, interface and manipulate biological components and transport cargo in aqueous solutions. Electrical fields in aqueous solutions offer repeatable and controllable stimuli, which induce actuation by the re-distribution of ions in the system. Electrical fields applied to polyelectrolyte-doped gels submerged in ionic solution distribute the mobile ions asymmetrically to create osmotic pressure differences that swell and deform the gels. The sign of the fixed charges on the polyelectrolyte network determines the direction of bending, which we harness to control the motion of the gel legs in opposing directions as a response to electrical fields. We present and analyze a walking gel actuator comprised of cationic and anionic gel legs made of copolymer networks of acrylamide (AAm)/sodium acrylate (NaAc) and acrylamide/quaternized dimethylaminoethyl methacrylate (DMAEMA Q), respectively. The anionic and cationic legs were attached by electric field-promoted polyion complexation. We characterize the electro-actuated response of the sodium acrylate hydrogel as a function of charge density and external salt concentration. We demonstrate that "osmotically passive" fixed charges play an important role in controlling the bending magnitude of the gel networks. The gel walkers achieve unidirectional motion on flat elastomer substrates and exemplify a simple way to move and manipulate soft matter devices and robots in aqueous solutions.

Technology developments integrating a space network communications testbed

NASA Technical Reports Server (NTRS)

Kwong, Winston; Jennings, Esther; Clare, Loren; Leang, Dee

2006-01-01

As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enables its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions.

R and T report: Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Soffen, Gerald A. (Editor)

1993-01-01

The 1993 Research and Technology Report for Goddard Space Flight Center is presented. Research covered areas such as (1) flight projects; (2) space sciences including cosmology, high energy, stars and galaxies, and the solar system; (3) earth sciences including process modeling, hydrology/cryology, atmospheres, biosphere, and solid earth; (4) networks, planning, and information systems including support for mission operations, data distribution, advanced software and systems engineering, and planning/scheduling; and (5) engineering and materials including spacecraft systems, material and testing, optics and photonics and robotics.

Approaching mathematical model of the immune network based DNA Strand Displacement system.

PubMed

Mardian, Rizki; Sekiyama, Kosuke; Fukuda, Toshio

2013-12-01

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Dynamics and control of robot for capturing objects in space

NASA Astrophysics Data System (ADS)

Huang, Panfeng

Space robots are expected to perform intricate tasks in future space services, such as satellite maintenance, refueling, and replacing the orbital replacement unit (ORU). To realize these missions, the capturing operation may not be avoided. Such operations will encounter some challenges because space robots have some unique characteristics unfound on ground-based robots, such as, dynamic singularities, dynamic coupling between manipulator and space base, limited energy supply and working without a fixed base, and so on. In addition, since contacts and impacts may not be avoided during capturing operation. Therefore, dynamics and control problems of space robot for capturing objects are significant research topics if the robots are to be deployed for the space services. A typical servicing operation mainly includes three phases: capturing the object, berthing and docking the object, then repairing the target. Therefore, this thesis will focus on resolving some challenging problems during capturing the object, berthing and docking, and so on. In this thesis, I study and analyze the dynamics and control problems of space robot for capturing objects. This work has potential impact in space robotic applications. I first study the contact and impact dynamics of space robot and objects. I specifically focus on analyzing the impact dynamics and mapping the relationship of influence and speed. Then, I develop the fundamental theory for planning the minimum-collision based trajectory of space robot and designing the configuration of space robot at the moment of capture. To compensate for the attitude of the space base during the capturing approach operation, a new balance control concept which can effectively balance the attitude of the space base using the dynamic couplings is developed. The developed balance control concept helps to understand of the nature of space dynamic coupling, and can be readily applied to compensate or minimize the disturbance to the space base. After capturing the object, the space robot must complete the following two tasks: one is to berth the object, and the other is to re-orientate the attitude of the whole robot system for communication and power supply. Therefore, I propose a method to accomplish these two tasks simultaneously using manipulator motion only. The ultimate goal of space services is to realize the capture and manipulation autonomously. Therefore, I propose an affective approach based on learning human skill to track and capture the objects automatically in space. With human-teaching demonstration, the space robot is able to learn and abstract human tracking and capturing skill using an efficient neural-network learning architecture that combines flexible Cascade Neural Networks with Node Decoupled Extended Kalman Filtering (CNN-NDEKF). The simulation results attest that this approach is useful and feasible in tracking trajectory planning and capturing of space robot. Finally I propose a novel approach based on Genetic Algorithms (GAs) to optimize the approach trajectory of space robots in order to realize effective and stable operations. I complete the minimum-torque path planning in order to save the limited energy in space, and design the minimum jerk trajectory for the stabilization of the space manipulator and its space base. These optimal algorithms are very important and useful for the application of space robot.

Adaptive neural network motion control of manipulators with experimental evaluations.

PubMed

Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

PubMed Central

Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

Tutorial: Neural networks and their potential application in nuclear power plants

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

Uhrig, R.E.

A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanjimore » characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise'' signals (e.g. electroencephalograms), modeling complex systems that cannot be modelled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants.« less

Bio-inspired group modeling and analysis for intruder detection in mobile sensor/robotic networks.

PubMed

Fu, Bo; Xiao, Yang; Liang, Xiannuan; Philip Chen, C L

2015-01-01

Although previous bio-inspired models have concentrated on invertebrates (such as ants), mammals such as primates with higher cognitive function are valuable for modeling the increasingly complex problems in engineering. Understanding primates' social and communication systems, and applying what is learned from them to engineering domains is likely to inspire solutions to a number of problems. This paper presents a novel bio-inspired approach to determine group size by researching and simulating primate society. Group size does matter for both primate society and digital entities. It is difficult to determine how to group mobile sensors/robots that patrol in a large area when many factors are considered such as patrol efficiency, wireless interference, coverage, inter/intragroup communications, etc. This paper presents a simulation-based theoretical study on patrolling strategies for robot groups with the comparison of large and small groups through simulations and theoretical results.

Symbolic dynamic filtering and language measure for behavior identification of mobile robots.

PubMed

Mallapragada, Goutham; Ray, Asok; Jin, Xin

2012-06-01

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.

Computer-assisted hip and knee arthroplasty. Navigation and active robotic systems: an evidence-based analysis.

PubMed

2004-01-01

The Medical Advisory Secretariat undertook a review of the evidence on the effectiveness and cost-effectiveness of computer assisted hip and knee arthroplasty. The two computer assisted arthroplasty systems that are the topics of this review are (1) navigation and (2) robotic-assisted hip and knee arthroplasty. Computer-assisted arthroplasty consists of navigation and robotic systems. Surgical navigation is a visualization system that provides positional information about surgical tools or implants relative to a target bone on a computer display. Most of the navigation-assisted arthroplasty devices that are the subject of this review are licensed by Health Canada. Robotic systems are active robots that mill bone according to information from a computer-assisted navigation system. The robotic-assisted arthroplasty devices that are the subject of this review are not currently licensed by Health Canada. The Cochrane and International Network of Agencies for Health Technology Assessment databases did not identify any health technology assessments on navigation or robotic-assisted hip or knee arthroplasty. The MEDLINE and EMBASE databases were searched for articles published between January 1, 1996 and November 30, 2003. This search produced 367 studies, of which 9 met the inclusion criteria. NAVIGATION-ASSISTED ARTHROPLASTY: Five studies were identified that examined navigation-assisted arthroplasty.A Level 1 evidence study from Germany found a statistically significant difference in alignment and angular deviation between navigation-assisted and free-hand total knee arthroplasty in favour of navigation-assisted surgery. However, the endpoints in this study were short-term. To date, the long-term effects (need for revision, implant longevity, pain, functional performance) are unknown.(1)A Level 2 evidence short-term study found that navigation-assisted total knee arthroplasty was significantly better than a non-navigated procedure for one of five postoperative measured angles.(2)A Level 2 evidence short-term study found no statistically significant difference in the variation of the abduction angle between navigation-assisted and conventional total hip arthroplasty.(3)Level 3 evidence observational studies of navigation-assisted total knee arthroplasty and unicompartmental knee arthroplasty have been conducted. Two studies reported that "the follow-up of the navigated prostheses is currently too short to know if clinical outcome or survival rates are improved. Longer follow-up is required to determine the respective advantages and disadvantages of both techniques."(4;5) ROBOTIC-ASSISTED ARTHROPLASTY: Four studies were identified that examined robotic-assisted arthroplasty.A Level 1 evidence study revealed that there was no statistically significant difference between functional hip scores at 24 months post implantation between patients who underwent robotic-assisted primary hip arthroplasty and those that were treated with manual implantation.(6)Robotic-assisted arthroplasty had advantages in terms of preoperative planning and the accuracy of the intraoperative procedure.(6)Patients who underwent robotic-assisted hip arthroplasty had a higher dislocation rate and more revisions.(6)Robotic-assisted arthroplasty may prove effective with certain prostheses (e.g., anatomic) because their use may result in less muscle detachment.(6)An observational study (Level 3 evidence) found that the incidence of severe embolic events during hip relocation was lower with robotic arthroplasty than with manual surgery.(7)An observational study (Level 3 evidence) found that there was no significant difference in gait analyses of patients who underwent robotic-assisted total hip arthroplasty using robotic surgery compared to patients who were treated with conventional cementless total hip arthroplasty.(8)An observational study (Level 3 evidence) compared outcomes of total knee arthroplasty between patients undergoing robotic surgery and patients who were historical controls. Brief, qualitative results suggested that there was much broader variation of angles after manual total knee arthroplasty compared to the robotic technique and that there was no difference in knee functional scores or implant position at the 3 and 6 month follow-up.(9).

Computer-Assisted Hip and Knee Arthroplasty. Navigation and Active Robotic Systems

