Study of Double-Weighted Graph Model and Optimal Path Planning for Tourist Scenic Area Oriented Intelligent Tour Guide

NASA Astrophysics Data System (ADS)

Shi, Y.; Long, Y.; Wi, X. L.

2014-04-01

When tourists visiting multiple tourist scenic spots, the travel line is usually the most effective road network according to the actual tour process, and maybe the travel line is different from planned travel line. For in the field of navigation, a proposed travel line is normally generated automatically by path planning algorithm, considering the scenic spots' positions and road networks. But when a scenic spot have a certain area and have multiple entrances or exits, the traditional described mechanism of single point coordinates is difficult to reflect these own structural features. In order to solve this problem, this paper focuses on the influence on the process of path planning caused by scenic spots' own structural features such as multiple entrances or exits, and then proposes a doubleweighted Graph Model, for the weight of both vertexes and edges of proposed Model can be selected dynamically. And then discusses the model building method, and the optimal path planning algorithm based on Dijkstra algorithm and Prim algorithm. Experimental results show that the optimal planned travel line derived from the proposed model and algorithm is more reasonable, and the travelling order and distance would be further optimized.

Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles

NASA Astrophysics Data System (ADS)

Hu, Xuemin; Chen, Long; Tang, Bo; Cao, Dongpu; He, Haibo

2018-02-01

This paper presents a real-time dynamic path planning method for autonomous driving that avoids both static and moving obstacles. The proposed path planning method determines not only an optimal path, but also the appropriate acceleration and speed for a vehicle. In this method, we first construct a center line from a set of predefined waypoints, which are usually obtained from a lane-level map. A series of path candidates are generated by the arc length and offset to the center line in the s - ρ coordinate system. Then, all of these candidates are converted into Cartesian coordinates. The optimal path is selected considering the total cost of static safety, comfortability, and dynamic safety; meanwhile, the appropriate acceleration and speed for the optimal path are also identified. Various types of roads, including single-lane roads and multi-lane roads with static and moving obstacles, are designed to test the proposed method. The simulation results demonstrate the effectiveness of the proposed method, and indicate its wide practical application to autonomous driving.

Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning

ERIC Educational Resources Information Center

Duan, Haibin; Li, Pei; Shi, Yuhui; Zhang, Xiangyin; Sun, Changhao

2015-01-01

This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the…

Path Searching Based Fault Automated Recovery Scheme for Distribution Grid with DG

NASA Astrophysics Data System (ADS)

Xia, Lin; Qun, Wang; Hui, Xue; Simeng, Zhu

2016-12-01

Applying the method of path searching based on distribution network topology in setting software has a good effect, and the path searching method containing DG power source is also applicable to the automatic generation and division of planned islands after the fault. This paper applies path searching algorithm in the automatic division of planned islands after faults: starting from the switch of fault isolation, ending in each power source, and according to the line load that the searching path traverses and the load integrated by important optimized searching path, forming optimized division scheme of planned islands that uses each DG as power source and is balanced to local important load. Finally, COBASE software and distribution network automation software applied are used to illustrate the effectiveness of the realization of such automatic restoration program.

A Comparison of Risk Sensitive Path Planning Methods for Aircraft Emergency Landing

NASA Technical Reports Server (NTRS)

Meuleau, Nicolas; Plaunt, Christian; Smith, David E.; Smith, Tristan

2009-01-01

Determining the best site to land a damaged aircraft presents some interesting challenges for standard path planning techniques. There are multiple possible locations to consider, the space is 3-dimensional with dynamics, the criteria for a good path is determined by overall risk rather than distance or time, and optimization really matters, since an improved path corresponds to greater expected survival rate. We have investigated a number of different path planning methods for solving this problem, including cell decomposition, visibility graphs, probabilistic road maps (PRMs), and local search techniques. In their pure form, none of these techniques have proven to be entirely satisfactory - some are too slow or unpredictable, some produce highly non-optimal paths or do not find certain types of paths, and some do not cope well with the dynamic constraints when controllability is limited. In the end, we are converging towards a hybrid technique that involves seeding a roadmap with a layered visibility graph, using PRM to extend that roadmap, and using local search to further optimize the resulting paths. We describe the techniques we have investigated, report on our experiments with these techniques, and discuss when and why various techniques were unsatisfactory.

Optimal guidance with obstacle avoidance for nap-of-the-earth flight

NASA Technical Reports Server (NTRS)

Pekelsma, Nicholas J.

1988-01-01

The development of automatic guidance is discussed for helicopter Nap-of-the-Earth (NOE) and near-NOE flight. It deals with algorithm refinements relating to automated real-time flight path planning and to mission planning. With regard to path planning, it relates rotorcraft trajectory characteristics to the NOE computation scheme and addresses real-time computing issues and both ride quality issues and pilot-vehicle interfaces. The automated mission planning algorithm refinements include route optimization, automatic waypoint generation, interactive applications, and provisions for integrating the results into the real-time path planning software. A microcomputer based mission planning workstation was developed and is described. Further, the application of Defense Mapping Agency (DMA) digital terrain to both the mission planning workstation and to automatic guidance is both discussed and illustrated.

Robotic path-finding in inverse treatment planning for stereotactic radiosurgery with continuous dose delivery

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

Vandewouw, Marlee M., E-mail: marleev@mie.utoronto

Purpose: Continuous dose delivery in radiation therapy treatments has been shown to decrease total treatment time while improving the dose conformity and distribution homogeneity over the conventional step-and-shoot approach. The authors develop an inverse treatment planning method for Gamma Knife® Perfexion™ that continuously delivers dose along a path in the target. Methods: The authors’ method is comprised of two steps: find a path within the target, then solve a mixed integer optimization model to find the optimal collimator configurations and durations along the selected path. Robotic path-finding techniques, specifically, simultaneous localization and mapping (SLAM) using an extended Kalman filter, aremore » used to obtain a path that travels sufficiently close to selected isocentre locations. SLAM is novelly extended to explore a 3D, discrete environment, which is the target discretized into voxels. Further novel extensions are incorporated into the steering mechanism to account for target geometry. Results: The SLAM method was tested on seven clinical cases and compared to clinical, Hamiltonian path continuous delivery, and inverse step-and-shoot treatment plans. The SLAM approach improved dose metrics compared to the clinical plans and Hamiltonian path continuous delivery plans. Beam-on times improved over clinical plans, and had mixed performance compared to Hamiltonian path continuous plans. The SLAM method is also shown to be robust to path selection inaccuracies, isocentre selection, and dose distribution. Conclusions: The SLAM method for continuous delivery provides decreased total treatment time and increased treatment quality compared to both clinical and inverse step-and-shoot plans, and outperforms existing path methods in treatment quality. It also accounts for uncertainty in treatment planning by accommodating inaccuracies.« less

Path Planning Method in Multi-obstacle Marine Environment

NASA Astrophysics Data System (ADS)

Zhang, Jinpeng; Sun, Hanxv

2017-12-01

In this paper, an improved algorithm for particle swarm optimization is proposed for the application of underwater robot in the complex marine environment. Not only did consider to avoid obstacles when path planning, but also considered the current direction and the size effect on the performance of the robot dynamics. The algorithm uses the trunk binary tree structure to construct the path search space and A * heuristic search method is used in the search space to find a evaluation standard path. Then the particle swarm algorithm to optimize the path by adjusting evaluation function, which makes the underwater robot in the current navigation easier to control, and consume less energy.

Research on Taxiway Path Optimization Based on Conflict Detection

PubMed Central

Zhou, Hang; Jiang, Xinxin

2015-01-01

Taxiway path planning is one of the effective measures to make full use of the airport resources, and the optimized paths can ensure the safety of the aircraft during the sliding process. In this paper, the taxiway path planning based on conflict detection is considered. Specific steps are shown as follows: firstly, make an improvement on A * algorithm, the conflict detection strategy is added to search for the shortest and safe path in the static taxiway network. Then, according to the sliding speed of aircraft, a time table for each node is determined and the safety interval is treated as the constraint to judge whether there is a conflict or not. The intelligent initial path planning model is established based on the results. Finally, make an example in an airport simulation environment, detect and relieve the conflict to ensure the safety. The results indicate that the model established in this paper is effective and feasible. Meanwhile, make comparison with the improved A*algorithm and other intelligent algorithms, conclude that the improved A*algorithm has great advantages. It could not only optimize taxiway path, but also ensure the safety of the sliding process and improve the operational efficiency. PMID:26226485

Research on Taxiway Path Optimization Based on Conflict Detection.

PubMed

Zhou, Hang; Jiang, Xinxin

2015-01-01

Taxiway path planning is one of the effective measures to make full use of the airport resources, and the optimized paths can ensure the safety of the aircraft during the sliding process. In this paper, the taxiway path planning based on conflict detection is considered. Specific steps are shown as follows: firstly, make an improvement on A * algorithm, the conflict detection strategy is added to search for the shortest and safe path in the static taxiway network. Then, according to the sliding speed of aircraft, a time table for each node is determined and the safety interval is treated as the constraint to judge whether there is a conflict or not. The intelligent initial path planning model is established based on the results. Finally, make an example in an airport simulation environment, detect and relieve the conflict to ensure the safety. The results indicate that the model established in this paper is effective and feasible. Meanwhile, make comparison with the improved A*algorithm and other intelligent algorithms, conclude that the improved A*algorithm has great advantages. It could not only optimize taxiway path, but also ensure the safety of the sliding process and improve the operational efficiency.

Path Planning Algorithms for Autonomous Border Patrol Vehicles

NASA Astrophysics Data System (ADS)

Lau, George Tin Lam

This thesis presents an online path planning algorithm developed for unmanned vehicles in charge of autonomous border patrol. In this Pursuit-Evasion game, the unmanned vehicle is required to capture multiple trespassers on its own before any of them reach a target safe house where they are safe from capture. The problem formulation is based on Isaacs' Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on Rapidly-exploring random trees (RRT) and is capable of producing trajectories within several seconds to capture 2 or 3 evaders. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by a nonlinear programming-based numerical optimal control solver. Experiments are also conducted on unmanned ground vehicles to show the feasibility of implementing the proposed online path planning algorithm on physical applications.

Trajectory Generation and Path Planning for Autonomous Aerobots

NASA Technical Reports Server (NTRS)

Sharma, Shivanjli; Kulczycki, Eric A.; Elfes, Alberto

2007-01-01

This paper presents global path planning algorithms for the Titan aerobot based on user defined waypoints in 2D and 3D space. The algorithms were implemented using information obtained through a planner user interface. The trajectory planning algorithms were designed to accurately represent the aerobot's characteristics, such as minimum turning radius. Additionally, trajectory planning techniques were implemented to allow for surveying of a planar area based solely on camera fields of view, airship altitude, and the location of the planar area's perimeter. The developed paths allow for planar navigation and three-dimensional path planning. These calculated trajectories are optimized to produce the shortest possible path while still remaining within realistic bounds of airship dynamics.

Curvature Continuous and Bounded Path Planning for Fixed-Wing UAVs

PubMed Central

Jiang, Peng; Li, Deshi; Sun, Tao

2017-01-01

Unmanned Aerial Vehicles (UAVs) play an important role in applications such as data collection and target reconnaissance. An accurate and optimal path can effectively increase the mission success rate in the case of small UAVs. Although path planning for UAVs is similar to that for traditional mobile robots, the special kinematic characteristics of UAVs (such as their minimum turning radius) have not been taken into account in previous studies. In this paper, we propose a locally-adjustable, continuous-curvature, bounded path-planning algorithm for fixed-wing UAVs. To deal with the curvature discontinuity problem, an optimal interpolation algorithm and a key-point shift algorithm are proposed based on the derivation of a curvature continuity condition. To meet the upper bound for curvature and to render the curvature extrema controllable, a local replanning scheme is designed by combining arcs and Bezier curves with monotonic curvature. In particular, a path transition mechanism is built for the replanning phase using minimum curvature circles for a planning philosophy. Numerical results demonstrate that the analytical planning algorithm can effectively generate continuous-curvature paths, while satisfying the curvature upper bound constraint and allowing UAVs to pass through all predefined waypoints in the desired mission region. PMID:28925960

Curvature Continuous and Bounded Path Planning for Fixed-Wing UAVs.