PubMed Central

2004-01-01

Executive Summary Objective The Medical Advisory Secretariat undertook a review of the evidence on the effectiveness and cost-effectiveness of computer assisted hip and knee arthroplasty. The two computer assisted arthroplasty systems that are the topics of this review are (1) navigation and (2) robotic-assisted hip and knee arthroplasty. The Technology Computer-assisted arthroplasty consists of navigation and robotic systems. Surgical navigation is a visualization system that provides positional information about surgical tools or implants relative to a target bone on a computer display. Most of the navigation-assisted arthroplasty devices that are the subject of this review are licensed by Health Canada. Robotic systems are active robots that mill bone according to information from a computer-assisted navigation system. The robotic-assisted arthroplasty devices that are the subject of this review are not currently licensed by Health Canada. Review Strategy The Cochrane and International Network of Agencies for Health Technology Assessment databases did not identify any health technology assessments on navigation or robotic-assisted hip or knee arthroplasty. The MEDLINE and EMBASE databases were searched for articles published between January 1, 1996 and November 30, 2003. This search produced 367 studies, of which 9 met the inclusion criteria. Summary of Findings Navigation-Assisted Arthroplasty Five studies were identified that examined navigation-assisted arthroplasty. A Level 1 evidence study from Germany found a statistically significant difference in alignment and angular deviation between navigation-assisted and free-hand total knee arthroplasty in favour of navigation-assisted surgery. However, the endpoints in this study were short-term. To date, the long-term effects (need for revision, implant longevity, pain, functional performance) are unknown.(1) A Level 2 evidence short-term study found that navigation-assisted total knee arthroplasty was significantly better than a non-navigated procedure for one of five postoperative measured angles.(2) A Level 2 evidence short-term study found no statistically significant difference in the variation of the abduction angle between navigation-assisted and conventional total hip arthroplasty.(3) Level 3 evidence observational studies of navigation-assisted total knee arthroplasty and unicompartmental knee arthroplasty have been conducted. Two studies reported that “the follow-up of the navigated prostheses is currently too short to know if clinical outcome or survival rates are improved. Longer follow-up is required to determine the respective advantages and disadvantages of both techniques.”(4;5) Robotic-Assisted Arthroplasty Four studies were identified that examined robotic-assisted arthroplasty. A Level 1 evidence study revealed that there was no statistically significant difference between functional hip scores at 24 months post implantation between patients who underwent robotic-assisted primary hip arthroplasty and those that were treated with manual implantation.(6) Robotic-assisted arthroplasty had advantages in terms of preoperative planning and the accuracy of the intraoperative procedure.(6) Patients who underwent robotic-assisted hip arthroplasty had a higher dislocation rate and more revisions.(6) Robotic-assisted arthroplasty may prove effective with certain prostheses (e.g., anatomic) because their use may result in less muscle detachment.(6) An observational study (Level 3 evidence) found that the incidence of severe embolic events during hip relocation was lower with robotic arthroplasty than with manual surgery.(7) An observational study (Level 3 evidence) found that there was no significant difference in gait analyses of patients who underwent robotic-assisted total hip arthroplasty using robotic surgery compared to patients who were treated with conventional cementless total hip arthroplasty.(8) An observational study (Level 3 evidence) compared outcomes of total knee arthroplasty between patients undergoing robotic surgery and patients who were historical controls. Brief, qualitative results suggested that there was much broader variation of angles after manual total knee arthroplasty compared to the robotic technique and that there was no difference in knee functional scores or implant position at the 3 and 6 month follow-up.(9) PMID:23074452

A Mars/phobos Transportation System

NASA Technical Reports Server (NTRS)

1989-01-01

A transportation system will be necessary to support construction and operation of bases on Phobos and Mars beginning in the year 2020 or later. An approach to defining a network of vehicles and the types of vehicles which may be used in the system are presented. The network will provide a convenient, integrated means for transporting robotically constructed bases to Phobos and Mars. All the technology needed for the current plan is expected to be available for use at the projected date of cargo departure from the Earth system. The modular design of the transportation system provides easily implemented contingency plans, so that difficulties with any one vehicle will have a minimal effect on the progress of the total mission. The transportation network proposed consists of orbital vehicles and atmospheric entry vehicles. Initially, only orbital vehicles will participate in the robotic construction phase of the Phobos base. The Interplanetary Transfer Vehicle (ITV) will carry the base and construction equipment to Phobos where the Orbital Maneuvering Vehicles (OMV's) will participate in the initial construction of the base. When the Mars base is ready to be sent, one or more ITV's will be used to transport the atmospheric entry vehicles from Earth. These atmospheric vehicles are the One Way Landers (OWL's) and the Ascent/Descent Vehicles (ADV's). They will be used to carry the base components and/or construction equipment. The OMV's and the Orbital Transfer Vehicles (OTV's) will assist in carrying the atmospheric entry vehicles to low Martian orbit where the OWL's or ADV's will descent to the planet surface. The ADV's were proposed to accommodate expansion of the system. Additionally, a smaller version of the ADV class is capable of transporting personnel between Mars and Phobos.

Optimal sensor fusion for land vehicle navigation

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

Morrow, J.D.

1990-10-01

Position location is a fundamental requirement in autonomous mobile robots which record and subsequently follow x,y paths. The Dept. of Energy, Office of Safeguards and Security, Robotic Security Vehicle (RSV) program involves the development of an autonomous mobile robot for patrolling a structured exterior environment. A straight-forward method for autonomous path-following has been adopted and requires digitizing'' the desired road network by storing x,y coordinates every 2m along the roads. The position location system used to define the locations consists of a radio beacon system which triangulates position off two known transponders, and dead reckoning with compass and odometer. Thismore » paper addresses the problem of combining these two measurements to arrive at a best estimate of position. Two algorithms are proposed: the optimal'' algorithm treats the measurements as random variables and minimizes the estimate variance, while the average error'' algorithm considers the bias in dead reckoning and attempts to guarantee an average error. Data collected on the algorithms indicate that both work well in practice. 2 refs., 7 figs.« less

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

PubMed Central

Arena, Eleonora; Arena, Paolo; Strauss, Roland; Patané, Luca

2017-01-01

In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster. PMID:28337138

Autonomous Robotic Inspection in Tunnels

NASA Astrophysics Data System (ADS)

Protopapadakis, E.; Stentoumis, C.; Doulamis, N.; Doulamis, A.; Loupos, K.; Makantasis, K.; Kopsiaftis, G.; Amditis, A.

2016-06-01

In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel's lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure.

Event-Based Sensing and Control for Remote Robot Guidance: An Experimental Case

PubMed Central

Santos, Carlos; Martínez-Rey, Miguel; Santiso, Enrique

2017-01-01

This paper describes the theoretical and practical foundations for remote control of a mobile robot for nonlinear trajectory tracking using an external localisation sensor. It constitutes a classical networked control system, whereby event-based techniques for both control and state estimation contribute to efficient use of communications and reduce sensor activity. Measurement requests are dictated by an event-based state estimator by setting an upper bound to the estimation error covariance matrix. The rest of the time, state prediction is carried out with the Unscented transformation. This prediction method makes it possible to select the appropriate instants at which to perform actuations on the robot so that guidance performance does not degrade below a certain threshold. Ultimately, we obtained a combined event-based control and estimation solution that drastically reduces communication accesses. The magnitude of this reduction is set according to the tracking error margin of a P3-DX robot following a nonlinear trajectory, remotely controlled with a mini PC and whose pose is detected by a camera sensor. PMID:28878144

Cooperative Robot Localization Using Event-Triggered Estimation

NASA Astrophysics Data System (ADS)

Iglesias Echevarria, David I.

It is known that multiple robot systems that need to cooperate to perform certain activities or tasks incur in high energy costs that hinder their autonomous functioning and limit the benefits provided to humans by these kinds of platforms. This work presents a communications-based method for cooperative robot localization. Implementing concepts from event-triggered estimation, used with success in the field of wireless sensor networks but rarely to do robot localization, agents are able to only send measurements to their neighbors when the expected novelty in this information is high. Since all agents know the condition that triggers a measurement to be sent or not, the lack of a measurement is therefore informative and fused into state estimates. In the case agents do not receive either direct nor indirect measurements of all others, the agents employ a covariance intersection fusion rule in order to keep the local covariance error metric bounded. A comprehensive analysis of the proposed algorithm and its estimation performance in a variety of scenarios is performed, and the algorithm is compared to similar cooperative localization approaches. Extensive simulations are performed that illustrate the effectiveness of this method.

Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation

PubMed Central

Torres-González, Arturo; Martínez-de Dios, Jose Ramiro; Ollero, Anibal

2017-01-01

This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the technological constraints of the robot and beacons, many applications impose further resource consumption limitations. This paper presents a scalable distributed RO-SLAM scheme for resource-constrained operation. It is capable of exploiting robot-beacon cooperation in order to improve SLAM accuracy while meeting a given resource consumption bound expressed as the maximum number of measurements that are integrated in SLAM per iteration. The proposed scheme combines a Sparse Extended Information Filter (SEIF) SLAM method, in which each beacon gathers and integrates robot-beacon and inter-beacon measurements, and a distributed information-driven measurement allocation tool that dynamically selects the measurements that are integrated in SLAM, balancing uncertainty improvement and resource consumption. The scheme adopts a robot-beacon distributed approach in which each beacon participates in the selection, gathering and integration in SLAM of robot-beacon and inter-beacon measurements, resulting in significant estimation accuracies, resource-consumption efficiency and scalability. It has been integrated in an octorotor Unmanned Aerial System (UAS) and evaluated in 3D SLAM outdoor experiments. The experimental results obtained show its performance and robustness and evidence its advantages over existing methods. PMID:28425946

Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation.

PubMed

Torres-González, Arturo; Martínez-de Dios, Jose Ramiro; Ollero, Anibal

2017-04-20

This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the technological constraints of the robot and beacons, many applications impose further resource consumption limitations. This paper presents a scalable distributed RO-SLAM scheme for resource-constrained operation. It is capable of exploiting robot-beacon cooperation in order to improve SLAM accuracy while meeting a given resource consumption bound expressed as the maximum number of measurements that are integrated in SLAM per iteration. The proposed scheme combines a Sparse Extended Information Filter (SEIF) SLAM method, in which each beacon gathers and integrates robot-beacon and inter-beacon measurements, and a distributed information-driven measurement allocation tool that dynamically selects the measurements that are integrated in SLAM, balancing uncertainty improvement and resource consumption. The scheme adopts a robot-beacon distributed approach in which each beacon participates in the selection, gathering and integration in SLAM of robot-beacon and inter-beacon measurements, resulting in significant estimation accuracies, resource-consumption efficiency and scalability. It has been integrated in an octorotor Unmanned Aerial System (UAS) and evaluated in 3D SLAM outdoor experiments. The experimental results obtained show its performance and robustness and evidence its advantages over existing methods.

The 1991 research and technology report, Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Soffen, Gerald (Editor); Ottenstein, Howard (Editor); Montgomery, Harry (Editor); Truszkowski, Walter (Editor); Frost, Kenneth (Editor); Sullivan, Walter (Editor); Boyle, Charles (Editor)

1991-01-01

The 1991 Research and Technology Report for Goddard Space Flight Center is presented. Research covered areas such as (1) earth sciences including upper atmosphere, lower atmosphere, oceans, hydrology, and global studies; (2) space sciences including solar studies, planetary studies, Astro-1, gamma ray investigations, and astrophysics; (3) flight projects; (4) engineering including robotics, mechanical engineering, electronics, imaging and optics, thermal and cryogenic studies, and balloons; and (5) ground systems, networks, and communications including data and networks, TDRSS, mission planning and scheduling, and software development and test.

Multi-layer neural networks for robot control

NASA Technical Reports Server (NTRS)

Pourboghrat, Farzad

1989-01-01

Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

I(CES)-cubes: a modular self-reconfigurable bipartite robotic system

NASA Astrophysics Data System (ADS)

Unsal, Cem; Kiliccote, Han; Khosla, Pradeep K.

1999-08-01

In this manuscript, we introduce I(CES)-Cubes, a class of 3D modular robotic system that is capable of reconfiguring itself in order to adapt to its environment. This is a bipartite system, i.e. a collection of (i) active elements capable of actuation, and (ii) passive elements acting as connectors between actuated elements. Active elements, called links, are 3-DOF manipulators that are capable of attaching/detaching themselves to/from the passive elements. The cubes can then be positioned and oriented using links, which are independent mechatronic elements. Self- reconfiguration property enables the system to performed locomotion tasks over difficult terrain. For example, the system would be capable of moving over obstacles and climbing stairs. These task are performed by positing and orienting cubes and links to form a 3D network with required shape and position. This paper describes the design of the passive and active elements, the attachment mechanics, and several reconfiguration scenarios. Specifics of the hardware implementation and result of experiments with current prototypes are also given.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

PubMed

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

PubMed Central

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world. PMID:26528176

Development of a sonar-based object recognition system

NASA Astrophysics Data System (ADS)

Ecemis, Mustafa Ihsan

2001-02-01

Sonars are used extensively in mobile robotics for obstacle detection, ranging and avoidance. However, these range-finding applications do not exploit the full range of information carried in sonar echoes. In addition, mobile robots need robust object recognition systems. Therefore, a simple and robust object recognition system using ultrasonic sensors may have a wide range of applications in robotics. This dissertation develops and analyzes an object recognition system that uses ultrasonic sensors of the type commonly found on mobile robots. Three principal experiments are used to test the sonar recognition system: object recognition at various distances, object recognition during unconstrained motion, and softness discrimination. The hardware setup, consisting of an inexpensive Polaroid sonar and a data acquisition board, is described first. The software for ultrasound signal generation, echo detection, data collection, and data processing is then presented. Next, the dissertation describes two methods to extract information from the echoes, one in the frequency domain and the other in the time domain. The system uses the fuzzy ARTMAP neural network to recognize objects on the basis of the information content of their echoes. In order to demonstrate that the performance of the system does not depend on the specific classification method being used, the K- Nearest Neighbors (KNN) Algorithm is also implemented. KNN yields a test accuracy similar to fuzzy ARTMAP in all experiments. Finally, the dissertation describes a method for extracting features from the envelope function in order to reduce the dimension of the input vector used by the classifiers. Decreasing the size of the input vectors reduces the memory requirements of the system and makes it run faster. It is shown that this method does not affect the performance of the system dramatically and is more appropriate for some tasks. The results of these experiments demonstrate that sonar can be used to develop a low-cost, low-computation system for real-time object recognition tasks on mobile robots. This system differs from all previous approaches in that it is relatively simple, robust, fast, and inexpensive.

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Global Astrophysical Telescope System - GATS

NASA Astrophysics Data System (ADS)

Polińska, M.; Kamiński, K.; Dimitrov, W.; Fagas, M.; Borczyk, W.; Kwiatkowski, T.; Baranowski, R.; Bartczak, P.; Schwarzenberg-Czerny, A.

2014-02-01

The Global Astronomical Telescope System is a project managed by the Astronomical Observatory Institute of Adam Mickiewicz University in Poznań (Poland) and it is primarily intended for stellar medium/high resolution spectroscopy. The system will be operating as a global network of robotic telescopes. The GATS consists of two telescopes: PST 1 in Poland (near Poznań) and PST 2 in the USA (Arizona). The GATS project is also intended to cooperate with the BRITE satellites and supplement their photometry with spectroscopic observations.

Framework and Implications of Virtual Neurorobotics

PubMed Central

Goodman, Philip H.; Zou, Quan; Dascalu, Sergiu-Mihai

2008-01-01

Despite decades of societal investment in artificial learning systems, truly “intelligent” systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain “algorithm” itself—trying to replicate uniquely “neuromorphic” dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or “avatars”, to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications. PMID:18982115

A fluid-filled soft robot that exhibits spontaneous switching among versatile spatiotemporal oscillatory patterns inspired by the true slime mold.

PubMed

Umedachi, Takuya; Idei, Ryo; Ito, Kentaro; Ishiguro, Akio

2013-01-01

Behavioral diversity is an essential feature of living systems, enabling them to exhibit adaptive behavior in hostile and dynamically changing environments. However, traditional engineering approaches strive to avoid, or suppress, the behavioral diversity in artificial systems to achieve high performance in specific environments for given tasks. The goals of this research include understanding how living systems exhibit behavioral diversity and using these findings to build lifelike robots that exhibit truly adaptive behaviors. To this end, we have focused on one of the most primitive forms of intelligence concerning behavioral diversity, namely, a plasmodium of true slime mold. The plasmodium is a large amoeba-like unicellular organism that does not possess any nervous system or specialized organs. However, it exhibits versatile spatiotemporal oscillatory patterns and switches spontaneously between these. Inspired by the plasmodium, we built a mathematical model that exhibits versatile oscillatory patterns and spontaneously transitions between these patterns. This model demonstrates that, in contrast to coupled nonlinear oscillators with a well-designed complex diffusion network, physically interacting mechanosensory oscillators are capable of generating versatile oscillatory patterns without changing any parameters. Thus, the results are expected to shed new light on the design scheme for lifelike robots that exhibit amazingly versatile and adaptive behaviors.

A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

NASA Astrophysics Data System (ADS)

Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

2016-09-01

Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin

PubMed Central

Taube Navaraj, William; García Núñez, Carlos; Shakthivel, Dhayalan; Vinciguerra, Vincenzo; Labeau, Fabrice; Gregory, Duncan H.; Dahiya, Ravinder

2017-01-01

This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs) based hardware-implementable neural network (HNN) approach for tactile data processing in electronic skin (e-skin). The viability of Si nanowires (NWs) as the active material for υ-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using υ-NWFETs to realize HNNs is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a bendable tactile skin with distributed neural elements (for local data processing, as in biological skin) in the backplane. The modeling and simulation of υ-NWFET based devices show that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network - thus validating the working of υ-NWFETs as the building block for HNN. The simulation has been further extended to υ-NWFET based circuits and neuronal computation system and this has been validated by interfacing it with a transparent tactile skin prototype (comprising of 6 × 6 ITO based capacitive tactile sensors array) integrated on the palm of a 3D printed robotic hand. In this regard, a tactile data coding system is presented to detect touch gesture and the direction of touch. Following these simulation studies, a four-gated υ-NWFET is fabricated with Pt/Ti metal stack for gates, source and drain, Ni floating gate, and Al2O3 high-k dielectric layer. The current-voltage characteristics of fabricated υ-NWFET devices confirm the dependence of turn-off voltages on the (synaptic) weight of each gate. The presented υ-NWFET approach is promising for a neuro-robotic tactile sensory system with distributed computing as well as numerous futuristic applications such as prosthetics, and electroceuticals. PMID:28979183

A Multidimensional Analysis of Prostate Surgery Costs in the United States: Robotic-Assisted versus Retropubic Radical Prostatectomy.

PubMed

Bijlani, Akash; Hebert, April E; Davitian, Mike; May, Holly; Speers, Mark; Leung, Robert; Mohamed, Nihal E; Sacks, Henry S; Tewari, Ashutosh

2016-06-01

The economic value of robotic-assisted laparoscopic prostatectomy (RALP) in the United States is still not well understood because of limited view analyses. The objective of this study was to examine the costs and benefits of RALP versus retropubic radical prostatectomy from an expanded view, including hospital, payer, and societal perspectives. We performed a model-based cost comparison using clinical outcomes obtained from a systematic review of the published literature. Equipment costs were obtained from the manufacturer of the robotic system; other economic model parameters were obtained from government agencies, online resources, commercially available databases, an advisory expert panel, and the literature. Clinical point estimates and care pathways based on National Comprehensive Cancer Network guidelines were used to model costs out to 3 years. Hospital costs and costs incurred for the patients' postdischarge complications, adjuvant and salvage radiation treatment, incontinence and potency treatment, and lost wages during recovery were considered. Robotic system costs were modeled in two ways: as hospital overhead (hospital overhead calculation: RALP-H) and as a function of robotic case volume (robotic amortization calculation: RALP-R). All costs were adjusted to year 2014 US dollars. Because of more favorable clinical outcomes over 3 years, RALP provided hospital ($1094 savings with RALP-H, $341 deficit with RALP-R), payer ($1451), and societal ($1202) economic benefits relative to retropubic radical prostatectomy. Monte-Carlo probabilistic sensitivity analysis demonstrated a 38% to 99% probability that RALP provides cost savings (depending on the perspective). Higher surgical consumable costs are offset by a decreased hospital stay, lower complication rate, and faster return to work. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Lunar rovers and local positioning system

NASA Astrophysics Data System (ADS)

Avery, James; Su, Renjeng

1991-11-01

Telerobotic rovers equipped with adequate actuators and sensors are clearly necessary for extraterrestrial construction. They will be employed as substitutes for humans, to perform jobs like surveying, sensing, signaling, manipulating, and the handling of small materials. Important design criteria for these rovers include versatility and robustness. They must be easily programmed and reprogrammed to perform a wide variety of different functions, and they must be robust so that construction work will not be jeopardized by parts failures. The key qualities and functions necessary for these rovers to achieve the required versatility and robustness are modularity, redundancy, and coordination. Three robotic rovers are being built by CSC as a test bed to implement the concepts of modularity and coordination. The specific goal of the design and construction of these robots is to demonstrate the software modularity and multirobot control algorithms required for the physical manipulation of constructible elements. Each rover consists of a transporter platform, bus manager, simple manipulator, and positioning receivers. These robots will be controlled from a central control console via a radio-frequency local area network (LAN). To date, one prototype transporter platform frame was built with batteries, motors, a prototype single-motor controller, and two prototype internal LAN boards. Software modules were developed in C language for monitor functions, i/o, and parallel port usage in each computer board. Also completed are the fabrication of half of the required number of computer boards, the procurement of 19.2 Kbaud RF modems for inter-robot communications, and the simulation of processing requirements for positioning receivers. In addition to the robotic platform, the fabrication of a local positioning system based on infrared signals is nearly completed. This positioning system will make the rovers into a moving reference system capable of performing site surveys. In addition, a four degree mechanical manipulator especially suited for coordinated teleoperation was conceptually designed and is currently being analyzed. This manipulator will be integrated into the rovers as their end effector. Twenty internal LAN cards fabricated by a commercial firm are being used, a prototype manipulator and a range finder for a positioning system were built, a prototype two-motor controller was designed, and one of the robots is performing its first telerobotic motion. In addition, the robots' internal LAN's were coordinated and tested, hardware design upgrades based on fabrication and fit experience were completed, and the positioning system is running.

Lunar rovers and local positioning system

NASA Technical Reports Server (NTRS)

Avery, James; Su, Renjeng

1991-01-01

Telerobotic rovers equipped with adequate actuators and sensors are clearly necessary for extraterrestrial construction. They will be employed as substitutes for humans, to perform jobs like surveying, sensing, signaling, manipulating, and the handling of small materials. Important design criteria for these rovers include versatility and robustness. They must be easily programmed and reprogrammed to perform a wide variety of different functions, and they must be robust so that construction work will not be jeopardized by parts failures. The key qualities and functions necessary for these rovers to achieve the required versatility and robustness are modularity, redundancy, and coordination. Three robotic rovers are being built by CSC as a test bed to implement the concepts of modularity and coordination. The specific goal of the design and construction of these robots is to demonstrate the software modularity and multirobot control algorithms required for the physical manipulation of constructible elements. Each rover consists of a transporter platform, bus manager, simple manipulator, and positioning receivers. These robots will be controlled from a central control console via a radio-frequency local area network (LAN). To date, one prototype transporter platform frame was built with batteries, motors, a prototype single-motor controller, and two prototype internal LAN boards. Software modules were developed in C language for monitor functions, i/o, and parallel port usage in each computer board. Also completed are the fabrication of half of the required number of computer boards, the procurement of 19.2 Kbaud RF modems for inter-robot communications, and the simulation of processing requirements for positioning receivers. In addition to the robotic platform, the fabrication of a local positioning system based on infrared signals is nearly completed. This positioning system will make the rovers into a moving reference system capable of performing site surveys. In addition, a four degree mechanical manipulator especially suited for coordinated teleoperation was conceptually designed and is currently being analyzed. This manipulator will be integrated into the rovers as their end effector. Twenty internal LAN cards fabricated by a commercial firm are being used, a prototype manipulator and a range finder for a positioning system were built, a prototype two-motor controller was designed, and one of the robots is performing its first telerobotic motion. In addition, the robots' internal LAN's were coordinated and tested, hardware design upgrades based on fabrication and fit experience were completed, and the positioning system is running. The rover system is able to perform simple tasks such as sensing and signaling; coordination systems which allow construction tasks to begin were established, and soon coordinated teams of robots in the laboratory will be able to manipulate common objects.