PubMed

Wang, Xiaoliang; Jiang, Peng; Li, Deshi; Sun, Tao

2017-09-19

Unmanned Aerial Vehicles (UAVs) play an important role in applications such as data collection and target reconnaissance. An accurate and optimal path can effectively increase the mission success rate in the case of small UAVs. Although path planning for UAVs is similar to that for traditional mobile robots, the special kinematic characteristics of UAVs (such as their minimum turning radius) have not been taken into account in previous studies. In this paper, we propose a locally-adjustable, continuous-curvature, bounded path-planning algorithm for fixed-wing UAVs. To deal with the curvature discontinuity problem, an optimal interpolation algorithm and a key-point shift algorithm are proposed based on the derivation of a curvature continuity condition. To meet the upper bound for curvature and to render the curvature extrema controllable, a local replanning scheme is designed by combining arcs and Bezier curves with monotonic curvature. In particular, a path transition mechanism is built for the replanning phase using minimum curvature circles for a planning philosophy. Numerical results demonstrate that the analytical planning algorithm can effectively generate continuous-curvature paths, while satisfying the curvature upper bound constraint and allowing UAVs to pass through all predefined waypoints in the desired mission region.

Thermal Stability of Al2O3/Silicone Composites as High-Temperature Encapsulants

NASA Astrophysics Data System (ADS)

Yao, Yiying

Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.

Path Planning for Robot based on Chaotic Artificial Potential Field Method

NASA Astrophysics Data System (ADS)

Zhang, Cheng

2018-03-01

Robot path planning in unknown environments is one of the hot research topics in the field of robot control. Aiming at the shortcomings of traditional artificial potential field methods, we propose a new path planning for Robot based on chaotic artificial potential field method. The path planning adopts the potential function as the objective function and introduces the robot direction of movement as the control variables, which combines the improved artificial potential field method with chaotic optimization algorithm. Simulations have been carried out and the results demonstrate that the superior practicality and high efficiency of the proposed method.

Aircraft path planning for optimal imaging using dynamic cost functions

NASA Astrophysics Data System (ADS)

Christie, Gordon; Chaudhry, Haseeb; Kochersberger, Kevin

2015-05-01

Unmanned aircraft development has accelerated with recent technological improvements in sensing and communications, which has resulted in an "applications lag" for how these aircraft can best be utilized. The aircraft are becoming smaller, more maneuverable and have longer endurance to perform sensing and sampling missions, but operating them aggressively to exploit these capabilities has not been a primary focus in unmanned systems development. This paper addresses a means of aerial vehicle path planning to provide a realistic optimal path in acquiring imagery for structure from motion (SfM) reconstructions and performing radiation surveys. This method will allow SfM reconstructions to occur accurately and with minimal flight time so that the reconstructions can be executed efficiently. An assumption is made that we have 3D point cloud data available prior to the flight. A discrete set of scan lines are proposed for the given area that are scored based on visibility of the scene. Our approach finds a time-efficient path and calculates trajectories between scan lines and over obstacles encountered along those scan lines. Aircraft dynamics are incorporated into the path planning algorithm as dynamic cost functions to create optimal imaging paths in minimum time. Simulations of the path planning algorithm are shown for an urban environment. We also present our approach for image-based terrain mapping, which is able to efficiently perform a 3D reconstruction of a large area without the use of GPS data.

Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Birge, Brian

2013-01-01

The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.

Interactive multi-objective path planning through a palette-based user interface

NASA Astrophysics Data System (ADS)

Shaikh, Meher T.; Goodrich, Michael A.; Yi, Daqing; Hoehne, Joseph

2016-05-01

n a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisfied commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a human is assigned the task of path planning for robot, the human may care about multiple objectives. This work proposes a graphical user interface (GUI) designed for interactive robot path-planning when an operator may prefer one objective over others or care about how multiple objectives are traded off. The GUI represents multiple objectives using the metaphor of an artist's palette. A distinct color is used to represent each objective, and tradeoffs among objectives are balanced in a manner that an artist mixes colors to get the desired shade of color. Thus, human intent is analogous to the artist's shade of color. We call the GUI an "Adverb Palette" where the word "Adverb" represents a specific type of objective for the path, such as the adverbs "quickly" and "safely" in the commands: "travel the path quickly", "make the journey safely". The novel interactive interface provides the user an opportunity to evaluate various alternatives (that tradeoff between different objectives) by allowing her to visualize the instantaneous outcomes that result from her actions on the interface. In addition to assisting analysis of various solutions given by an optimization algorithm, the palette has additional feature of allowing the user to define and visualize her own paths, by means of waypoints (guiding locations) thereby spanning variety for planning. The goal of the Adverb Palette is thus to provide a way for the user and robot to find an acceptable solution even though they use very different representations of the problem. Subjective evaluations suggest that even non-experts in robotics can carry out the planning tasks with a great deal of flexibility using the adverb palette.

Application of ant colony algorithm in path planning of the data center room robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Ma, Jianming; Wang, Ying

2017-05-01

According to the Internet Data Center (IDC) room patrol robot as the background, the robot in the search path of autonomous obstacle avoidance and path planning ability, worked out in advance of the robot room patrol mission. The simulation experimental results show that the improved ant colony algorithm for IDC room patrol robot obstacle avoidance planning, makes the robot along an optimal or suboptimal and safe obstacle avoidance path to reach the target point to complete the task. To prove the feasibility of the method.

New Polyazine-Bridged RuII,RhIII and RuII,RhI Supramolecular Photocatalysts for Water Reduction to Hydrogen Applicable for Solar Energy Conversion and Mechanistic Investigation of the Photocatalytic Cycle

NASA Astrophysics Data System (ADS)

Zhou, Rongwei

Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.

Optimal path planning for a mobile robot using cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Mohanty, Prases K.; Parhi, Dayal R.

2016-03-01

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.

Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind.

PubMed

Luo, He; Liang, Zhengzheng; Zhu, Moning; Hu, Xiaoxuan; Wang, Guoqiang

2018-01-01

Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided.

Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind

PubMed Central

Liang, Zhengzheng; Zhu, Moning; Hu, Xiaoxuan; Wang, Guoqiang

2018-01-01

Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided. PMID:29561888

Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Li, Xiaohui; Sun, Zhenping; Cao, Dongpu; Liu, Daxue; He, Hangen

2017-03-01

This study proposes a novel integrated local trajectory planning and tracking control (ILTPTC) framework for autonomous vehicles driving along a reference path with obstacles avoidance. For this ILTPTC framework, an efficient state-space sampling-based trajectory planning scheme is employed to smoothly follow the reference path. A model-based predictive path generation algorithm is applied to produce a set of smooth and kinematically-feasible paths connecting the initial state with the sampling terminal states. A velocity control law is then designed to assign a speed value at each of the points along the generated paths. An objective function considering both safety and comfort performance is carefully formulated for assessing the generated trajectories and selecting the optimal one. For accurately tracking the optimal trajectory while overcoming external disturbances and model uncertainties, a combined feedforward and feedback controller is developed. Both simulation analyses and vehicle testing are performed to verify the effectiveness of the proposed ILTPTC framework, and future research is also briefly discussed.

A Comparison of Two Path Planners for Planetary Rovers

NASA Technical Reports Server (NTRS)

Tarokh, M.; Shiller, Z.; Hayati, S.

1999-01-01

The paper presents two path planners suitable for planetary rovers. The first is based on fuzzy description of the terrain, and genetic algorithm to find a traversable path in a rugged terrain. The second planner uses a global optimization method with a cost function that is the path distance divided by the velocity limit obtained from the consideration of the rover static and dynamic stability. A description of both methods is provided, and the results of paths produced are given which show the effectiveness of the path planners in finding near optimal paths. The features of the methods and their suitability and application for rover path planning are compared

Vehicle path-planning in three dimensions using optics analogs for optimizing visibility and energy cost

NASA Technical Reports Server (NTRS)

Rowe, Neil C.; Lewis, David H.

1989-01-01

Path planning is an important issue for space robotics. Finding safe and energy-efficient paths in the presence of obstacles and other constraints can be complex although important. High-level (large-scale) path planning for robotic vehicles was investigated in three-dimensional space with obstacles, accounting for: (1) energy costs proportional to path length; (2) turn costs where paths change trajectory abruptly; and (3) safety costs for the danger associated with traversing a particular path due to visibility or invisibility from a fixed set of observers. Paths optimal with respect to these cost factors are found. Autonomous or semi-autonomous vehicles were considered operating either in a space environment around satellites and space platforms, or aircraft, spacecraft, or smart missiles operating just above lunar and planetary surfaces. One class of applications concerns minimizing detection, as for example determining the best way to make complex modifications to a satellite without being observed by hostile sensors; another example is verifying there are no paths (holes) through a space defense system. Another class of applications concerns maximizing detection, as finding a good trajectory between mountain ranges of a planet while staying reasonably close to the surface, or finding paths for a flight between two locations that maximize the average number of triangulation points available at any time along the path.

Path planning algorithms for assembly sequence planning. [in robot kinematics

NASA Technical Reports Server (NTRS)

Krishnan, S. S.; Sanderson, Arthur C.

1991-01-01

Planning for manipulation in complex environments often requires reasoning about the geometric and mechanical constraints which are posed by the task. In planning assembly operations, the automatic generation of operations sequences depends on the geometric feasibility of paths which permit parts to be joined into subassemblies. Feasible locations and collision-free paths must be present for part motions, robot and grasping motions, and fixtures. This paper describes an approach to reasoning about the feasibility of straight-line paths among three-dimensional polyhedral parts using an algebra of polyhedral cones. A second method recasts the feasibility conditions as constraints in a nonlinear optimization framework. Both algorithms have been implemented and results are presented.

Sampling-Based Coverage Path Planning for Complex 3D Structures

DTIC Science & Technology

2012-09-01

one such task, in which a single robot must sweep its end effector over the entirety of a known workspace. For two-dimensional environments, optimal...structures. First, we introduce a new algorithm for planning feasible coverage paths. It is more computationally efficient in problems of complex geometry...iteratively shortens and smooths a feasible coverage path; robot configurations are adjusted without violating any coverage con- straints. Third, we propose

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Ancient village fire escape path planning based on improved ant colony algorithm

NASA Astrophysics Data System (ADS)

Xia, Wei; Cao, Kang; Hu, QianChuan

2017-06-01

The roadways are narrow and perplexing in ancient villages, it brings challenges and difficulties for people to choose route to escape when a fire occurs. In this paper, a fire escape path planning method based on ant colony algorithm is presented according to the problem. The factors in the fire environment which influence the escape speed is introduced to improve the heuristic function of the algorithm, optimal transfer strategy, and adjustment pheromone volatile factor to improve pheromone update strategy adaptively, improve its dynamic search ability and search speed. Through simulation, the dynamic adjustment of the optimal escape path is obtained, and the method is proved to be feasible.

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

PubMed

Li, Bai; Gong, Li-gang; Yang, Wen-lun

2014-01-01

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Energy Optimal Path Planning: Integrating Coastal Ocean Modelling with Optimal Control

NASA Astrophysics Data System (ADS)

Subramani, D. N.; Haley, P. J., Jr.; Lermusiaux, P. F. J.

2016-02-01

A stochastic optimization methodology is formulated for computing energy-optimal paths from among time-optimal paths of autonomous vehicles navigating in a dynamic flow field. To set up the energy optimization, the relative vehicle speed and headings are considered to be stochastic, and new stochastic Dynamically Orthogonal (DO) level-set equations that govern their stochastic time-optimal reachability fronts are derived. Their solution provides the distribution of time-optimal reachability fronts and corresponding distribution of time-optimal paths. An optimization is then performed on the vehicle's energy-time joint distribution to select the energy-optimal paths for each arrival time, among all stochastic time-optimal paths for that arrival time. The accuracy and efficiency of the DO level-set equations for solving the governing stochastic level-set reachability fronts are quantitatively assessed, including comparisons with independent semi-analytical solutions. Energy-optimal missions are studied in wind-driven barotropic quasi-geostrophic double-gyre circulations, and in realistic data-assimilative re-analyses of multiscale coastal ocean flows. The latter re-analyses are obtained from multi-resolution 2-way nested primitive-equation simulations of tidal-to-mesoscale dynamics in the Middle Atlantic Bight and Shelbreak Front region. The effects of tidal currents, strong wind events, coastal jets, and shelfbreak fronts on the energy-optimal paths are illustrated and quantified. Results showcase the opportunities for longer-duration missions that intelligently utilize the ocean environment to save energy, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.

Sensitivity analysis of an optimization-based trajectory planner for autonomous vehicles in urban environments

NASA Astrophysics Data System (ADS)

Hardy, Jason; Campbell, Mark; Miller, Isaac; Schimpf, Brian

2008-10-01

The local path planner implemented on Cornell's 2007 DARPA Urban Challenge entry vehicle Skynet utilizes a novel mixture of discrete and continuous path planning steps to facilitate a safe, smooth, and human-like driving behavior. The planner first solves for a feasible path through the local obstacle map using a grid based search algorithm. The resulting path is then refined using a cost-based nonlinear optimization routine with both hard and soft constraints. The behavior of this optimization is influenced by tunable weighting parameters which govern the relative cost contributions assigned to different path characteristics. This paper studies the sensitivity of the vehicle's performance to these path planner weighting parameters using a data driven simulation based on logged data from the National Qualifying Event. The performance of the path planner in both the National Qualifying Event and in the Urban Challenge is also presented and analyzed.

Autonomous Navigation by a Mobile Robot

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance; Aghazarian, Hrand

2005-01-01

ROAMAN is a computer program for autonomous navigation of a mobile robot on a long (as much as hundreds of meters) traversal of terrain. Developed for use aboard a robotic vehicle (rover) exploring the surface of a remote planet, ROAMAN could also be adapted to similar use on terrestrial mobile robots. ROAMAN implements a combination of algorithms for (1) long-range path planning based on images acquired by mast-mounted, wide-baseline stereoscopic cameras, and (2) local path planning based on images acquired by body-mounted, narrow-baseline stereoscopic cameras. The long-range path-planning algorithm autonomously generates a series of waypoints that are passed to the local path-planning algorithm, which plans obstacle-avoiding legs between the waypoints. Both the long- and short-range algorithms use an occupancy-grid representation in computations to detect obstacles and plan paths. Maps that are maintained by the long- and short-range portions of the software are not shared because substantial localization errors can accumulate during any long traverse. ROAMAN is not guaranteed to generate an optimal shortest path, but does maintain the safety of the rover.

Mission-directed path planning for planetary rover exploration

NASA Astrophysics Data System (ADS)

Tompkins, Paul

2005-07-01

Robotic rovers uniquely benefit planetary exploration---they enable regional exploration with the precision of in-situ measurements, a combination impossible from an orbiting spacecraft or fixed lander. Mission planning for planetary rover exploration currently utilizes sophisticated software for activity planning and scheduling, but simplified path planning and execution approaches tailored for localized operations to individual targets. This approach is insufficient for the investigation of multiple, regionally distributed targets in a single command cycle. Path planning tailored for this task must consider the impact of large scale terrain on power, speed and regional access; the effect of route timing on resource availability; the limitations of finite resource capacity and other operational constraints on vehicle range and timing; and the mutual influence between traverses and upstream and downstream stationary activities. Encapsulating this reasoning in an efficient autonomous planner would allow a rover to continue operating rationally despite significant deviations from an initial plan. This research presents mission-directed path planning that enables an autonomous, strategic reasoning capability for robotic explorers. Planning operates in a space of position, time and energy. Unlike previous hierarchical approaches, it treats these dimensions simultaneously to enable globally-optimal solutions. The approach calls on a near incremental search algorithm designed for planning and re-planning under global constraints, in spaces of higher than two dimensions. Solutions under this method specify routes that avoid terrain obstacles, optimize the collection and use of rechargable energy, satisfy local and global mission constraints, and account for the time and energy of interleaved mission activities. Furthermore, the approach efficiently re-plans in response to updates in vehicle state and world models, and is well suited to online operation aboard a robot. Simulations exhibit that the new methodology succeeds where conventional path planners would fail. Three planetary-relevant field experiments demonstrate the power of mission-directed path planning in directing actual exploration robots. Offline mission-directed planning sustained a solar-powered rover in a 24-hour sun-synchronous traverse. Online planning and re-planning enabled full navigational autonomy of over 1 kilometer, and supported the execution of science activities distributed over hundreds of meters.

UCAV path planning in the presence of radar-guided surface-to-air missile threats

NASA Astrophysics Data System (ADS)

Zeitz, Frederick H., III

This dissertation addresses the problem of path planning for unmanned combat aerial vehicles (UCAVs) in the presence of radar-guided surface-to-air missiles (SAMs). The radars, collocated with SAM launch sites, operate within the structure of an Integrated Air Defense System (IADS) that permits communication and cooperation between individual radars. The problem is formulated in the framework of the interaction between three sub-systems: the aircraft, the IADS, and the missile. The main features of this integrated model are: The aircraft radar cross section (RCS) depends explicitly on both the aspect and bank angles; hence, the RCS and aircraft dynamics are coupled. The probabilistic nature of IADS tracking is accounted for; namely, the probability that the aircraft has been continuously tracked by the IADS depends on the aircraft RCS and range from the perspective of each radar within the IADS. Finally, the requirement to maintain tracking prior to missile launch and during missile flyout are also modeled. Based on this model, the problem of UCAV path planning is formulated as a minimax optimal control problem, with the aircraft bank angle serving as control. Necessary conditions of optimality for this minimax problem are derived. Based on these necessary conditions, properties of the optimal paths are derived. These properties are used to discretize the dynamic optimization problem into a finite-dimensional, nonlinear programming problem that can be solved numerically. Properties of the optimal paths are also used to initialize the numerical procedure. A homotopy method is proposed to solve the finite-dimensional, nonlinear programming problem, and a heuristic method is proposed to improve the discretization during the homotopy process. Based upon the properties of numerical solutions, a method is proposed for parameterizing and storing information for later recall in flight to permit rapid replanning in response to changing threats. Illustrative examples are presented that confirm the standard flying tactics of "denying range, aspect, and aim," by yielding flight paths that "weave" to avoid long exposures of aspects with large RCS.

Optimization and planning of operating theatre activities: an original definition of pathways and process modeling.

PubMed

Barbagallo, Simone; Corradi, Luca; de Ville de Goyet, Jean; Iannucci, Marina; Porro, Ivan; Rosso, Nicola; Tanfani, Elena; Testi, Angela

2015-05-17

The Operating Room (OR) is a key resource of all major hospitals, but it also accounts for up 40% of resource costs. Improving cost effectiveness, while maintaining a quality of care, is a universal objective. These goals imply an optimization of planning and a scheduling of the activities involved. This is highly challenging due to the inherent variable and unpredictable nature of surgery. A Business Process Modeling Notation (BPMN 2.0) was used for the representation of the "OR Process" (being defined as the sequence of all of the elementary steps between "patient ready for surgery" to "patient operated upon") as a general pathway ("path"). The path was then both further standardized as much as possible and, at the same time, keeping all of the key-elements that would allow one to address or define the other steps of planning, and the inherent and wide variability in terms of patient specificity. The path was used to schedule OR activity, room-by-room, and day-by-day, feeding the process from a "waiting list database" and using a mathematical optimization model with the objective of ending up in an optimized planning. The OR process was defined with special attention paid to flows, timing and resource involvement. Standardization involved a dynamics operation and defined an expected operating time for each operation. The optimization model has been implemented and tested on real clinical data. The comparison of the results reported with the real data, shows that by using the optimization model, allows for the scheduling of about 30% more patients than in actual practice, as well as to better exploit the OR efficiency, increasing the average operating room utilization rate up to 20%. The optimization of OR activity planning is essential in order to manage the hospital's waiting list. Optimal planning is facilitated by defining the operation as a standard pathway where all variables are taken into account. By allowing a precise scheduling, it feeds the process of planning and, further up-stream, the management of a waiting list in an interactive and bi-directional dynamic process.

Calibration of neural networks using genetic algorithms, with application to optimal path planning

NASA Technical Reports Server (NTRS)

Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel

1987-01-01

Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.

Kinematic path planning for space-based robotics

NASA Astrophysics Data System (ADS)

Seereeram, Sanjeev; Wen, John T.

1998-01-01

Future space robotics tasks require manipulators of significant dexterity, achievable through kinematic redundancy and modular reconfigurability, but with a corresponding complexity of motion planning. Existing research aims for full autonomy and completeness, at the expense of efficiency, generality or even user friendliness. Commercial simulators require user-taught joint paths-a significant burden for assembly tasks subject to collision avoidance, kinematic and dynamic constraints. Our research has developed a Kinematic Path Planning (KPP) algorithm which bridges the gap between research and industry to produce a powerful and useful product. KPP consists of three key components: path-space iterative search, probabilistic refinement, and an operator guidance interface. The KPP algorithm has been successfully applied to the SSRMS for PMA relocation and dual-arm truss assembly tasks. Other KPP capabilities include Cartesian path following, hybrid Cartesian endpoint/intermediate via-point planning, redundancy resolution and path optimization. KPP incorporates supervisory (operator) input at any detail to influence the solution, yielding desirable/predictable paths for multi-jointed arms, avoiding obstacles and obeying manipulator limits. This software will eventually form a marketable robotic planner suitable for commercialization in conjunction with existing robotic CAD/CAM packages.

Dual stage potential field method for robotic path planning

NASA Astrophysics Data System (ADS)

Singh, Pradyumna Kumar; Parida, Pramod Kumar

2018-04-01

Path planning for autonomous mobile robots are the root for all autonomous mobile systems. Various methods are used for optimization of path to be followed by the autonomous mobile robots. Artificial potential field based path planning method is one of the most used methods for the researchers. Various algorithms have been proposed using the potential field approach. But in most of the common problems are encounters while heading towards the goal or target. i.e. local minima problem, zero potential regions problem, complex shaped obstacles problem, target near obstacle problem. In this paper we provide a new algorithm in which two types of potential functions are used one after another. The former one is to use to get the probable points and later one for getting the optimum path. In this algorithm we consider only the static obstacle and goal.

Safe Maritime Autonomous Path Planning in a High Sea State

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Quadrelli, Marco; Huntsberger, Terrance L.

2014-01-01

This paper presents a path planning method for sea surface vehicles that prevents capsizing and bow-diving in a high sea-state. A key idea is to use response amplitude operators (RAOs) or, in control terminology, the transfer functions from a sea state to a vessel's motion, in order to find a set of speeds and headings that results in excessive pitch and roll oscillations. This information is translated to arithmetic constraints on the ship's velocity, which are passed to a model predictive control (MPC)-based path planner to find a safe and optimal path that achieves specified goals. An obstacle avoidance capability is also added to the path planner. The proposed method is demonstrated by simulations.

Visual environment recognition for robot path planning using template matched filters

NASA Astrophysics Data System (ADS)

Orozco-Rosas, Ulises; Picos, Kenia; Díaz-Ramírez, Víctor H.; Montiel, Oscar; Sepúlveda, Roberto

2017-08-01

A visual approach in environment recognition for robot navigation is proposed. This work includes a template matching filtering technique to detect obstacles and feasible paths using a single camera to sense a cluttered environment. In this problem statement, a robot can move from the start to the goal by choosing a single path between multiple possible ways. In order to generate an efficient and safe path for mobile robot navigation, the proposal employs a pseudo-bacterial potential field algorithm to derive optimal potential field functions using evolutionary computation. Simulation results are evaluated in synthetic and real scenes in terms of accuracy of environment recognition and efficiency of path planning computation.