Video-based peer feedback through social networking for robotic surgery simulation: a multicenter randomized controlled trial.

PubMed

Carter, Stacey C; Chiang, Alexander; Shah, Galaxy; Kwan, Lorna; Montgomery, Jeffrey S; Karam, Amer; Tarnay, Christopher; Guru, Khurshid A; Hu, Jim C

2015-05-01

To examine the feasibility and outcomes of video-based peer feedback through social networking to facilitate robotic surgical skill acquisition. The acquisition of surgical skills may be challenging for novel techniques and/or those with prolonged learning curves. Randomized controlled trial involving 41 resident physicians performing the Tubes (Da Vinci Intuitive Surgical, Sunnyvale, CA) simulator exercise with versus without peer feedback of video-recorded performance through a social networking Web page. Data collected included simulator exercise score, time to completion, and comfort and satisfaction with robotic surgery simulation. There were no baseline differences between the intervention group (n = 20) and controls (n = 21). The intervention group showed improvement in mean scores from session 1 to sessions 2 and 3 (60.7 vs 75.5, P < 0.001, and 60.7 vs 80.1, P < 0.001, respectively). The intervention group scored significantly higher than controls at sessions 2 and 3 (75.5 vs 59.6, P = 0.009, and 80.1 vs 65.9, P = 0.019, respectively). The mean time (seconds) to complete the task was shorter for the intervention group than for controls during sessions 2 and 3 (217.4 vs 279.0, P = 0.004, and 201.4 vs 261.9, P = 0.006, respectively). At the study conclusion, feedback subjects were more comfortable with robotic surgery than controls (90% vs 62%, P = 0.021) and expressed greater satisfaction with the learning experience (100% vs 67%, P = 0.014). Of the intervention subjects, 85% found that peer feedback was useful and 100% found it effective. Video-based peer feedback through social networking appears to be an effective paradigm for surgical education and accelerates the robotic surgery learning curve during simulation.

Hybrid Mobile Communication Networks for Planetary Exploration

NASA Technical Reports Server (NTRS)

Alena, Richard; Lee, Charles; Walker, Edward; Osenfort, John; Stone, Thom

2007-01-01

A paper discusses the continuing work of the Mobile Exploration System Project, which has been performing studies toward the design of hybrid communication networks for future exploratory missions to remote planets. A typical network could include stationary radio transceivers on a remote planet, mobile radio transceivers carried by humans and robots on the planet, terrestrial units connected via the Internet to an interplanetary communication system, and radio relay transceivers aboard spacecraft in orbit about the planet. Prior studies have included tests on prototypes of these networks deployed in Arctic and desert regions chosen to approximate environmental conditions on Mars. Starting from the findings of the prior studies, the paper discusses methods of analysis, design, and testing of the hybrid communication networks. It identifies key radio-frequency (RF) and network engineering issues. Notable among these issues is the study of wireless LAN throughput loss due to repeater use, RF signal strength, and network latency variations. Another major issue is that of using RF-link analysis to ensure adequate link margin in the face of statistical variations in signal strengths.

Motion planning with complete knowledge using a colored SOM.

PubMed

Vleugels, J; Kok, J N; Overmars, M

1997-01-01

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space. In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively. In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot. The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.

A reliability study on brain activation during active and passive arm movements supported by an MRI-compatible robot.

PubMed

Estévez, Natalia; Yu, Ningbo; Brügger, Mike; Villiger, Michael; Hepp-Reymond, Marie-Claude; Riener, Robert; Kollias, Spyros

2014-11-01

In neurorehabilitation, longitudinal assessment of arm movement related brain function in patients with motor disability is challenging due to variability in task performance. MRI-compatible robots monitor and control task performance, yielding more reliable evaluation of brain function over time. The main goals of the present study were first to define the brain network activated while performing active and passive elbow movements with an MRI-compatible arm robot (MaRIA) in healthy subjects, and second to test the reproducibility of this activation over time. For the fMRI analysis two models were compared. In model 1 movement onset and duration were included, whereas in model 2 force and range of motion were added to the analysis. Reliability of brain activation was tested with several statistical approaches applied on individual and group activation maps and on summary statistics. The activated network included mainly the primary motor cortex, primary and secondary somatosensory cortex, superior and inferior parietal cortex, medial and lateral premotor regions, and subcortical structures. Reliability analyses revealed robust activation for active movements with both fMRI models and all the statistical methods used. Imposed passive movements also elicited mainly robust brain activation for individual and group activation maps, and reliability was improved by including additional force and range of motion using model 2. These findings demonstrate that the use of robotic devices, such as MaRIA, can be useful to reliably assess arm movement related brain activation in longitudinal studies and may contribute in studies evaluating therapies and brain plasticity following injury in the nervous system.

Towards organ printing: engineering an intra-organ branched vascular tree.

PubMed

Visconti, Richard P; Kasyanov, Vladimir; Gentile, Carmine; Zhang, Jing; Markwald, Roger R; Mironov, Vladimir

2010-03-01

Effective vascularization of thick three-dimensional engineered tissue constructs is a problem in tissue engineering. As in native organs, a tissue-engineered intra-organ vascular tree must be comprised of a network of hierarchically branched vascular segments. Despite this requirement, current tissue-engineering efforts are still focused predominantly on engineering either large-diameter macrovessels or microvascular networks. We present the emerging concept of organ printing or robotic additive biofabrication of an intra-organ branched vascular tree, based on the ability of vascular tissue spheroids to undergo self-assembly. The feasibility and challenges of this robotic biofabrication approach to intra-organ vascularization for tissue engineering based on organ-printing technology using self-assembling vascular tissue spheroids including clinically relevantly vascular cell sources are analyzed. It is not possible to engineer 3D thick tissue or organ constructs without effective vascularization. An effective intra-organ vascular system cannot be built by the simple connection of large-diameter vessels and microvessels. Successful engineering of functional human organs suitable for surgical implantation will require concomitant engineering of a 'built in' intra-organ branched vascular system. Organ printing enables biofabrication of human organ constructs with a 'built in' intra-organ branched vascular tree.

Adaptive control strategies for flexible robotic arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity if not unstable closed-loop behavior. Therefore a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

TALON: the telescope alert operation network system: intelligent linking of distributed autonomous robotic telescopes

NASA Astrophysics Data System (ADS)

White, Robert R.; Wren, James; Davis, Heath R.; Galassi, Mark; Starr, Daniel; Vestrand, W. T.; Wozniak, P.

2004-09-01

The internet has brought about great change in the astronomical community, but this interconnectivity is just starting to be exploited for use in instrumentation. Utilizing the internet for communicating between distributed astronomical systems is still in its infancy, but it already shows great potential. Here we present an example of a distributed network of telescopes that performs more efficiently in synchronous operation than as individual instruments. RAPid Telescopes for Optical Response (RAPTOR) is a system of telescopes at LANL that has intelligent intercommunication, combined with wide-field optics, temporal monitoring software, and deep-field follow-up capability all working in closed-loop real-time operation. The Telescope ALert Operations Network (TALON) is a network server that allows intercommunication of alert triggers from external and internal resources and controls the distribution of these to each of the telescopes on the network. TALON is designed to grow, allowing any number of telescopes to be linked together and communicate. Coupled with an intelligent alert client at each telescope, it can analyze and respond to each distributed TALON alert based on the telescopes needs and schedule.

An Overview of the Micro Pulse Lidar Network (MPLNET)

NASA Technical Reports Server (NTRS)

Welton, Ellsworth

2010-01-01

The NASA Micro Pulse Lidar Network (MPLNET) is a federated network of Micro Pulse Lidar (MPL) systems designed to measure aerosol and cloud vertical structure continuously, day and night, over long time periods required to contribute to climate change studies and provide ground validation for models and satellite sensors in the NASA Earth Observing System (FOS). At present, there are eighteen active sites worldwide, and several more in the planning stage. Numerous temporary sites are deployed in support of various field campaigns. Most MPLNET sites are co-located with sites in the NASA Aerosol Robotic Network (AERONET) to provide both column and vertically resolved aerosol and cloud data. MPLNET data and more information on the project are available at http://mpinet.gsfc.nasa.gov . Here we present a summary of the first ten years of MPLNET, along with an overview of our current status, specifically our version two data products and applications. Future network plans will be presented, with a focus on our activities in South East Asia.

Sensing and data classification for a robotic meteorite search

NASA Astrophysics Data System (ADS)

Pedersen, Liam; Apostolopoulos, Dimi; Whittaker, William L.; Benedix, Gretchen; Rousch, Ted

1999-01-01

Upcoming missions to Mars and the mon call for highly autonomous robots with capability to perform intra-site exploration, reason about their scientific finds, and perform comprehensive on-board analysis of data collected. An ideal case for testing such technologies and robot capabilities is the robotic search for Antarctic meteorites. The successful identification and classification of meteorites depends on sensing modalities and intelligent evaluation of acquired data. Data from color imagery and spectroscopic measurements are used to identify terrestrial rocks and distinguish them from meteorites. However, because of the large number of rocks and the high cost and delay of using some of the sensors, it is necessary to eliminate as many meteorite candidates as possible using cheap long range sensors, such as color cameras. More resource consuming sensor will be held in reserve for the more promising samples only. Bayes networks are used as the formalism for incrementally combing data from multiple sources in a statistically rigorous manner. Furthermore, they can be used to infer the utility of further sensor readings given currently known data. This information, along with cost estimates, in necessary for the sensing system to rationally schedule further sensor reading sand deployments. This paper address issues associated with sensor selection and implementation of an architecture for automatic identification of rocks and meteorites from a mobile robot.

Service oriented network architecture for control and management of home appliances

NASA Astrophysics Data System (ADS)

Hayakawa, Hiroshi; Koita, Takahiro; Sato, Kenya

2005-12-01

Recent advances in multimedia network systems and mechatronics have led to the development of a new generation of applications that associate the use of various multimedia objects with the behavior of multiple robotic actors. The connection of audio and video devices through high speed multimedia networks is expected to make the system more convenient to use. For example, many home appliances, such as a video camera, a display monitor, a video recorder, an audio system and so on, are being equipped with a communication interface in the near future. Recently some platforms (i.e. UPnP1, HAVi2 and so on) are proposed for constructing home networks; however, there are some issues to be solved to realize various services by connecting different equipment via the pervasive peer-to-peer network. UPnP offers network connectivity of PCs of intelligent home appliances, practically, which means to require a PC in the network to control other devices. Meanwhile, HAVi has been developed for intelligent AV equipments with sophisticated functions using high CPU power and large memory. Considering the targets of home alliances are embedded systems, this situation raises issues of software and hardware complexity, cost, power consumption and so on. In this study, we have proposed and developed the service oriented network architecture for control and management of home appliances, named SONICA (Service Oriented Network Interoperability for Component Adaptation), to address these issues described before.

Gamma--Ray burst afterglows with the Watcher robotic telescope

NASA Astrophysics Data System (ADS)

Topinka, M.; Hanlon, L.; Meehan, S.; Tisdall, P.; Jelínek, M.; Kubánek, P.; van Heerden, H.; Meintjes, P.

2014-12-01

The main scientific goal of the Watcher robotic telescope is the rapid follow-up observation of gamma--ray burst afterglows. Some examples of recent observations, including GRB 120327A and GRB 130606A, at a redshift of 5.9, are presented. The telescope has recently been successfully integrated into the GLORIA global robotic telescope network, which allows users to use the array for their own scientific projects.

Neural network-based multiple robot simultaneous localization and mapping.

PubMed

Saeedi, Sajad; Paull, Liam; Trentini, Michael; Li, Howard

2011-12-01

In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

Service Oriented Robotic Architecture for Space Robotics: Design, Testing, and Lessons Learned

NASA Technical Reports Server (NTRS)

Fluckiger, Lorenzo Jean Marc E; Utz, Hans Heinrich

2013-01-01

This paper presents the lessons learned from six years of experiments with planetary rover prototypes running the Service Oriented Robotic Architecture (SORA) developed by the Intelligent Robotics Group (IRG) at the NASA Ames Research Center. SORA relies on proven software engineering methods and technologies applied to space robotics. Based on a Service Oriented Architecture and robust middleware, SORA encompasses on-board robot control and a full suite of software tools necessary for remotely operated exploration missions. SORA has been eld tested in numerous scenarios of robotic lunar and planetary exploration. The experiments conducted by IRG with SORA exercise a large set of the constraints encountered in space applications: remote robotic assets, ight relevant science instruments, distributed operations, high network latencies and unreliable or intermittent communication links. In this paper, we present the results of these eld tests in regard to the developed architecture, and discuss its bene ts and limitations.

Building adaptive connectionist-based controllers: review of experiments in human-robot interaction, collective robotics, and computational neuroscience

NASA Astrophysics Data System (ADS)

Billard, Aude

2000-10-01

This paper summarizes a number of experiments in biologically inspired robotics. The common feature to all experiments is the use of artificial neural networks as the building blocks for the controllers. The experiments speak in favor of using a connectionist approach for designing adaptive and flexible robot controllers, and for modeling neurological processes. I present 1) DRAMA, a novel connectionist architecture, which has general property for learning time series and extracting spatio-temporal regularities in multi-modal and highly noisy data; 2) Robota, a doll-shaped robot, which imitates and learns a proto-language; 3) an experiment in collective robotics, where a group of 4 to 15 Khepera robots learn dynamically the topography of an environment whose features change frequently; 4) an abstract, computational model of primate ability to learn by imitation; 5) a model for the control of locomotor gaits in a quadruped legged robot.

An observatory control system for the University of Hawai'i 2.2m Telescope

NASA Astrophysics Data System (ADS)

McKay, Luke; Erickson, Christopher; Mukensnable, Donn; Stearman, Anthony; Straight, Brad

2016-07-01

The University of Hawai'i 2.2m telescope at Maunakea has operated since 1970, and has had several controls upgrades to date. The newest system will operate as a distributed hierarchy of GNU/Linux central server, networked single-board computers, microcontrollers, and a modular motion control processor for the main axes. Rather than just a telescope control system, this new effort is towards a cohesive, modular, and robust whole observatory control system, with design goals of fully robotic unattended operation, high reliability, and ease of maintenance and upgrade.

Task Analysis and Descriptions of Required Job Competencies of Robotics/Automated Systems Technicians. Outlines for New Courses and Modules.

ERIC Educational Resources Information Center

Hull, Daniel M.; Lovett, James E.

The six new robotics and automated systems specialty courses developed by the Robotics/Automated Systems Technician (RAST) project are described in this publication. Course titles are Fundamentals of Robotics and Automated Systems, Automated Systems and Support Components, Controllers for Robots and Automated Systems, Robotics and Automated…

Exploring the effects of dimensionality reduction in deep networks for force estimation in robotic-assisted surgery

NASA Astrophysics Data System (ADS)

Aviles, Angelica I.; Alsaleh, Samar; Sobrevilla, Pilar; Casals, Alicia

2016-03-01

Robotic-Assisted Surgery approach overcomes the limitations of the traditional laparoscopic and open surgeries. However, one of its major limitations is the lack of force feedback. Since there is no direct interaction between the surgeon and the tissue, there is no way of knowing how much force the surgeon is applying which can result in irreversible injuries. The use of force sensors is not practical since they impose different constraints. Thus, we make use of a neuro-visual approach to estimate the applied forces, in which the 3D shape recovery together with the geometry of motion are used as input to a deep network based on LSTM-RNN architecture. When deep networks are used in real time, pre-processing of data is a key factor to reduce complexity and improve the network performance. A common pre-processing step is dimensionality reduction which attempts to eliminate redundant and insignificant information by selecting a subset of relevant features to use in model construction. In this work, we show the effects of dimensionality reduction in a real-time application: estimating the applied force in Robotic-Assisted Surgeries. According to the results, we demonstrated positive effects of doing dimensionality reduction on deep networks including: faster training, improved network performance, and overfitting prevention. We also show a significant accuracy improvement, ranging from about 33% to 86%, over existing approaches related to force estimation.

The LCOGT near-Earth-object follow-up network

NASA Astrophysics Data System (ADS)

Lister, T.

2014-07-01

Las Cumbres Observatory Global Telescope (LCOGT) network is a planned homogeneous network that will eventually consist of over 35 telescopes at 6 locations in the northern and southern hemispheres [1]. This network is versatile and designed to respond rapidly to target of opportunity events and also to do long term monitoring of slowly changing astronomical phenomena. The global coverage of the network and the apertures of telescope available make the LCOGT network ideal for follow-up and characterization of a wide range of solar-system objects (e.g. asteroids, Kuiper-belt objects, comets) and in particular near-Earth objects (NEOs). There are 3 classes to the telescope resources: 2-meter aperture, 1-meter aperture and 0.4-meter aperture. We have been operating our two 2-meter telescopes since 2005 and began a specific program of NEO follow-up for the Pan-STARRS survey in October 2010. The combination of all-sky access, large aperture, rapid response, robotic operation and good site conditions allows us to provide time-critical follow-up astrometry and photometry on newly discovered objects and faint objects as they recede from the Earth, allowing the orbital arc to be extended and preventing loss of objects. These telescope resources have greatly increased as LCOGT has completed the first phase of the deployment, designated as ''Version 1.0'', with the installation, commissioning and ongoing operation of nine 1-meter telescopes. These are distributed among four sites with one 1-meter at McDonald Observatory (Texas), three telescopes at Cerro Tololo (Chile), three telescopes at SAAO (South Africa) and the final two telescope at Siding Spring Observatory (Australia). In addition to the 1-meter network, the scheduling and control system for the two 2-meter telescopes have been upgraded and unified with that of the 1-meter network to provide a coherent robotic telescopic network. The telescope network is now operating and observations are being executed remotely and robotically. I am using the LCOGT network to confirm newly detected NEO candidates produced by the major sky surveys such as Catalina Sky Survey (CSS) and Pan-STARRS (PS1) with additional targets coming from the NEOWISE satellite and the Palomar Transient Factory (PTF). Robotic observations of NEOs and other solar-system objects have been routinely carried out for several years on the 2-m and 1-m telescopes, with over 20,000 positional and magnitude measurements reported to the Minor Planet Center (MPC) in the last two years. We have developed software to automatically fetch candidates from Pan-STARRS and the MPC Confirmation Page, compute orbits and ephemerides, plan and schedule observations on the telescopes and retrieve the processed data [2]. The program is being expanded which will allow us to greatly increase the amount of survey discoveries that are followed-up, obtain accurate astrometry and provide important characterization data in the form of colors, lightcurves, rotation rates and spectra for NEOs. An increasing amount of time is being spent to obtain follow-up astrometry and photometry for radar-targeted objects in order to improve the orbits and determine the rotation periods. Priority for follow-up is now given to the fainter and most southern targets on the Confirmation Page, objects that are scheduled for Goldstone/Arecibo radar targeting and those objects which could become potential mission destinations for spacecraft. This will be extended to obtain more light curves of other NEOs which could be Near-Earth Object Human Space Flight Accessible Targets Study (NHATS) or Asteroid Retrieval Mission (ARM) targets. With the increase in time available from the LCOGT 1-meter network and commissioning of low-resolution spectrographs on the 2-meter telescopes for moving objects, this will produce a large advance in capabilities for NEO follow-up and characterization. This will produce an unprecedented network for NEO follow-up, particularly in the Southern Hemisphere where there is currently a shortage of suitable facilities. We will continue to develop our software to take advantage of the increased resources and capabilities of the LCOGT Network.

Switch for serial or parallel communication networks

DOEpatents

Crosette, D.B.

1994-07-19

A communication switch apparatus and a method for use in a geographically extensive serial, parallel or hybrid communication network linking a multi-processor or parallel processing system has a very low software processing overhead in order to accommodate random burst of high density data. Associated with each processor is a communication switch. A data source and a data destination, a sensor suite or robot for example, may also be associated with a switch. The configuration of the switches in the network are coordinated through a master processor node and depends on the operational phase of the multi-processor network: data acquisition, data processing, and data exchange. The master processor node passes information on the state to be assumed by each switch to the processor node associated with the switch. The processor node then operates a series of multi-state switches internal to each communication switch. The communication switch does not parse and interpret communication protocol and message routing information. During a data acquisition phase, the communication switch couples sensors producing data to the processor node associated with the switch, to a downlink destination on the communications network, or to both. It also may couple an uplink data source to its processor node. During the data exchange phase, the switch couples its processor node or an uplink data source to a downlink destination (which may include a processor node or a robot), or couples an uplink source to its processor node and its processor node to a downlink destination. 9 figs.

Switch for serial or parallel communication networks

DOEpatents

Crosette, Dario B.

1994-01-01

A communication switch apparatus and a method for use in a geographically extensive serial, parallel or hybrid communication network linking a multi-processor or parallel processing system has a very low software processing overhead in order to accommodate random burst of high density data. Associated with each processor is a communication switch. A data source and a data destination, a sensor suite or robot for example, may also be associated with a switch. The configuration of the switches in the network are coordinated through a master processor node and depends on the operational phase of the multi-processor network: data acquisition, data processing, and data exchange. The master processor node passes information on the state to be assumed by each switch to the processor node associated with the switch. The processor node then operates a series of multi-state switches internal to each communication switch. The communication switch does not parse and interpret communication protocol and message routing information. During a data acquisition phase, the communication switch couples sensors producing data to the processor node associated with the switch, to a downlink destination on the communications network, or to both. It also may couple an uplink data source to its processor node. During the data exchange phase, the switch couples its processor node or an uplink data source to a downlink destination (which may include a processor node or a robot), or couples an uplink source to its processor node and its processor node to a downlink destination.

The Moon is a Planet Too: Lunar Science and Robotic Exploration

NASA Technical Reports Server (NTRS)

Cohen, Barbara

2008-01-01

The first decades of the 21st century will be marked by major lunar science and exploration activities. The Moon is a witness to 4.5 billion years of solar system history, recording that history more completely and more clearly than any other planetary body. Lunar science encompasses early planetary evolution and differentiation, lava eruptions and fire fountains, impact scars throughout time, and billions of years of volatile input. I will cover the main outstanding issues in lunar science today and the most intriguing scientific opportunities made possible by renewed robotic and human lunar exploration. Barbara is a planetary scientist at NASA s Marshall Space Flight Center. She studies meteorites from the Moon, Mars and asteroids and has been to Antarctica twice to hunt for them. Barbara also works on the Mars Exploration Rovers Spirit and Opportunity and has an asteroid named after her. She is currently helping the Lunar Precursor Robotics Program on the Lunar Mapping and Modeling Project, a project tasked by the Exploration System Mission Directorate (ESMD) to develop maps and tools of the Moon to benefit the Constellation Program lunar planning. She is also supporting the Science Mission Directorate s (SMD) lunar flight projects line at Marshall as the co-chair of the Science Definition Team for NASA s next robotic landers, which will be nodes of the International Lunar Network, providing geophysical information about the Moon s interior structure and composition.