Flight Planning

NASA Technical Reports Server (NTRS)

1991-01-01

Seagull Technology, Inc., Sunnyvale, CA, produced a computer program under a Langley Research Center Small Business Innovation Research (SBIR) grant called STAFPLAN (Seagull Technology Advanced Flight Plan) that plans optimal trajectory routes for small to medium sized airlines to minimize direct operating costs while complying with various airline operating constraints. STAFPLAN incorporates four input databases, weather, route data, aircraft performance, and flight-specific information (times, payload, crew, fuel cost) to provide the correct amount of fuel optimal cruise altitude, climb and descent points, optimal cruise speed, and flight path.

Mobile transporter path planning

NASA Technical Reports Server (NTRS)

Baffes, Paul; Wang, Lui

1990-01-01

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.

A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm.

PubMed

Wei, Kun; Ren, Bingyin

2018-02-13

In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human-Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most important issue which must be resolved first in the process of improving robotic manipulator intelligence. Among the path-planning methods, the Rapidly Exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator, especially in a complex environment because of its probability completeness, perfect expansion, and fast exploring speed over other planning methods. However, the existing RRT algorithm has a limitation in path planning for a robotic manipulator in a dynamic unstructured environment. Therefore, an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed. This method that targets a directional node extends and can increase the sampling speed and efficiency of RRT dramatically. A path optimization strategy based on the maximum curvature constraint is presented to generate a smooth and curved continuous executable path for a robotic manipulator. Finally, the correctness, effectiveness, and practicability of the proposed method are demonstrated and validated via a MATLAB static simulation and a Robot Operating System (ROS) dynamic simulation environment as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator. The proposed method not only provides great practical engineering significance for a robotic manipulator's obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of robots' path planning.

Introduction of a computer-based method for automated planning of reduction paths under consideration of simulated muscular forces.

PubMed

Buschbaum, Jan; Fremd, Rainer; Pohlemann, Tim; Kristen, Alexander

2017-08-01

Reduction is a crucial step in the surgical treatment of bone fractures. Finding an optimal path for restoring anatomical alignment is considered technically demanding because collisions as well as high forces caused by surrounding soft tissues can avoid desired reduction movements. The repetition of reduction movements leads to a trial-and-error process which causes a prolonged duration of surgery. By planning an appropriate reduction path-an optimal sequence of target-directed movements-these problems should be overcome. For this purpose, a computer-based method has been developed. Using the example of simple femoral shaft fractures, 3D models are generated out of CT images. A reposition algorithm aligns both fragments by reconstructing their broken edges. According to the criteria of a deduced planning strategy, a modified A*-algorithm searches collision-free route of minimal force from the dislocated into the computed target position. Muscular forces are considered using a musculoskeletal reduction model (OpenSim model), and bone collisions are detected by an appropriate method. Five femoral SYNBONE models were broken into different fracture classification types and were automatically reduced from ten randomly selected displaced positions. Highest mean translational and rotational error for achieving target alignment is [Formula: see text] and [Formula: see text]. Mean value and standard deviation of occurring forces are [Formula: see text] for M. tensor fasciae latae and [Formula: see text] for M. semitendinosus over all trials. These pathways are precise, collision-free, required forces are minimized, and thus regarded as optimal paths. A novel method for planning reduction paths under consideration of collisions and muscular forces is introduced. The results deliver additional knowledge for an appropriate tactical reduction procedure and can provide a basis for further navigated or robotic-assisted developments.

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen

2016-01-01

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen

2016-01-01

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881

A Robot Trajectory Optimization Approach for Thermal Barrier Coatings Used for Free-Form Components

NASA Astrophysics Data System (ADS)

Cai, Zhenhua; Qi, Beichun; Tao, Chongyuan; Luo, Jie; Chen, Yuepeng; Xie, Changjun

2017-10-01

This paper is concerned with a robot trajectory optimization approach for thermal barrier coatings. As the requirements of high reproducibility of complex workpieces increase, an optimal thermal spraying trajectory should not only guarantee an accurate control of spray parameters defined by users (e.g., scanning speed, spray distance, scanning step, etc.) to achieve coating thickness homogeneity but also help to homogenize the heat transfer distribution on the coating surface. A mesh-based trajectory generation approach is introduced in this work to generate path curves on a free-form component. Then, two types of meander trajectories are generated by performing a different connection method. Additionally, this paper presents a research approach for introducing the heat transfer analysis into the trajectory planning process. Combining heat transfer analysis with trajectory planning overcomes the defects of traditional trajectory planning methods (e.g., local over-heating), which helps form the uniform temperature field by optimizing the time sequence of path curves. The influence of two different robot trajectories on the process of heat transfer is estimated by coupled FEM models which demonstrates the effectiveness of the presented optimization approach.

Simulation based optimization on automated fibre placement process

NASA Astrophysics Data System (ADS)

Lei, Shi

2018-02-01

In this paper, a software simulation (Autodesk TruPlan & TruFiber) based method is proposed to optimize the automate fibre placement (AFP) process. Different types of manufacturability analysis are introduced to predict potential defects. Advanced fibre path generation algorithms are compared with respect to geometrically different parts. Major manufacturing data have been taken into consideration prior to the tool paths generation to achieve high success rate of manufacturing.

Remote sensing for industrial applications in the energy business: digital territorial data integration for planning of overhead power transmission lines (OHTLs)

NASA Astrophysics Data System (ADS)

Terrazzino, Alfonso; Volponi, Silvia; Borgogno Mondino, Enrico

2001-12-01

An investigation has been carried out, concerning remote sensing techniques, in order to assess their potential application to the energy system business: the most interesting results concern a new approach, based on digital data from remote sensing, to infrastructures with a large territorial distribution: in particular OverHead Transmission Lines, for the high voltage transmission and distribution of electricity on large distances. Remote sensing could in principle be applied to all the phases of the system lifetime, from planning to design, to construction, management, monitoring and maintenance. In this article, a remote sensing based approach is presented, targeted to the line planning: optimization of OHTLs path and layout, according to different parameters (technical, environmental and industrial). Planning new OHTLs is of particular interest in emerging markets, where typically the cartography is missing or available only on low accuracy scale (1:50.000 and lower), often not updated. Multi- spectral images can be used to generate thematic maps of the region of interest for the planning (soil coverage). Digital Elevation Models (DEMs), allow the planners to easily access the morphologic information of the surface. Other auxiliary information from local laws, environmental instances, international (IEC) standards can be integrated in order to perform an accurate optimized path choice and preliminary spotting of the OHTLs. This operation is carried out by an ABB proprietary optimization algorithm: the output is a preliminary path that bests fits the optimization parameters of the line in a life cycle approach.

Development and demonstration of an on-board mission planner for helicopters

NASA Technical Reports Server (NTRS)

Deutsch, Owen L.; Desai, Mukund

1988-01-01

Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project.

Rapid, parallel path planning by propagating wavefronts of spiking neural activity

PubMed Central

Ponulak, Filip; Hopfield, John J.

2013-01-01

Efficient path planning and navigation is critical for animals, robotics, logistics and transportation. We study a model in which spatial navigation problems can rapidly be solved in the brain by parallel mental exploration of alternative routes using propagating waves of neural activity. A wave of spiking activity propagates through a hippocampus-like network, altering the synaptic connectivity. The resulting vector field of synaptic change then guides a simulated animal to the appropriate selected target locations. We demonstrate that the navigation problem can be solved using realistic, local synaptic plasticity rules during a single passage of a wavefront. Our model can find optimal solutions for competing possible targets or learn and navigate in multiple environments. The model provides a hypothesis on the possible computational mechanisms for optimal path planning in the brain, at the same time it is useful for neuromorphic implementations, where the parallelism of information processing proposed here can fully be harnessed in hardware. PMID:23882213

Path optimization with limited sensing ability

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

Kang, Sung Ha, E-mail: kang@math.gatech.edu; Kim, Seong Jun, E-mail: skim396@math.gatech.edu; Zhou, Haomin, E-mail: hmzhou@math.gatech.edu

2015-10-15

We propose a computational strategy to find the optimal path for a mobile sensor with limited coverage to traverse a cluttered region. The goal is to find one of the shortest feasible paths to achieve the complete scan of the environment. We pose the problem in the level set framework, and first consider a related question of placing multiple stationary sensors to obtain the full surveillance of the environment. By connecting the stationary locations using the nearest neighbor strategy, we form the initial guess for the path planning problem of the mobile sensor. Then the path is optimized by reducingmore » its length, via solving a system of ordinary differential equations (ODEs), while maintaining the complete scan of the environment. Furthermore, we use intermittent diffusion, which converts the ODEs into stochastic differential equations (SDEs), to find an optimal path whose length is globally minimal. To improve the computation efficiency, we introduce two techniques, one to remove redundant connecting points to reduce the dimension of the system, and the other to deal with the entangled path so the solution can escape the local traps. Numerical examples are shown to illustrate the effectiveness of the proposed method.« less

Energy-optimal path planning in the coastal ocean

NASA Astrophysics Data System (ADS)

Subramani, Deepak N.; Haley, Patrick J.; Lermusiaux, Pierre F. J.

2017-05-01

We integrate data-driven ocean modeling with the stochastic Dynamically Orthogonal (DO) level-set optimization methodology to compute and study energy-optimal paths, speeds, and headings for ocean vehicles in the Middle-Atlantic Bight (MAB) region. We hindcast the energy-optimal paths from among exact time-optimal paths for the period 28 August 2006 to 9 September 2006. To do so, we first obtain a data-assimilative multiscale reanalysis, combining ocean observations with implicit two-way nested multiresolution primitive-equation simulations of the tidal-to-mesoscale dynamics in the region. Second, we solve the reduced-order stochastic DO level-set partial differential equations (PDEs) to compute the joint probability of minimum arrival time, vehicle-speed time series, and total energy utilized. Third, for each arrival time, we select the vehicle-speed time series that minimize the total energy utilization from the marginal probability of vehicle-speed and total energy. The corresponding energy-optimal path and headings are obtained through the exact particle-backtracking equation. Theoretically, the present methodology is PDE-based and provides fundamental energy-optimal predictions without heuristics. Computationally, it is 3-4 orders of magnitude faster than direct Monte Carlo methods. For the missions considered, we analyze the effects of the regional tidal currents, strong wind events, coastal jets, shelfbreak front, and other local circulations on the energy-optimal paths. Results showcase the opportunities for vehicles that intelligently utilize the ocean environment to minimize energy usage, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.

Optimal path planning for video-guided smart munitions via multitarget tracking

NASA Astrophysics Data System (ADS)

Borkowski, Jeffrey M.; Vasquez, Juan R.

2006-05-01

An advent in the development of smart munitions entails autonomously modifying target selection during flight in order to maximize the value of the target being destroyed. A unique guidance law can be constructed that exploits both attribute and kinematic data obtained from an onboard video sensor. An optimal path planning algorithm has been developed with the goals of obstacle avoidance and maximizing the value of the target impacted by the munition. Target identification and classification provides a basis for target value which is used in conjunction with multi-target tracks to determine an optimal waypoint for the munition. A dynamically feasible trajectory is computed to provide constraints on the waypoint selection. Results demonstrate the ability of the autonomous system to avoid moving obstacles and revise target selection in flight.

Optimisation des trajectoires d'un systeme de gestion de vol d'avions pour la reduction des couts de vol

NASA Astrophysics Data System (ADS)

Sidibe, Souleymane

The implementation and monitoring of operational flight plans is a major occupation for a crew of commercial flights. The purpose of this operation is to set the vertical and lateral trajectories followed by airplane during phases of flight: climb, cruise, descent, etc. These trajectories are subjected to conflicting economical constraints: minimization of flight time and minimization of fuel consumed and environmental constraints. In its task of mission planning, the crew is assisted by the Flight Management System (FMS) which is used to construct the path to follow and to predict the behaviour of the aircraft along the flight plan. The FMS considered in our research, particularly includes an optimization model of flight only by calculating the optimal speed profile that minimizes the overall cost of flight synthesized by a criterion of cost index following a steady cruising altitude. However, the model based solely on optimization of the speed profile is not sufficient. It is necessary to expand the current optimization for simultaneous optimization of the speed and altitude in order to determine an optimum cruise altitude that minimizes the overall cost when the path is flown with the optimal speed profile. Then, a new program was developed. The latter is based on the method of dynamic programming invented by Bellman to solve problems of optimal paths. In addition, the improvement passes through research new patterns of trajectories integrating ascendant cruises and using the lateral plane with the effect of the weather: wind and temperature. Finally, for better optimization, the program takes into account constraint of flight domain of aircrafts which utilize the FMS.

A Flexible Toolkit Supporting Knowledge-based Tactical Planning for Ground Forces

DTIC Science & Technology

2011-06-01

assigned to each of the Special Areas to model its temporal behaviour . In Figure 5 an optimal path going over two defined intermediate points is...which area can be reached by an armoured infantry platoon within a given time interval, which path should be taken by a support unit to minimize...al. 2008]. Although trained commanders and staff personnel may achieve very accurate planning results, time consuming procedures are excluded when

Development of preoperative planning software for transforaminal endoscopic surgery and the guidance for clinical applications.

PubMed

Chen, Xiaojun; Cheng, Jun; Gu, Xin; Sun, Yi; Politis, Constantinus

2016-04-01

Preoperative planning is of great importance for transforaminal endoscopic techniques applied in percutaneous endoscopic lumbar discectomy. In this study, a modular preoperative planning software for transforaminal endoscopic surgery was developed and demonstrated. The path searching method is based on collision detection, and the oriented bounding box was constructed for the anatomical models. Then, image reformatting algorithms were developed for multiplanar reconstruction which provides detailed anatomical information surrounding the virtual planned path. Finally, multithread technique was implemented to realize the steady-state condition of the software. A preoperative planning software for transforaminal endoscopic surgery (TE-Guider) was developed; seven cases of patients with symptomatic lumbar disc herniations were planned preoperatively using TE-Guider. The distances to the midlines and the direction of the optimal paths were exported, and each result was in line with the empirical value. TE-Guider provides an efficient and cost-effective way to search the ideal path and entry point for the puncture. However, more clinical cases will be conducted to demonstrate its feasibility and reliability.

An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle

NASA Astrophysics Data System (ADS)

Cao, Haotian; Song, Xiaolin; Zhao, Song; Bao, Shan; Huang, Zhi

2017-08-01

Automated driving has received a broad of attentions from the academia and industry, since it is effective to greatly reduce the severity of potential traffic accidents and achieve the ultimate automobile safety and comfort. This paper presents an optimal model-based trajectory following architecture for highly automated vehicle in its driving tasks such as automated guidance or lane keeping, which includes a velocity-planning module, a steering controller and a velocity-tracking controller. The velocity-planning module considering the optimal time-consuming and passenger comforts simultaneously could generate a smooth velocity profile. The robust sliding mode control (SMC) steering controller with adaptive preview time strategy could not only track the target path well, but also avoid a big lateral acceleration occurred in its path-tracking progress due to a fuzzy-adaptive preview time mechanism introduced. In addition, an SMC controller with input-output linearisation method for velocity tracking is built and validated. Simulation results show this trajectory following architecture are effective and feasible for high automated driving vehicle, comparing with the Driver-in-the-Loop simulations performed by an experienced driver and novice driver, respectively. The simulation results demonstrate that the present trajectory following architecture could plan a satisfying longitudinal speed profile, track the target path well and safely when dealing with different road geometry structure, it ensures a good time efficiency and driving comfort simultaneously.

Air Vehicle Path Planning

DTIC Science & Technology

2001-12-13

6-18 6.13. Apollonius Circle for the Case of Two Unequal Power Radars . . . 6-20 6.14. Solution Triangle...Voronoi edge is an Apollonius circle [32, 19]. In this section, we are concerned with the optimality of the Voronoi path for the two radar exposure...Comparison of Cost vs. Path Length for Constrained Trajectories Around and Between Two Radars 6-18 from the two radars is an Apollonius circle

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.

Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms

NASA Technical Reports Server (NTRS)

Knudson, Matthew D.; Colby, Mitchell; Tumer, Kagan

2014-01-01

Dynamic flight environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal flight paths. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance

Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms

NASA Technical Reports Server (NTRS)

Colby, Mitchell; Knudson, Matthew D.; Tumer, Kagan

2014-01-01

Dynamic environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal paths through these environments. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially with the number of agents in the system. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance.

Path Planning For A Class Of Cutting Operations

NASA Astrophysics Data System (ADS)

Tavora, Jose

1989-03-01

Optimizing processing time in some contour-cutting operations requires solving the so-called no-load path problem. This problem is formulated and an approximate resolution method (based on heuristic search techniques) is described. Results for real-life instances (clothing layouts in the apparel industry) are presented and evaluated.

Optimal slew path planning for the Sino-French Space-based multiband astronomical Variable Objects Monitor mission

NASA Astrophysics Data System (ADS)

She, Yuchen; Li, Shuang

2018-01-01

The planning algorithm to calculate a satellite's optimal slew trajectory with a given keep-out constraint is proposed. An energy-optimal formulation is proposed for the Space-based multiband astronomical Variable Objects Monitor Mission Analysis and Planning (MAP) system. The innovative point of the proposed planning algorithm lies in that the satellite structure and control limitation are not considered as optimization constraints but are formulated into the cost function. This modification is able to relieve the burden of the optimizer and increases the optimization efficiency, which is the major challenge for designing the MAP system. Mathematical analysis is given to prove that there is a proportional mapping between the formulation and the satellite controller output. Simulations with different scenarios are given to demonstrate the efficiency of the developed algorithm.

Sensor-Oriented Path Planning for Multiregion Surveillance with a Single Lightweight UAV SAR

PubMed Central

Li, Jincheng; Chen, Jie; Wang, Pengbo; Li, Chunsheng

2018-01-01

In the surveillance of interested regions by unmanned aerial vehicle (UAV), system performance relies greatly on the motion control strategy of the UAV and the operation characteristics of the onboard sensors. This paper investigates the 2D path planning problem for the lightweight UAV synthetic aperture radar (SAR) system in an environment of multiple regions of interest (ROIs), the sizes of which are comparable to the radar swath width. Taking into account the special requirements of the SAR system on the motion of the platform, we model path planning for UAV SAR as a constrained multiobjective optimization problem (MOP). Based on the fact that the UAV route can be designed in the map image, an image-based path planner is proposed in this paper. First, the neighboring ROIs are merged by the morphological operation. Then, the parts of routes for data collection of the ROIs can be located according to the geometric features of the ROIs and the observation geometry of UAV SAR. Lastly, the route segments for ROIs surveillance are connected by a path planning algorithm named the sampling-based sparse A* search (SSAS) algorithm. Simulation experiments in real scenarios demonstrate that the proposed sensor-oriented path planner can improve the reconnaissance performance of lightweight UAV SAR greatly compared with the conventional zigzag path planner. PMID:29439447

Sensor-Oriented Path Planning for Multiregion Surveillance with a Single Lightweight UAV SAR.

PubMed

Li, Jincheng; Chen, Jie; Wang, Pengbo; Li, Chunsheng

2018-02-11

In the surveillance of interested regions by unmanned aerial vehicle (UAV), system performance relies greatly on the motion control strategy of the UAV and the operation characteristics of the onboard sensors. This paper investigates the 2D path planning problem for the lightweight UAV synthetic aperture radar (SAR) system in an environment of multiple regions of interest (ROIs), the sizes of which are comparable to the radar swath width. Taking into account the special requirements of the SAR system on the motion of the platform, we model path planning for UAV SAR as a constrained multiobjective optimization problem (MOP). Based on the fact that the UAV route can be designed in the map image, an image-based path planner is proposed in this paper. First, the neighboring ROIs are merged by the morphological operation. Then, the parts of routes for data collection of the ROIs can be located according to the geometric features of the ROIs and the observation geometry of UAV SAR. Lastly, the route segments for ROIs surveillance are connected by a path planning algorithm named the sampling-based sparse A* search (SSAS) algorithm. Simulation experiments in real scenarios demonstrate that the proposed sensor-oriented path planner can improve the reconnaissance performance of lightweight UAV SAR greatly compared with the conventional zigzag path planner.

Current-Sensitive Path Planning for an Underactuated Free-Floating Ocean Sensorweb

NASA Technical Reports Server (NTRS)

Dahl, Kristen P.; Thompson, David R.; McLaren, David; Chao, Yi; Chien, Steve

2011-01-01

This work investigates multi-agent path planning in strong, dynamic currents using thousands of highly under-actuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats' motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting.

Robustness of mission plans for unmanned aircraft

NASA Astrophysics Data System (ADS)

Niendorf, Moritz

This thesis studies the robustness of optimal mission plans for unmanned aircraft. Mission planning typically involves tactical planning and path planning. Tactical planning refers to task scheduling and in multi aircraft scenarios also includes establishing a communication topology. Path planning refers to computing a feasible and collision-free trajectory. For a prototypical mission planning problem, the traveling salesman problem on a weighted graph, the robustness of an optimal tour is analyzed with respect to changes to the edge costs. Specifically, the stability region of an optimal tour is obtained, i.e., the set of all edge cost perturbations for which that tour is optimal. The exact stability region of solutions to variants of the traveling salesman problems is obtained from a linear programming relaxation of an auxiliary problem. Edge cost tolerances and edge criticalities are derived from the stability region. For Euclidean traveling salesman problems, robustness with respect to perturbations to vertex locations is considered and safe radii and vertex criticalities are introduced. For weighted-sum multi-objective problems, stability regions with respect to changes in the objectives, weights, and simultaneous changes are given. Most critical weight perturbations are derived. Computing exact stability regions is intractable for large instances. Therefore, tractable approximations are desirable. The stability region of solutions to relaxations of the traveling salesman problem give under approximations and sets of tours give over approximations. The application of these results to the two-neighborhood and the minimum 1-tree relaxation are discussed. Bounds on edge cost tolerances and approximate criticalities are obtainable likewise. A minimum spanning tree is an optimal communication topology for minimizing the cumulative transmission power in multi aircraft missions. The stability region of a minimum spanning tree is given and tolerances, stability balls, and criticalities are derived. This analysis is extended to Euclidean minimum spanning trees. This thesis aims at enabling increased mission performance by providing means of assessing the robustness and optimality of a mission and methods for identifying critical elements. Examples of the application to mission planning in contested environments, cargo aircraft mission planning, multi-objective mission planning, and planning optimal communication topologies for teams of unmanned aircraft are given.

Planning Paths Through Singularities in the Center of Mass Space

NASA Technical Reports Server (NTRS)

Doggett, William R.; Messner, William C.; Juang, Jer-Nan

1998-01-01

The center of mass space is a convenient space for planning motions that minimize reaction forces at the robot's base or optimize the stability of a mechanism. A unique problem associated with path planning in the center of mass space is the potential existence of multiple center of mass images for a single Cartesian obstacle, since a single center of mass location can correspond to multiple robot joint configurations. The existence of multiple images results in a need to either maintain multiple center of mass obstacle maps or to update obstacle locations when the robot passes through a singularity, such as when it moves from an elbow-up to an elbow-down configuration. To illustrate the concepts presented in this paper, a path is planned for an example task requiring motion through multiple center of mass space maps. The object of the path planning algorithm is to locate the bang- bang acceleration profile that minimizes the robot's base reactions in the presence of a single Cartesian obstacle. To simplify the presentation, only non-redundant robots are considered and joint non-linearities are neglected.