Design of Biomedical Robots for Phenotype Prediction Problems

PubMed Central

deAndrés-Galiana, Enrique J.; Sonis, Stephen T.

2016-01-01

Abstract Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem. PMID:27347715

Design of Biomedical Robots for Phenotype Prediction Problems.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan Luis; Sonis, Stephen T

2016-08-01

Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.

Thoughts turned into high-level commands: Proof-of-concept study of a vision-guided robot arm driven by functional MRI (fMRI) signals.

PubMed

Minati, Ludovico; Nigri, Anna; Rosazza, Cristina; Bruzzone, Maria Grazia

2012-06-01

Previous studies have demonstrated the possibility of using functional MRI to control a robot arm through a brain-machine interface by directly coupling haemodynamic activity in the sensory-motor cortex to the position of two axes. Here, we extend this work by implementing interaction at a more abstract level, whereby imagined actions deliver structured commands to a robot arm guided by a machine vision system. Rather than extracting signals from a small number of pre-selected regions, the proposed system adaptively determines at individual level how to map representative brain areas to the input nodes of a classifier network. In this initial study, a median action recognition accuracy of 90% was attained on five volunteers performing a game consisting of collecting randomly positioned coloured pawns and placing them into cups. The "pawn" and "cup" instructions were imparted through four mental imaginery tasks, linked to robot arm actions by a state machine. With the current implementation in MatLab language the median action recognition time was 24.3s and the robot execution time was 17.7s. We demonstrate the notion of combining haemodynamic brain-machine interfacing with computer vision to implement interaction at the level of high-level commands rather than individual movements, which may find application in future fMRI approaches relevant to brain-lesioned patients, and provide source code supporting further work on larger command sets and real-time processing. Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

Technology Developments Integrating a Space Network Communications Testbed

NASA Technical Reports Server (NTRS)

Kwong, Winston; Jennings, Esther; Clare, Loren; Leang, Dee

2006-01-01

As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enable its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions. It can simulate entire networks and can interface with external (testbed) systems. The key technology developments enabling the integration of MACHETE into a distributed testbed are the Monitor and Control module and the QualNet IP Network Emulator module. Specifically, the Monitor and Control module establishes a standard interface mechanism to centralize the management of each testbed component. The QualNet IP Network Emulator module allows externally generated network traffic to be passed through MACHETE to experience simulated network behaviors such as propagation delay, data loss, orbital effects and other communications characteristics, including entire network behaviors. We report a successful integration of MACHETE with a space communication testbed modeling a lunar exploration scenario. This document is the viewgraph slides of the presentation.

Master-slave robotic system for needle indentation and insertion.

PubMed

Shin, Jaehyun; Zhong, Yongmin; Gu, Chengfan

2017-12-01

Bilateral control of a master-slave robotic system is a challenging issue in robotic-assisted minimally invasive surgery. It requires the knowledge on contact interaction between a surgical (slave) robot and soft tissues. This paper presents a master-slave robotic system for needle indentation and insertion. This master-slave robotic system is able to characterize the contact interaction between the robotic needle and soft tissues. A bilateral controller is implemented using a linear motor for robotic needle indentation and insertion. A new nonlinear state observer is developed to online monitor the contact interaction with soft tissues. Experimental results demonstrate the efficacy of the proposed master-slave robotic system for robotic needle indentation and needle insertion.

Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators.

PubMed

Yang, Qinmin; Jagannathan, Sarangapani

2012-04-01

In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

The Infantry Squad: Decisive Force Now and in the Future

DTIC Science & Technology

2012-01-01

robotic improvements. ●A design that includes the human dimension as a foundation. In the Near Future The vignette that follows describes how the...view the …the truth is, the network needs to be available at the individual soldier level. Robots such as TALON allow warfighters to clear routes...quickly without having to wait for explosive ordnance dis- posal teams. Here a TALON robot inspects a suspected improvised explosive device. (P ho

CÆLIS: software for assimilation, management and processing data of an atmospheric measurement network

NASA Astrophysics Data System (ADS)

Fuertes, David; Toledano, Carlos; González, Ramiro; Berjón, Alberto; Torres, Benjamín; Cachorro, Victoria E.; de Frutos, Ángel M.

2018-02-01

Given the importance of the atmospheric aerosol, the number of instruments and measurement networks which focus on its characterization are growing. Many challenges are derived from standardization of protocols, monitoring of the instrument status to evaluate the network data quality and manipulation and distribution of large volume of data (raw and processed). CÆLIS is a software system which aims at simplifying the management of a network, providing tools by monitoring the instruments, processing the data in real time and offering the scientific community a new tool to work with the data. Since 2008 CÆLIS has been successfully applied to the photometer calibration facility managed by the University of Valladolid, Spain, in the framework of Aerosol Robotic Network (AERONET). Thanks to the use of advanced tools, this facility has been able to analyze a growing number of stations and data in real time, which greatly benefits the network management and data quality control. The present work describes the system architecture of CÆLIS and some examples of applications and data processing.

A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

PubMed Central

Axenie, Cristian; Richter, Christoph; Conradt, Jörg

2016-01-01

Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor. PMID:27775621

Robots with language.

PubMed

Parisi, Domenico

2010-01-01

Trying to understand human language by constructing robots that have language necessarily implies an embodied view of language, where the meaning of linguistic expressions is derived from the physical interactions of the organism with the environment. The paper describes a neural model of language according to which the robot's behaviour is controlled by a neural network composed of two sub-networks, one dedicated to the non-linguistic interactions of the robot with the environment and the other one to processing linguistic input and producing linguistic output. We present the results of a number of simulations using the model and we suggest how the model can be used to account for various language-related phenomena such as disambiguation, the metaphorical use of words, the pervasive idiomaticity of multi-word expressions, and mental life as talking to oneself. The model implies a view of the meaning of words and multi-word expressions as a temporal process that takes place in the entire brain and has no clearly defined boundaries. The model can also be extended to emotional words if we assume that an embodied view of language includes not only the interactions of the robot's brain with the external environment but also the interactions of the brain with what is inside the body.

The Impact of Human-Automation Collaboration in Decentralized Multiple Unmanned Vehicle Control

DTIC Science & Technology

2011-01-01

based decentralized auctions for robust task allocation ,[ IEEE Trans. Robot., vol. 25, no. 4, pp...operators can aid such systems by bringing their knowledge- based reasoning and experience to bear. Given a decentralized task planner and a goal- based ...experience to bear. Given a decentralized task planner and a goal- based operator interface for a network of unmanned vehicles in a search, track,

YADCLAN: yet another digitally-controlled linear artificial neuron.

PubMed

Frenger, Paul

2003-01-01

This paper updates the author's 1999 RMBS presentation on digitally controlled linear artificial neuron design. Each neuron is based on a standard operational amplifier having excitatory and inhibitory inputs, variable gain, an amplified linear analog output and an adjustable threshold comparator for digital output. This design employs a 1-wire serial network of digitally controlled potentiometers and resistors whose resistance values are set and read back under microprocessor supervision. This system embodies several unique and useful features, including: enhanced neuronal stability, dynamic reconfigurability and network extensibility. This artificial neuronal is being employed for feature extraction and pattern recognition in an advanced robotic application.

Intelligent navigation and accurate positioning of an assist robot in indoor environments

NASA Astrophysics Data System (ADS)

Hua, Bin; Rama, Endri; Capi, Genci; Jindai, Mitsuru; Tsuri, Yosuke

2017-12-01

Intact robot's navigation and accurate positioning in indoor environments are still challenging tasks. Especially in robot applications, assisting disabled and/or elderly people in museums/art gallery environments. In this paper, we present a human-like navigation method, where the neural networks control the wheelchair robot to reach the goal location safely, by imitating the supervisor's motions, and positioning in the intended location. In a museum similar environment, the mobile robot starts navigation from various positions, and uses a low-cost camera to track the target picture, and a laser range finder to make a safe navigation. Results show that the neural controller with the Conjugate Gradient Backpropagation training algorithm gives a robust response to guide the mobile robot accurately to the goal position.

Robotic systems in spine surgery.

PubMed

Onen, Mehmet Resid; Naderi, Sait

2014-01-01

Surgical robotic systems have been available for almost twenty years. The first surgical robotic systems were designed as supportive systems for laparoscopic approaches in general surgery (the first procedure was a cholecystectomy in 1987). The da Vinci Robotic System is the most common system used for robotic surgery today. This system is widely used in urology, gynecology and other surgical disciplines, and recently there have been initial reports of its use in spine surgery, for transoral access and anterior approaches for lumbar inter-body fusion interventions. SpineAssist, which is widely used in spine surgery, and Renaissance Robotic Systems, which are considered the next generation of robotic systems, are now FDA approved. These robotic systems are designed for use as guidance systems in spine instrumentation, cement augmentations and biopsies. The aim is to increase surgical accuracy while reducing the intra-operative exposure to harmful radiation to the patient and operating team personnel during the intervention. We offer a review of the published literature related to the use of robotic systems in spine surgery and provide information on using robotic systems.

Person detection, tracking and following using stereo camera

NASA Astrophysics Data System (ADS)

Wang, Xiaofeng; Zhang, Lilian; Wang, Duo; Hu, Xiaoping

2018-04-01

Person detection, tracking and following is a key enabling technology for mobile robots in many human-robot interaction applications. In this article, we present a system which is composed of visual human detection, video tracking and following. The detection is based on YOLO(You only look once), which applies a single convolution neural network(CNN) to the full image, thus can predict bounding boxes and class probabilities directly in one evaluation. Then the bounding box provides initial person position in image to initialize and train the KCF(Kernelized Correlation Filter), which is a video tracker based on discriminative classifier. At last, by using a stereo 3D sparse reconstruction algorithm, not only the position of the person in the scene is determined, but also it can elegantly solve the problem of scale ambiguity in the video tracker. Extensive experiments are conducted to demonstrate the effectiveness and robustness of our human detection and tracking system.

Coordinated Control of Slip Ratio for Wheeled Mobile Robots Climbing Loose Sloped Terrain

PubMed Central

Li, Zhengcai; Wang, Yang

2014-01-01

A challenging problem faced by wheeled mobile robots (WMRs) such as planetary rovers traversing loose sloped terrain is the inevitable longitudinal slip suffered by the wheels, which often leads to their deviation from the predetermined trajectory, reduced drive efficiency, and possible failures. This study investigates this problem using terramechanics analysis of the wheel-soil interaction. First, a slope-based wheel-soil interaction terramechanics model is built, and an online slip coordinated algorithm is designed based on the goal of optimal drive efficiency. An equation of state is established using the coordinated slip as the desired input and the actual slip as a state variable. To improve the robustness and adaptability of the control system, an adaptive neural network is designed. Analytical results and those of a simulation using Vortex demonstrate the significantly improved mobile performance of the WMR using the proposed control system. PMID:25276849

Coordinated control of slip ratio for wheeled mobile robots climbing loose sloped terrain.