Treatment Planning and Image Guidance for Radiofrequency Ablations of Large Tumors

PubMed Central

Ren, Hongliang; Campos-Nanez, Enrique; Yaniv, Ziv; Banovac, Filip; Abeledo, Hernan; Hata, Nobuhiko; Cleary, Kevin

2014-01-01

This article addresses the two key challenges in computer-assisted percutaneous tumor ablation: planning multiple overlapping ablations for large tumors while avoiding critical structures, and executing the prescribed plan. Towards semi-automatic treatment planning for image-guided surgical interventions, we develop a systematic approach to the needle-based ablation placement task, ranging from pre-operative planning algorithms to an intra-operative execution platform. The planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization formulation, which consists of optimal path selection and ablation coverage optimization based on integer programming. The system implementation is presented and validated in phantom studies and on an animal model. The presented system can potentially be further extended for other ablation techniques such as cryotherapy. PMID:24235279

Implications of path tolerance and path characteristics on critical vehicle manoeuvres

NASA Astrophysics Data System (ADS)

Lundahl, K.; Frisk, E.; Nielsen, L.

2017-12-01

Path planning and path following are core components in safe autonomous driving. Typically, a path planner provides a path with some tolerance on how tightly the path should be followed. Based on that, and other path characteristics, for example, sharpness of curves, a speed profile needs to be assigned so that the vehicle can stay within the given tolerance without going unnecessarily slow. Here, such trajectory planning is based on optimal control formulations where critical cases arise as on-the-limit solutions. The study focuses on heavy commercial vehicles, causing rollover to be of a major concern, due to the relatively high centre of gravity. Several results are obtained on required model complexity depending on path characteristics, for example, quantification of required path tolerance for a simple model to be sufficient, quantification of when yaw inertia needs to be considered in more detail, and how the curvature rate of change interplays with available friction. Overall, in situations where the vehicle is subject to a wide range of driving conditions, from good transport roads to more tricky avoidance manoeuvres, the requirements on the path following will vary. For this, the provided results form a basis for real-time path following.

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

A new optimization tool path planning for 3-axis end milling of free-form surfaces based on efficient machining intervals

NASA Astrophysics Data System (ADS)

Vu, Duy-Duc; Monies, Frédéric; Rubio, Walter

2018-05-01

A large number of studies, based on 3-axis end milling of free-form surfaces, seek to optimize tool path planning. Approaches try to optimize the machining time by reducing the total tool path length while respecting the criterion of the maximum scallop height. Theoretically, the tool path trajectories that remove the most material follow the directions in which the machined width is the largest. The free-form surface is often considered as a single machining area. Therefore, the optimization on the entire surface is limited. Indeed, it is difficult to define tool trajectories with optimal feed directions which generate largest machined widths. Another limiting point of previous approaches for effectively reduce machining time is the inadequate choice of the tool. Researchers use generally a spherical tool on the entire surface. However, the gains proposed by these different methods developed with these tools lead to relatively small time savings. Therefore, this study proposes a new method, using toroidal milling tools, for generating toolpaths in different regions on the machining surface. The surface is divided into several regions based on machining intervals. These intervals ensure that the effective radius of the tool, at each cutter-contact points on the surface, is always greater than the radius of the tool in an optimized feed direction. A parallel plane strategy is then used on the sub-surfaces with an optimal specific feed direction for each sub-surface. This method allows one to mill the entire surface with efficiency greater than with the use of a spherical tool. The proposed method is calculated and modeled using Maple software to find optimal regions and feed directions in each region. This new method is tested on a free-form surface. A comparison is made with a spherical cutter to show the significant gains obtained with a toroidal milling cutter. Comparisons with CAM software and experimental validations are also done. The results show the efficiency of the method.

A variational dynamic programming approach to robot-path planning with a distance-safety criterion

NASA Technical Reports Server (NTRS)

Suh, Suk-Hwan; Shin, Kang G.

1988-01-01

An approach to robot-path planning is developed by considering both the traveling distance and the safety of the robot. A computationally-efficient algorithm is developed to find a near-optimal path with a weighted distance-safety criterion by using a variational calculus and dynamic programming (VCDP) method. The algorithm is readily applicable to any factory environment by representing the free workspace as channels. A method for deriving these channels is also proposed. Although it is developed mainly for two-dimensional problems, this method can be easily extended to a class of three-dimensional problems. Numerical examples are presented to demonstrate the utility and power of this method.

Dynamic Programming Algorithms for Planning and Robotics in Continuous Domains and the Hamilton-Jacobi Equation

DTIC Science & Technology

2008-09-22

provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently...CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 72 19a. NAME OF RESPONSIBLE PERSON a . REPORT unclassified b...2008 Ian Mitchell, University of British Columbia 3 Basic Path Planning • Find the optimal path p(s) to a target (or from a source) • Inputs – Cost c

The research of autonomous obstacle avoidance of mobile robot based on multi-sensor integration

NASA Astrophysics Data System (ADS)

Zhao, Ming; Han, Baoling

2016-11-01

The object of this study is the bionic quadruped mobile robot. The study has proposed a system design plan for mobile robot obstacle avoidance with the binocular stereo visual sensor and the self-control 3D Lidar integrated with modified ant colony optimization path planning to realize the reconstruction of the environmental map. Because the working condition of a mobile robot is complex, the result of the 3D reconstruction with a single binocular sensor is undesirable when feature points are few and the light condition is poor. Therefore, this system integrates the stereo vision sensor blumblebee2 and the Lidar sensor together to detect the cloud information of 3D points of environmental obstacles. This paper proposes the sensor information fusion technology to rebuild the environment map. Firstly, according to the Lidar data and visual data on obstacle detection respectively, and then consider two methods respectively to detect the distribution of obstacles. Finally fusing the data to get the more complete, more accurate distribution of obstacles in the scene. Then the thesis introduces ant colony algorithm. It has analyzed advantages and disadvantages of the ant colony optimization and its formation cause deeply, and then improved the system with the help of the ant colony optimization to increase the rate of convergence and precision of the algorithm in robot path planning. Such improvements and integrations overcome the shortcomings of the ant colony optimization like involving into the local optimal solution easily, slow search speed and poor search results. This experiment deals with images and programs the motor drive under the compiling environment of Matlab and Visual Studio and establishes the visual 2.5D grid map. Finally it plans a global path for the mobile robot according to the ant colony algorithm. The feasibility and effectiveness of the system are confirmed by ROS and simulation platform of Linux.

Planning Under Uncertainty: Methods and Applications

DTIC Science & Technology

2010-06-09

begun research into fundamental algorithms for optimization and re?optimization of continuous optimization problems (such as linear and quadratic... algorithm yields a 14.3% improvement over the original design while saving 68.2 % of the simulation evaluations compared to standard sample-path...They provide tools for building and justifying computational algorithms for such problems. Year. 2010 Month: 03 Final Research under this grant

Hybrid Motion Planning with Multiple Destinations

NASA Technical Reports Server (NTRS)

Clouse, Jeffery

1998-01-01

In our initial proposal, we laid plans for developing a hybrid motion planning system that combines the concepts of visibility-based motion planning, artificial potential field based motion planning, evolutionary constrained optimization, and reinforcement learning. Our goal was, and still is, to produce a hybrid motion planning system that outperforms the best traditional motion planning systems on problems with dynamic environments. The proposed hybrid system will be in two parts the first is a global motion planning system and the second is a local motion planning system. The global system will take global information about the environment, such as the placement of the obstacles and goals, and produce feasible paths through those obstacles. We envision a system that combines the evolutionary-based optimization and visibility-based motion planning to achieve this end.

Hierarchical Motion Planning for Autonomous Aerial and Terrestrial Vehicles

NASA Astrophysics Data System (ADS)

Cowlagi, Raghvendra V.

Autonomous mobile robots---both aerial and terrestrial vehicles---have gained immense importance due to the broad spectrum of their potential military and civilian applications. One of the indispensable requirements for the autonomy of a mobile vehicle is the vehicle's capability of planning and executing its motion, that is, finding appropriate control inputs for the vehicle such that the resulting vehicle motion satisfies the requirements of the vehicular task. The motion planning and control problem is inherently complex because it involves two disparate sub-problems: (1) satisfaction of the vehicular task requirements, which requires tools from combinatorics and/or formal methods, and (2) design of the vehicle control laws, which requires tools from dynamical systems and control theory. Accordingly, this problem is usually decomposed and solved over two levels of hierarchy. The higher level, called the geometric path planning level, finds a geometric path that satisfies the vehicular task requirements, e.g., obstacle avoidance. The lower level, called the trajectory planning level, involves sufficient smoothening of this geometric path followed by a suitable time parametrization to obtain a reference trajectory for the vehicle. Although simple and efficient, such hierarchical decomposition suffers a serious drawback: the geometric path planner has no information of the kinematical and dynamical constraints of the vehicle. Consequently, the geometric planner may produce paths that the trajectory planner cannot transform into a feasible reference trajectory. Two main ideas appear in the literature to remedy this problem: (a) randomized sampling-based planning, which eliminates the geometric planner altogether by planning in the vehicle state space, and (b) geometric planning supported by feedback control laws. The former class of methods suffer from a lack of optimality of the resultant trajectory, while the latter class of methods makes a restrictive assumption concerning the vehicle kinematical model. We propose a hierarchical motion planning framework based on a novel mode of interaction between these two levels of planning. This interaction rests on the solution of a special shortest-path problem on graphs, namely, one using costs defined on multiple edge transitions in the path instead of the usual single edge transition costs. These costs are provided by a local trajectory generation algorithm, which we implement using model predictive control and the concept of effective target sets for simplifying the non-convex constraints involved in the problem. The proposed motion planner ensures "consistency" between the two levels of planning, i.e., a guarantee that the higher level geometric path is always associated with a kinematically and dynamically feasible trajectory. The main contributions of this thesis are: 1. A motion planning framework based on history-dependent costs (H-costs) in cell decomposition graphs for incorporating vehicle dynamical constraints: this framework offers distinct advantages in comparison with the competing approaches of discretization of the state space, of randomized sampling-based motion planning, and of local feedback-based, decoupled hierarchical motion planning, 2. An efficient and flexible algorithm for finding optimal H-cost paths, 3. A precise and general formulation of a local trajectory problem (the tile motion planning problem) that allows independent development of the discrete planner and the trajectory planner, while maintaining "compatibility" between the two planners, 4. A local trajectory generation algorithm using mpc, and the application of the concept of effective target sets for a significant simplification of the local trajectory generation problem, 5. The geometric analysis of curvature-bounded traversal of rectangular channels, leading to less conservative results in comparison with a result reported in the literature, and also to the efficient construction of effective target sets for the solution of the tile motion planning problem, 6. A wavelet-based multi-resolution path planning scheme, and a proof of completeness of the proposed scheme: such proofs are altogether absent from other works on multi-resolution path planning, 7. A technique for extracting all information about cells---namely, the locations, the sizes, and the associated image intensities---directly from the set of significant detail coefficients considered for path planning at a given iteration, and 8. The extension of the multi-resolution path planning scheme to include vehicle dynamical constraints using the aforementioned history-dependent costs approach. The future work includes an implementation of the proposed framework involving a discrete planner that solves classical planning problems more general than the single-query path planning problem considered thus far, and involving trajectory generation schemes for realistic vehicle dynamical models such as the bicycle model.

Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach

PubMed Central

Ferri, Gabriele; Cococcioni, Marco; Alvarez, Alberto

2015-01-01

This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called Aη, is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators. PMID:26712763

Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach.

PubMed

Ferri, Gabriele; Cococcioni, Marco; Alvarez, Alberto

2015-12-26

This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called A η , is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators.

Real-time maneuver optimization of space-based robots in a dynamic environment: Theory and on-orbit experiments

NASA Astrophysics Data System (ADS)

Chamitoff, Gregory E.; Saenz-Otero, Alvar; Katz, Jacob G.; Ulrich, Steve; Morrell, Benjamin J.; Gibbens, Peter W.