PubMed

Li, Zhengcai; Wang, Yang

2014-01-01

A challenging problem faced by wheeled mobile robots (WMRs) such as planetary rovers traversing loose sloped terrain is the inevitable longitudinal slip suffered by the wheels, which often leads to their deviation from the predetermined trajectory, reduced drive efficiency, and possible failures. This study investigates this problem using terramechanics analysis of the wheel-soil interaction. First, a slope-based wheel-soil interaction terramechanics model is built, and an online slip coordinated algorithm is designed based on the goal of optimal drive efficiency. An equation of state is established using the coordinated slip as the desired input and the actual slip as a state variable. To improve the robustness and adaptability of the control system, an adaptive neural network is designed. Analytical results and those of a simulation using Vortex demonstrate the significantly improved mobile performance of the WMR using the proposed control system.

Soft computing-based terrain visual sensing and data fusion for unmanned ground robotic systems

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir

2006-05-01

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Design of an integrated master-slave robotic system for minimally invasive surgery.

PubMed

Li, Jianmin; Zhou, Ningxin; Wang, Shuxin; Gao, Yuanqian; Liu, Dongchun

2012-03-01

Minimally invasive surgery (MIS) robots are commonly used in hospitals and medical centres. However, currently available robotic systems are very complicated and huge, greatly raising system costs and the requirements of operating rooms. These disadvantages have become the major impediments to the expansion of MIS robots. An integrated MIS robotic system is proposed based on the analysis of advantages and disadvantages of different MIS robots. In the proposed system, the master manipulators, slave manipulators, image display device and control system have been designed as a whole. Modular design is adopted for the control system for easy maintenance and upgrade. The kinematic relations between the master and the slave are also investigated and embedded in software to realize intuitive movements of hand and instrument. Finally, animal experiments were designed to test the effectiveness of the robot. The robot realizes natural hand-eye movements between the master and the slave to facilitate MIS operations. The experimental results show that the robot can realize similar functions to those of current commercialized robots. The integrated design simplifies the robotic system and facilitates use of the robot. Compared with the commercialized robots, the proposed MIS robot achieves similar functions and features but with a smaller size and less weight. Copyright © 2011 John Wiley & Sons, Ltd.

Learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Hall, Ernest L.; Liao, Xiaoqun; Alhaj Ali, Souma M.

2003-10-01

Unlike intelligent industrial robots which often work in a structured factory setting, intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. However, such machines have many potential applications in medicine, defense, industry and even the home that make their study important. Sensors such as vision are needed. However, in many applications some form of learning is also required. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots. During the past 20 years, the use of intelligent industrial robots that are equipped not only with motion control systems but also with sensors such as cameras, laser scanners, or tactile sensors that permit adaptation to a changing environment has increased dramatically. However, relatively little has been done concerning learning. Adaptive and robust control permits one to achieve point to point and controlled path operation in a changing environment. This problem can be solved with a learning control. In the unstructured environment, the terrain and consequently the load on the robot"s motors are constantly changing. Learning the parameters of a proportional, integral and derivative controller (PID) and artificial neural network provides an adaptive and robust control. Learning may also be used for path following. Simulations that include learning may be conducted to see if a robot can learn its way through a cluttered array of obstacles. If a situation is performed repetitively, then learning can also be used in the actual application. To reach an even higher degree of autonomous operation, a new level of learning is required. Recently learning theories such as the adaptive critic have been proposed. In this type of learning a critic provides a grade to the controller of an action module such as a robot. The creative control process is used that is "beyond the adaptive critic." A mathematical model of the creative control process is presented that illustrates the use for mobile robots. Examples from a variety of intelligent mobile robot applications are also presented. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots that could lead to many applications.

Multi-function robots with speech interaction and emotion feedback

NASA Astrophysics Data System (ADS)

Wang, Hongyu; Lou, Guanting; Ma, Mengchao

2018-03-01

Nowadays, the service robots have been applied in many public circumstances; however, most of them still don’t have the function of speech interaction, especially the function of speech-emotion interaction feedback. To make the robot more humanoid, Arduino microcontroller was used in this study for the speech recognition module and servo motor control module to achieve the functions of the robot’s speech interaction and emotion feedback. In addition, W5100 was adopted for network connection to achieve information transmission via Internet, providing broad application prospects for the robot in the area of Internet of Things (IoT).

CRUSER (Consortium for Robotics and Unmanned Systems Education and Research)

DTIC Science & Technology

2013-07-08

LPI) comms: covert and innovative networks – such as the “Digital Semaphore ” concept being taken to field experimentation in FY13. 3)  UxS support of...Resolution Full Motion Video for Unmanned Systems and Remote Sensing Jeff Weekley, NPS Digital Semaphore Dr. Don Brutzman, NPS •  7-10 May 2012... Semaphore CRUSER  Thread  1   Sept  2011   Warfare   InnovaKon   Workshop   May  2012   Technical   ConKnuum   Apr  2013

Method and System for Controlling a Dexterous Robot Execution Sequence Using State Classification

NASA Technical Reports Server (NTRS)

Sanders, Adam M. (Inventor); Quillin, Nathaniel (Inventor); Platt, Robert J., Jr. (Inventor); Pfeiffer, Joseph (Inventor); Permenter, Frank Noble (Inventor)

2014-01-01

A robotic system includes a dexterous robot and a controller. The robot includes a plurality of robotic joints, actuators for moving the joints, and sensors for measuring a characteristic of the joints, and for transmitting the characteristics as sensor signals. The controller receives the sensor signals, and is configured for executing instructions from memory, classifying the sensor signals into distinct classes via the state classification module, monitoring a system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the system state. A method for controlling the robot in the above system includes receiving the signals via the controller, classifying the signals using the state classification module, monitoring the present system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the present system state.

What makes a robot 'social'?

PubMed

Jones, Raya A

2017-08-01

Rhetorical moves that construct humanoid robots as social agents disclose tensions at the intersection of science and technology studies (STS) and social robotics. The discourse of robotics often constructs robots that are like us (and therefore unlike dumb artefacts). In the discourse of STS, descriptions of how people assimilate robots into their activities are presented directly or indirectly against the backdrop of actor-network theory, which prompts attributing agency to mundane artefacts. In contradistinction to both social robotics and STS, it is suggested here that to view a capacity to partake in dialogical action (to have a 'voice') is necessary for regarding an artefact as authentically social. The theme is explored partly through a critical reinterpretation of an episode that Morana Alač reported and analysed towards demonstrating her bodies-in-interaction concept. This paper turns to 'body' with particular reference to Gibsonian affordances theory so as to identify the level of analysis at which dialogicality enters social interactions.

The other half of the embodied mind.

PubMed

Parisi, Domenico

2011-01-01

Embodied theories of mind tend to be theories of the cognitive half of the mind and to ignore its emotional half while a complete theory of the mind should account for both halves. Robots are a new way of expressing theories of the mind which are less ambiguous and more capable to generate specific and non-controversial predictions than verbally expressed theories. We outline a simple robotic model of emotional states as states of a sub-part of the neural network controlling the robot's behavior which has specific properties and which allows the robot to make faster and more correct motivational decisions, and we describe possible extensions of the model to account for social emotional states and for the expression of emotions that, unlike those of current "emotional" robots, are really "felt" by the robot in that they play a well-identified functional role in the robot's behavior.

The Other Half of the Embodied Mind

PubMed Central

Parisi, Domenico

2011-01-01

Embodied theories of mind tend to be theories of the cognitive half of the mind and to ignore its emotional half while a complete theory of the mind should account for both halves. Robots are a new way of expressing theories of the mind which are less ambiguous and more capable to generate specific and non-controversial predictions than verbally expressed theories. We outline a simple robotic model of emotional states as states of a sub-part of the neural network controlling the robot's behavior which has specific properties and which allows the robot to make faster and more correct motivational decisions, and we describe possible extensions of the model to account for social emotional states and for the expression of emotions that, unlike those of current “emotional” robots, are really “felt” by the robot in that they play a well-identified functional role in the robot's behavior. PMID:21687441

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

NASA Technical Reports Server (NTRS)

Zornetzer, Steve; Gage, Douglas

2005-01-01

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion.

Characteristics of Behavior of Robots with Emotion Model

NASA Astrophysics Data System (ADS)

Sato, Shigehiko; Nozawa, Akio; Ide, Hideto

Cooperated multi robots system has much dominance in comparison with single robot system. It is able to adapt to various circumstances and has a flexibility for variation of tasks. However it has still problems to control each robot, though methods for control multi robots system have been studied. Recently, the robots have been coming into real scene. And emotion and sensitivity of the robots have been widely studied. In this study, human emotion model based on psychological interaction was adapt to multi robots system to achieve methods for organization of multi robots. The characteristics of behavior of multi robots system achieved through computer simulation were analyzed. As a result, very complexed and interesting behavior was emerged even though it has rather simple configuration. And it has flexiblity in various circumstances. Additional experiment with actual robots will be conducted based on the emotion model.

High resolution depth reconstruction from monocular images and sparse point clouds using deep convolutional neural network

NASA Astrophysics Data System (ADS)

Dimitrievski, Martin; Goossens, Bart; Veelaert, Peter; Philips, Wilfried

2017-09-01

Understanding the 3D structure of the environment is advantageous for many tasks in the field of robotics and autonomous vehicles. From the robot's point of view, 3D perception is often formulated as a depth image reconstruction problem. In the literature, dense depth images are often recovered deterministically from stereo image disparities. Other systems use an expensive LiDAR sensor to produce accurate, but semi-sparse depth images. With the advent of deep learning there have also been attempts to estimate depth by only using monocular images. In this paper we combine the best of the two worlds, focusing on a combination of monocular images and low cost LiDAR point clouds. We explore the idea that very sparse depth information accurately captures the global scene structure while variations in image patches can be used to reconstruct local depth to a high resolution. The main contribution of this paper is a supervised learning depth reconstruction system based on a deep convolutional neural network. The network is trained on RGB image patches reinforced with sparse depth information and the output is a depth estimate for each pixel. Using image and point cloud data from the KITTI vision dataset we are able to learn a correspondence between local RGB information and local depth, while at the same time preserving the global scene structure. Our results are evaluated on sequences from the KITTI dataset and our own recordings using a low cost camera and LiDAR setup.

Mobile wireless network for the urban environment

NASA Astrophysics Data System (ADS)

Budulas, Peter; Luu, Brian; Gopaul, Richard

2005-05-01

As the Army transforms into the Future Force, particular attention must be paid to operations in Complex and Urban Terrain. Our adversaries increasingly draw us into operations in the urban environment and one can presume that this trend will continue in future battles. In order to ensure that the United States Army maintains battlefield dominance, the Army Research Laboratory (ARL) is developing technology to equip our soldiers for the urban operations of the future. Sophisticated soldier borne systems will extend sensing to the individual soldier, and correspondingly, allow the soldier to establish an accurate picture of their surrounding environment utilizing information from local and remote assets. Robotic platforms will be an integral part of the future combat team. These platforms will augment the team with remote sensing modalities, task execution capabilities, and enhanced communication systems. To effectively utilize the products provided by each of these systems, collected data must be exchanged in real time to all affected entities. Therefore, the Army Research Laboratory is also developing the technology that will be required to support high bandwidth mobile communication in urban environments. This technology incorporates robotic systems that will allow connectivity in areas unreachable by traditional systems. This paper will address some of the issues of providing wireless connectivity in complex and urban terrain. It will further discuss approaches developed by the Army Research Laboratory to integrate communications capabilities into soldier and robotic systems and provide seamless connectivity between the elements of a combat team, and higher echelons.

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

NASA Astrophysics Data System (ADS)

Kapoor, Neha; Ohri, Jyoti

2017-02-01

Inspite of so much research, key technical problem, naming chattering of conventional, simple and robust SMC is still a challenge to the researchers and hence limits its practical application. However, newly developed soft computing based techniques can provide solution. In order to have advantages of conventional and heuristic soft computing based control techniques, in this paper various commonly used intelligent techniques, neural network, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) have been combined with sliding mode controller (SMC). For validation, proposed hybrid control schemes have been implemented for tracking a predefined trajectory by robotic manipulator, incorporating structured and unstructured uncertainties in the system. After reviewing numerous papers, all the commonly occurring uncertainties like continuous disturbance, uniform random white noise, static friction like coulomb friction and viscous friction, dynamic friction like Dhal friction and LuGre friction have been inserted in the system. Various performance indices like norm of tracking error, chattering in control input, norm of input torque, disturbance rejection, chattering rejection have been used. Comparative results show that with almost eliminated chattering the intelligent SMC controllers are found to be more efficient over simple SMC. It has also been observed from results that ANFIS based controller has the best tracking performance with the reduced burden on the system. No paper in the literature has found to have all these structured and unstructured uncertainties together for motion control of robotic manipulator.

Survey of Methods and Algorithms of Robot Swarm Aggregation

NASA Astrophysics Data System (ADS)

E Shlyakhov, N.; Vatamaniuk, I. V.; Ronzhin, A. L.

2017-01-01

The paper considers the problem of swarm aggregation of autonomous robots with the use of three methods based on the analogy of the behavior of biological objects. The algorithms substantiating the requirements for hardware realization of sensor, computer and network resources and propulsion devices are presented. Techniques for efficiency estimation of swarm aggregation via space-time characteristics are described. The developed model of the robot swarm reconfiguration into a predetermined three-dimensional shape is presented.

Cyber Moat: Adaptive Virtualized Network Framework for Deception and Disinformation

DTIC Science & Technology

2016-12-12

As one type of bots, web crawlers have been leveraged by search engines (e.g., Googlebot by Google) to popularize websites through website indexing...However, the number of malicious bots is increasing too. To regulate the behavior of crawlers, most websites include a file called "robots.txt" that...However, "robots.txt" only provides a guideline, and almost all malicious robots ignore it. Moreover, since this file is publicly available, malicious

Barriers to wider adoption of mobile telerobotic surgery: engineering, clinical and business challenges.

PubMed

Moses, Gerald R; Doarn, Charles R

2008-01-01

A portable robotic telesurgery network could remove the geographic disparity of surgical care and provide expert surgical support for first responders to traumatic injury. This is particularly relevant to battlefield medicine where surgical intervention is currently not available to the most perilous fighting circumstances. Similar utility applies to the peacetime healthcare mission. The authors identify the potential advantage to healthcare from a mobile robotic telesurgery system and specify barriers to the employability and acceptance of such a system. The proposed research roadmap will describe a portable telesurgery system that represe government/industrial recognition and reward for excellent care provided by quality surgeons. The provision of expert surgical care improves the outcomes of surgical intervention by reducing errors. For example, during the common procedure of laparoscopic cholecystectomy, distributed telesurgical care could normalize surgical performance and limit major variance of surgeon outliers; such as reducing common bile duct injury to the very low rate seen with operation by proficient surgeons.

Neural-Learning-Based Telerobot Control With Guaranteed Performance.

PubMed

Yang, Chenguang; Wang, Xinyu; Cheng, Long; Ma, Hongbin

2017-10-01

In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

Robotic System For Greenhouse Or Nursery

NASA Technical Reports Server (NTRS)

Gill, Paul; Montgomery, Jim; Silver, John; Heffelfinger, Neil; Simonton, Ward; Pease, Jim

1993-01-01

Report presents additional information about robotic system described in "Robotic Gripper With Force Control And Optical Sensors" (MFS-28537). "Flexible Agricultural Robotics Manipulator System" (FARMS) serves as prototype of robotic systems intended to enhance productivities of agricultural assembly-line-type facilities in large commercial greenhouses and nurseries.

A GPU-accelerated cortical neural network model for visually guided robot navigation.

PubMed

Beyeler, Michael; Oros, Nicolas; Dutt, Nikil; Krichmar, Jeffrey L

2015-12-01

Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

Creating Communications, Computing, and Networking Technology Development Road Maps for Future NASA Human and Robotic Missions

NASA Technical Reports Server (NTRS)

Bhasin, Kul; Hayden, Jeffrey L.

2005-01-01

For human and robotic exploration missions in the Vision for Exploration, roadmaps are needed for capability development and investments based on advanced technology developments. A roadmap development process was undertaken for the needed communications, and networking capabilities and technologies for the future human and robotics missions. The underlying processes are derived from work carried out during development of the future space communications architecture, an d NASA's Space Architect Office (SAO) defined formats and structures for accumulating data. Interrelationships were established among emerging requirements, the capability analysis and technology status, and performance data. After developing an architectural communications and networking framework structured around the assumed needs for human and robotic exploration, in the vicinity of Earth, Moon, along the path to Mars, and in the vicinity of Mars, information was gathered from expert participants. This information was used to identify the capabilities expected from the new infrastructure and the technological gaps in the way of obtaining them. We define realistic, long-term space communication architectures based on emerging needs and translate the needs into interfaces, functions, and computer processing that will be required. In developing our roadmapping process, we defined requirements for achieving end-to-end activities that will be carried out by future NASA human and robotic missions. This paper describes: 10 the architectural framework developed for analysis; 2) our approach to gathering and analyzing data from NASA, industry, and academia; 3) an outline of the technology research to be done, including milestones for technology research and demonstrations with timelines; and 4) the technology roadmaps themselves.

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

NASA Astrophysics Data System (ADS)

Zhou, Changjiu; Meng, Qingchun; Guo, Zhongwen; Qu, Wiefen; Yin, Bo

2002-04-01

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

NASA Technical Reports Server (NTRS)

Padgett, Mary L. (Editor)

1993-01-01

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

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

Jones, J.P.; Bangs, A.L.; Butler, P.L.

Hetero Helix is a programming environment which simulates shared memory on a heterogeneous network of distributed-memory computers. The machines in the network may vary with respect to their native operating systems and internal representation of numbers. Hetero Helix presents a simple programming model to developers, and also considers the needs of designers, system integrators, and maintainers. The key software technology underlying Hetero Helix is the use of a compiler'' which analyzes the data structures in shared memory and automatically generates code which translates data representations from the format native to each machine into a common format, and vice versa. Themore » design of Hetero Helix was motivated in particular by the requirements of robotics applications. Hetero Helix has been used successfully in an integration effort involving 27 CPUs in a heterogeneous network and a body of software totaling roughly 100,00 lines of code. 25 refs., 6 figs.« less

The GEOS-5 Neural Network Retrieval for AOD

NASA Astrophysics Data System (ADS)

Castellanos, P.; da Silva, A. M., Jr.

2017-12-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

The GEOS-5 Neural Network Retrieval (NNR) for AOD

NASA Technical Reports Server (NTRS)

Castellanos, Patricia; Da Silva, Arlindo

2017-01-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

Towards an SEMG-based tele-operated robot for masticatory rehabilitation.

PubMed

Kalani, Hadi; Moghimi, Sahar; Akbarzadeh, Alireza

2016-08-01

This paper proposes a real-time trajectory generation for a masticatory rehabilitation robot based on surface electromyography (SEMG) signals. We used two Gough-Stewart robots. The first robot was used as a rehabilitation robot while the second robot was developed to model the human jaw system. The legs of the rehabilitation robot were controlled by the SEMG signals of a tele-operator to reproduce the masticatory motion in the human jaw, supposedly mounted on the moving platform, through predicting the location of a reference point. Actual jaw motions and the SEMG signals from the masticatory muscles were recorded and used as output and input, respectively. Three different methods, namely time-delayed neural networks, time delayed fast orthogonal search, and time-delayed Laguerre expansion technique, were employed and compared to predict the kinematic parameters. The optimal model structures as well as the input delays were obtained for each model and each subject through a genetic algorithm. Equations of motion were obtained by the virtual work method. Fuzzy method was employed to develop a fuzzy impedance controller. Moreover, a jaw model was developed to demonstrate the time-varying behavior of the muscle lengths during the rehabilitation process. The three modeling methods were capable of providing reasonably accurate estimations of the kinematic parameters, although the accuracy and training/validation speed of time-delayed fast orthogonal search were higher than those of the other two aforementioned methods. Also, during a simulation study, the fuzzy impedance scheme proved successful in controlling the moving platform for the accurate navigation of the reference point in the desired trajectory. SEMG has been widely used as a control command for prostheses and exoskeleton robots. However, in the current study by employing the proposed rehabilitation robot the complete continuous profile of the clenching motion was reproduced in the sagittal plane. Copyright © 2016. Published by Elsevier Ltd.

Human likeness: cognitive and affective factors affecting adoption of robot-assisted learning systems

NASA Astrophysics Data System (ADS)

Yoo, Hosun; Kwon, Ohbyung; Lee, Namyeon

2016-07-01

With advances in robot technology, interest in robotic e-learning systems has increased. In some laboratories, experiments are being conducted with humanoid robots as artificial tutors because of their likeness to humans, the rich possibilities of using this type of media, and the multimodal interaction capabilities of these robots. The robot-assisted learning system, a special type of e-learning system, aims to increase the learner's concentration, pleasure, and learning performance dramatically. However, very few empirical studies have examined the effect on learning performance of incorporating humanoid robot technology into e-learning systems or people's willingness to accept or adopt robot-assisted learning systems. In particular, human likeness, the essential characteristic of humanoid robots as compared with conventional e-learning systems, has not been discussed in a theoretical context. Hence, the purpose of this study is to propose a theoretical model to explain the process of adoption of robot-assisted learning systems. In the proposed model, human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and para-social relationships; these factors are considered as possible determinants of the degree to which human cognition and affection are related to the adoption of robot-assisted learning systems.

Tandem robot control system and method for controlling mobile robots in tandem

DOEpatents

Hayward, David R.; Buttz, James H.; Shirey, David L.

2002-01-01

A control system for controlling mobile robots provides a way to control mobile robots, connected in tandem with coupling devices, to navigate across difficult terrain or in closed spaces. The mobile robots can be controlled cooperatively as a coupled system in linked mode or controlled individually as separate robots.

Towards a Sociological Understanding of Robots as Companions

NASA Astrophysics Data System (ADS)

van Oost, Ellen; Reed, Darren

While Information Communication Technologies (ICTs) have, in the past, primarily mediated or facilitated emotional bonding between humans, contemporary robot technologies are increasingly making the bond between human and robots the core issue. Thinking of robots as companions is not only a development that opens up huge potential for new applications, it also raises social and ethical issues. In this paper we will argue that current conceptions of human-robot companionship are primarily rooted in cognitive psychological traditions and provide important, yet limited understanding of the companion relationship. Elaborating on a sociological perspective on the appropriation of new technology, we will argue for a richer understanding of companionship that takes the situatedness (in location, network and time) of the use-context into account.