2018-01-01

This paper presents the development of a real-time path-planning optimization approach to controlling the motion of space-based robots. The algorithm is capable of planning three dimensional trajectories for a robot to navigate within complex surroundings that include numerous static and dynamic obstacles, path constraints and performance limitations. The methodology employs a unique transformation that enables rapid generation of feasible solutions for complex geometries, making it suitable for application to real-time operations and dynamic environments. This strategy was implemented on the Synchronized Position Hold Engage Reorient Experimental Satellite (SPHERES) test-bed on the International Space Station (ISS), and experimental testing was conducted onboard the ISS during Expedition 17 by the first author. Lessons learned from the on-orbit tests were used to further refine the algorithm for future implementations.

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

PubMed

Lin, Lanny; Goodrich, Michael A

2014-12-01

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

Hayabusa Re-Entry: Trajectory Analysis and Observation Mission Design

NASA Technical Reports Server (NTRS)

Cassell, Alan M.; Winter, Michael W.; Allen, Gary A.; Grinstead, Jay H.; Antimisiaris, Manny E.; Albers, James; Jenniskens, Peter

2011-01-01

On June 13th, 2010, the Hayabusa sample return capsule successfully re-entered Earth s atmosphere over the Woomera Prohibited Area in southern Australia in its quest to return fragments from the asteroid 1998 SF36 Itokawa . The sample return capsule entered at a super-orbital velocity of 12.04 km/sec (inertial), making it the second fastest human-made object to traverse the atmosphere. The NASA DC-8 airborne observatory was utilized as an instrument platform to record the luminous portion of the sample return capsule re-entry (60 sec) with a variety of on-board spectroscopic imaging instruments. The predicted sample return capsule s entry state information at 200 km altitude was propagated through the atmosphere to generate aerothermodynamic and trajectory data used for initial observation flight path design and planning. The DC- 8 flight path was designed by considering safety, optimal sample return capsule viewing geometry and aircraft capabilities in concert with key aerothermodynamic events along the predicted trajectory. Subsequent entry state vector updates provided by the Deep Space Network team at NASA s Jet Propulsion Laboratory were analyzed after the planned trajectory correction maneuvers to further refine the DC-8 observation flight path. Primary and alternate observation flight paths were generated during the mission planning phase which required coordination with Australian authorities for pre-mission approval. The final observation flight path was chosen based upon trade-offs between optimal viewing requirements, ground based observer locations (to facilitate post-flight trajectory reconstruction), predicted weather in the Woomera Prohibited Area and constraints imposed by flight path filing deadlines. To facilitate sample return capsule tracking by the instrument operators, a series of two racetrack flight path patterns were performed prior to the observation leg so the instruments could be pointed towards the region in the star background where the sample return capsule was expected to become visible. An overview of the design methodologies and trade-offs used in the Hayabusa re-entry observation campaign are presented.

Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task

NASA Astrophysics Data System (ADS)

Nazemizadeh, M.; Rahimi, H. N.; Amini Khoiy, K.

2012-03-01

This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange's principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.

A 2D chaotic path planning for mobile robots accomplishing boundary surveillance missions in adversarial conditions

NASA Astrophysics Data System (ADS)

Curiac, Daniel-Ioan; Volosencu, Constantin

2014-10-01

The path-planning algorithm represents a crucial issue for every autonomous mobile robot. In normal circumstances a patrol robot will compute an optimal path to ensure its task accomplishment, but in adversarial conditions the problem is getting more complicated. Here, the robot’s trajectory needs to be altered into a misleading and unpredictable path to cope with potential opponents. Chaotic systems provide the needed framework for obtaining unpredictable motion in all of the three basic robot surveillance missions: area, points of interests and boundary monitoring. Proficient approaches have been provided for the first two surveillance tasks, but for boundary patrol missions no method has been reported yet. This paper addresses the mentioned research gap by proposing an efficient method, based on chaotic dynamic of the Hénon system, to ensure unpredictable boundary patrol on any shape of chosen closed contour.

Real-time path planning and autonomous control for helicopter autorotation

NASA Astrophysics Data System (ADS)

Yomchinda, Thanan

Autorotation is a descending maneuver that can be used to recover helicopters in the event of total loss of engine power; however it is an extremely difficult and complex maneuver. The objective of this work is to develop a real-time system which provides full autonomous control for autorotation landing of helicopters. The work includes the development of an autorotation path planning method and integration of the path planner with a primary flight control system. The trajectory is divided into three parts: entry, descent and flare. Three different optimization algorithms are used to generate trajectories for each of these segments. The primary flight control is designed using a linear dynamic inversion control scheme, and a path following control law is developed to track the autorotation trajectories. Details of the path planning algorithm, trajectory following control law, and autonomous autorotation system implementation are presented. The integrated system is demonstrated in real-time high fidelity simulations. Results indicate feasibility of the capability of the algorithms to operate in real-time and of the integrated systems ability to provide safe autorotation landings. Preliminary simulations of autonomous autorotation on a small UAV are presented which will lead to a final hardware demonstration of the algorithms.

Dynamics of a Two-Link Vehicle in an L-Shaped Corridor Revisited

NASA Astrophysics Data System (ADS)

Antonyuk, E. Ya.; Zabuga, A. T.

2014-03-01

The kinematics of a two-link mobile robot with three steerable wheels moving in an L-shaped corridor is analyzed. A smooth (with continuous first derivative) path is designed maintaining the optimal maneuverability of the vehicle. The motion of the vehicle along this path is planned. Analytical expressions for the reactions at the contact of the wheels with the ground are given in the general case of motion. The radius of curvature of the programmed path is shown to have a strong influence on the reactions.

Path planning and energy management of solar-powered unmanned ground vehicles

NASA Astrophysics Data System (ADS)

Kaplan, Adam

Many of the applications pertinent to unmanned vehicles, such as environmental research and analysis, communications, and information-surveillance and reconnaissance, benefit from prolonged vehicle operation time. Conventional efforts to increase the operational time of electric-powered unmanned vehicles have traditionally focused on the design of energy-efficient components and the identification of energy efficient search patterns, while little attention has been paid to the vehicle's mission-level path plan and power management. This thesis explores the formulation and generation of integrated motion-plans and power-schedules for solar-panel equipped mobile robots operating under strict energy constraints, which cannot be effectively addressed through conventional motion planning algorithms. Transit problems are considered to design time-optimal paths using both Balkcom-Mason and Pseudo-Dubins curves. Additionally, a more complicated problem to generate mission plans for vehicles which must persistently travel between certain locations, similar to the traveling salesperson problem (TSP), is presented. A comparison between one of the common motion-planning algorithms and experimental results of the prescribed algorithms, made possible by use of a test environment and mobile robot designed and developed specifically for this research, are presented and discussed.

Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles

NASA Astrophysics Data System (ADS)

Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi

2012-09-01

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

TH-EF-BRB-04: 4π Dynamic Conformal Arc Therapy Dynamic Conformal Arc Therapy (DCAT) for SBRT

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

Chiu, T; Long, T; Tian, Z.

2016-06-15

Purpose: To develop an efficient and effective trajectory optimization methodology for 4π dynamic conformal arc treatment (4π DCAT) with synchronized gantry and couch motion; and to investigate potential clinical benefits for stereotactic body radiation therapy (SBRT) to breast, lung, liver and spine tumors. Methods: The entire optimization framework for 4π DCAT inverse planning consists of two parts: 1) integer programming algorithm and 2) particle swarm optimization (PSO) algorithm. The integer programming is designed to find an optimal solution for arc delivery trajectory with both couch and gantry rotation, while PSO minimize a non-convex objective function based on the selected trajectorymore » and dose-volume constraints. In this study, control point interaction is explicitly taken into account. Beam trajectory was modeled as a series of control points connected together to form a deliverable path. With linear treatment planning objectives, a mixed-integer program (MIP) was formulated. Under mild assumptions, the MIP is tractable. Assigning monitor units to control points along the path can be integrated into the model and done by PSO. The developed 4π DCAT inverse planning strategy is evaluated on SBRT cases and compared to clinically treated plans. Results: The resultant dose distribution of this technique was evaluated between 3D conformal treatment plan generated by Pinnacle treatment planning system and 4π DCAT on a lung SBRT patient case. Both plans share the same scale of MU, 3038 and 2822 correspondingly to 3D conformal plan and 4π DCAT. The mean doses for most of OARs were greatly reduced at 32% (cord), 70% (esophagus), 2.8% (lung) and 42.4% (stomach). Conclusion: Initial results in this study show the proposed 4π DCAT treatment technique can achieve better OAR sparing and lower MUs, which indicates that the developed technique is promising for high dose SBRT to reduce the risk of secondary cancer.« less

Enhancing The Science Collection Capability Of Nasas Lunar Reconnaissance Orbiter (LRO)

DTIC Science & Technology

2017-12-01

dog-leg maneuver. The optimal control concept can be used to automate maneuver design with bright object avoidance. 6.1 Introduction Attitude maneuver...plan can be executed and the science objectives satisfied, rapid slew maneuvers are developed using optimal control theory. A key challenge to the...rapid slew is meeting operational constraints, which are treated as path constraints in optimal control . It is shown that the slew time for a payload

Construction of Optimal-Path Maps for Homogeneous-Cost-Region Path-Planning Problems

DTIC Science & Technology

1989-09-01

of Artificial Inteligence , 9%,4. 24. Kirkpatrick, S., Gelatt Jr., C. D., and Vecchi, M. P., "Optinization by Sinmulated Ani- nealing", Science, Vol...studied in depth by researchers in such fields as artificial intelligence, robot;cs, and computa- tional geometry. Most methods require homogeneous...the results of the research. 10 U. L SLEVANT RESEARCH A. APPLICABLE CONCEPTS FROM ARTIFICIAL INTELLIGENCE 1. Search Methods One of the central

Image-based path planning for automated virtual colonoscopy navigation

NASA Astrophysics Data System (ADS)

Hong, Wei

2008-03-01

Virtual colonoscopy (VC) is a noninvasive method for colonic polyp screening, by reconstructing three-dimensional models of the colon using computerized tomography (CT). In virtual colonoscopy fly-through navigation, it is crucial to generate an optimal camera path for efficient clinical examination. In conventional methods, the centerline of the colon lumen is usually used as the camera path. In order to extract colon centerline, some time consuming pre-processing algorithms must be performed before the fly-through navigation, such as colon segmentation, distance transformation, or topological thinning. In this paper, we present an efficient image-based path planning algorithm for automated virtual colonoscopy fly-through navigation without the requirement of any pre-processing. Our algorithm only needs the physician to provide a seed point as the starting camera position using 2D axial CT images. A wide angle fisheye camera model is used to generate a depth image from the current camera position. Two types of navigational landmarks, safe regions and target regions are extracted from the depth images. Camera position and its corresponding view direction are then determined using these landmarks. The experimental results show that the generated paths are accurate and increase the user comfort during the fly-through navigation. Moreover, because of the efficiency of our path planning algorithm and rendering algorithm, our VC fly-through navigation system can still guarantee 30 FPS.

A portable back massage robot based on Traditional Chinese Medicine.

PubMed

Wang, Wendong; Liang, Chaohong; Zhang, Peng; Shi, Yikai

2018-05-30

A portable back massage robot which can complete the massage operations such as tapping, kneading and rolling was designed to improve the level of intelligence and massage effect. An efficient full covered path planning algorithm was put forward for a portable back massage robot to improve the coverage. Currently, massage robots has become one of important research focuses with the increasing requirements for healthcare. The massage robot is difficult to be widely accepted as there are problems of massage robot in control, structure, and coverage path planning. The 3D electromagnetic simulation model was established to optimize electromagnetic force. By analyzing the Traditional Chinese Medicine massage operation and the demands, the path planning algorithm models were established. The experimental platform of the massage robot was built. The simulation results show presented path planning algorithm is suitable for back massage, which ensures that the massage robot traverse the entire back area with improved massage coverage. The tested results show that the massage effect is best when the duty cycle is in the range of 1/8 to 1/2, and the massage force increases with the increase of the input voltage. The massage robot eventually achieved the desired massage effect, and the proposed efficient algorithm can effectively improve the coverage and promote the massage effect.

Game theory and risk-based leveed river system planning with noncooperation

NASA Astrophysics Data System (ADS)

Hui, Rui; Lund, Jay R.; Madani, Kaveh

2016-01-01

Optimal risk-based levee designs are usually developed for economic efficiency. However, in river systems with multiple levees, the planning and maintenance of different levees are controlled by different agencies or groups. For example, along many rivers, levees on opposite riverbanks constitute a simple leveed river system with each levee designed and controlled separately. Collaborative planning of the two levees can be economically optimal for the whole system. Independent and self-interested landholders on opposite riversides often are willing to separately determine their individual optimal levee plans, resulting in a less efficient leveed river system from an overall society-wide perspective (the tragedy of commons). We apply game theory to simple leveed river system planning where landholders on each riverside independently determine their optimal risk-based levee plans. Outcomes from noncooperative games are analyzed and compared with the overall economically optimal outcome, which minimizes net flood cost system-wide. The system-wide economically optimal solution generally transfers residual flood risk to the lower-valued side of the river, but is often impractical without compensating for flood risk transfer to improve outcomes for all individuals involved. Such compensation can be determined and implemented with landholders' agreements on collaboration to develop an economically optimal plan. By examining iterative multiple-shot noncooperative games with reversible and irreversible decisions, the costs of myopia for the future in making levee planning decisions show the significance of considering the externalities and evolution path of dynamic water resource problems to improve decision-making.

Autonomous terrain characterization and modelling for dynamic control of unmanned vehicles

NASA Technical Reports Server (NTRS)

Talukder, A.; Manduchi, R.; Castano, R.; Owens, K.; Matthies, L.; Castano, A.; Hogg, R.

2002-01-01

This end-to-end obstacle negotiation system is envisioned to be useful in optimized path planning and vehicle navigation in terrain conditions cluttered with vegetation, bushes, rocks, etc. Results on natural terrain with various natural materials are presented.

Design of an advanced flight planning system

NASA Technical Reports Server (NTRS)

Sorensen, J. A.; Goka, T.

1985-01-01

The demand for both fuel conservation and four-dimensional traffic management require that the preflight planning process be designed to account for advances in airborne flight management and weather forecasting. The steps and issues in designing such an advanced flight planning system are presented. Focus is placed on the different optimization options for generating the three-dimensional reference path. For the cruise phase, one can use predefined jet routes, direct routes based on a network of evenly spaced grid points, or a network where the grid points are existing navaid locations. Each choice has its own problem in determining an optimum solution. Finding the reference path is further complicated by choice of cruise altitude levels, use of a time-varying weather field, and requiring a fixed time-of-arrival (four-dimensional problem).

Simulation Of Research And Development Projects

NASA Technical Reports Server (NTRS)

Miles, Ralph F.

1987-01-01

Measures of preference for alternative project plans calculated. Simulation of Research and Development Projects (SIMRAND) program aids in optimal allocation of research and development resources needed to achieve project goals. Models system subsets or project tasks as various network paths to final goal. Each path described in terms of such task variables as cost per hour, cost per unit, and availability of resources. Uncertainty incorporated by treating task variables as probabilistic random variables. Written in Microsoft FORTRAN 77.

A Stochastic Approach to Path Planning in the Weighted-Region Problem

DTIC Science & Technology

1991-03-01

polynomial time. However, the polyhedrons in this three-dimensional obstacle-avoidance problem are all obstacles (i.e. travel is not permitted within...them). Therefore, optimal paths tend to avoid their vertices, and settle into closest approach tangents across polyhedron edges. So, in a sense...intersection update map database with new vertex for this edge 3. IF (C1 > D) and (C2 > D) THEN edge intersects ellipse at two points OR edge is

The force control and path planning of electromagnetic induction-based massage robot.

PubMed

Wang, Wendong; Zhang, Lei; Li, Jinzhe; Yuan, Xiaoqing; Shi, Yikai; Jiang, Qinqin; He, Lijing

2017-07-20

Massage robot is considered as an effective physiological treatment to relieve fatigue, improve blood circulation, relax muscle tone, etc. The simple massage equipment quickly spread into market due to low cost, but they are not widely accepted due to restricted massage function. Complicated structure and high cost caused difficulties for developing multi-function massage equipment. This paper presents a novel massage robot which can achieve tapping, rolling, kneading and other massage operations, and proposes an improved reciprocating path planning algorithm to improve massage effect. The number of coil turns, the coil current and the distance between massage head and yoke were chosen to investigate the influence on massage force by finite element method. The control system model of the wheeled massage robot was established, including control subsystem of the motor, path algorithm control subsystem, parameter module of the massage robot and virtual reality interface module. The improved reciprocating path planning algorithm was proposed to improve regional coverage rate and massage effect. The influence caused by coil current, the number of coil turns and the distance between massage head and yoke were simulated in Maxwell. It indicated that coil current has more important influence compared to the other two factors. The path planning simulation of the massage robot was completed in Matlab, and the results show that the improved reciprocating path planning algorithm achieved higher coverage rate than the traditional algorithm. With the analysis of simulation results, it can be concluded that the number of coil turns and the distance between the moving iron core and the yoke could be determined prior to coil current, and the force can be controllable by optimizing structure parameters of massage head and adjusting coil current. Meanwhile, it demonstrates that the proposed algorithm could effectively improve path coverage rate during massage operations, therefore the massage effect can be improved.

Conception et analyse d'un systeme d'optimisation de plans de vol pour les avions

NASA Astrophysics Data System (ADS)

Maazoun, Wissem

The main objective of this thesis is to develop an optimization method for the preparation of flight plans for aircrafts. The flight plan minimizes all costs associated with the flight. We determine an optimal path for an airplane from a departure airport to a destination airport. The optimal path minimizes the sum of all costs, i.e. the cost of fuel added to the cost of time (wages, rental of the aircraft, arrival delays, etc.). The optimal trajectory is obtained by considering all possible trajectories on a 3D graph (longitude, latitude and altitude) where the altitude levels are separated by 2,000 feet, and by applying a shortest path algorithm. The main task was to accurately compute fuel consumption on each edge of the graph, making sure that each arc has a minimal cost and is covered in a realistic way from the point of view of control, i.e. in accordance with the rules of navigation. To compute the cost of an arc, we take into account weather conditions (temperature, pressure, wind components, etc.). The optimization of each arc is done via the evaluation of an optimum speed that takes all costs into account. Each arc of the graph typically includes several sub-phases of the flight, e.g. altitude change, speed change, and constant speed and altitude. In the initial climb and the final descent phases, the costs are determined by considering altitude changes at constant CAS (Calibrated Air Speed) or constant Mach number. CAS and Mach number are adjusted to minimize cost. The aerodynamic model used is the one proposed by Eurocontrol, which uses the BADA (Base of Aircraft Data) tables. This model is based on the total energy equation that determines the instantaneous fuel consumption. Calculations on each arc are done by solving a system of differential equations that systematically takes all costs into account. To compute the cost of an arc, we must know the time to go through it, which is generally unknown. To have well-posed boundary conditions, we use the horizontal displacement as the independent variable of the system of differential equations. We consider the velocity components of the wind in a 3D system of coordinates to compute the instantaneous ground speed of the aircraft. To consider the cost of time, we use the cost index. The cost of an arc depends on the aircraft mass at the beginning of this arc, and this mass depends on the path. As we consider all possible paths, the cost of an arc must be computed for each trajectory to which it belongs. For a long-distance flight, the number of arcs to be considered in the graph is large and therefore the cost of an arc is typically computed many times. Our algorithm computes the costs of one million arcs in seconds while having a high accuracy. The determination of the optimal trajectory can therefore be done in a short time. To get the optimal path, the mass of the aircraft at the departure point must also be optimal. It is therefore necessary to know the optimal amount of fuel for the journey. The aircraft mass is known only at the arrival point. This mass is the mass of the aircraft including passengers, cargo and reserve fuel mass. The optimal path is determined by calculating backwards, i.e. from the arrival point to the departure point. For the determination of the optimal trajectory, we use an elliptical grid that has focal points at the departure and arrival points. The use of this grid is essential for the construction of a direct and acyclic graph. We use the Bellman-Ford algorithm on a DAG to determine the shortest path. This algorithm is easy to implement and results in short computation times. Our algorithm computes an optimal trajectory with an optimal cost for each arc. Altitude changes are done optimally with respect to the mass of the aircraft and the cost of time. Our algorithm gives the mass, speed, altitude and total cost at any point of the trajectory as well as the optimal profiles of climb and descent. A prototype has been implemented in C. We made simulations of all types of possible arcs and of several complete trajectories to illustrate the behaviour of the algorithm.

Optimized path planning for soft tissue resection via laser vaporization

NASA Astrophysics Data System (ADS)

Ross, Weston; Cornwell, Neil; Tucker, Matthew; Mann, Brian; Codd, Patrick

2018-02-01

Robotic and robotic-assisted surgeries are becoming more prevalent with the promise of improving surgical outcomes through increased precision, reduced operating times, and minimally invasive procedures. The handheld laser scalpel in neurosurgery has been shown to provide a more gentle approach to tissue manipulation on or near critical structures over classical tooling, though difficulties of control have prevented large scale adoption of the tool. This paper presents a novel approach to generating a cutting path for the volumetric resection of tissue using a computer-guided laser scalpel. A soft tissue ablation simulator is developed and used in conjunction with an optimization routine to select parameters which maximize the total resection of target tissue while minimizing the damage to surrounding tissue. The simulator predicts the ablative properties of tissue from an interrogation cut for tuning and simulates the removal of a tumorous tissue embedded on the surface of healthy tissue using a laser scalpel. We demonstrate the ability to control depth and smoothness of cut using genetic algorithms to optimize the ablation parameters and cutting path. The laser power level, cutting rate and spacing between cuts are optimized over multiple surface cuts to achieve the desired resection volumes.

MODAS Validation in Littoral Areas Using GRASP

DTIC Science & Technology

2002-09-30

result (4 hr) is guiding new work on calculation efficiency. Figure 4. Near-optimal coordinated passive search plan against a complex transitor ... Transitor tracks form a river of roughly parallel potential paths. The two searcher tracks criss- cross this river like shoe lacings over much of

Planning nonlinear access paths for temporal bone surgery.

PubMed

Fauser, Johannes; Sakas, Georgios; Mukhopadhyay, Anirban

2018-05-01

Interventions at the otobasis operate in the narrow region of the temporal bone where several highly sensitive organs define obstacles with minimal clearance for surgical instruments. Nonlinear trajectories for potential minimally invasive interventions can provide larger distances to risk structures and optimized orientations of surgical instruments, thus improving clinical outcomes when compared to existing linear approaches. In this paper, we present fast and accurate planning methods for such nonlinear access paths. We define a specific motion planning problem in [Formula: see text] with notable constraints in computation time and goal pose that reflect the requirements of temporal bone surgery. We then present [Formula: see text]-RRT-Connect: two suitable motion planners based on bidirectional Rapidly exploring Random Tree (RRT) to solve this problem efficiently. The benefits of [Formula: see text]-RRT-Connect are demonstrated on real CT data of patients. Their general performance is shown on a large set of realistic synthetic anatomies. We also show that these new algorithms outperform state-of-the-art methods based on circular arcs or Bézier-Splines when applied to this specific problem. With this work, we demonstrate that preoperative and intra-operative planning of nonlinear access paths is possible for minimally invasive surgeries at the otobasis.

Serial robot for the trajectory optimization and error compensation of TMT mask exchange system

NASA Astrophysics Data System (ADS)

Wang, Jianping; Zhang, Feifan; Zhou, Zengxiang; Zhai, Chao

2015-10-01

Mask exchange system is the main part of Multi-Object Broadband Imaging Echellette (MOBIE) on the Thirty Meter Telescope (TMT). According to the conception of the TMT mask exchange system, the pre-design was introduced in the paper which was based on IRB 140 robot. The stiffness model of IRB 140 in SolidWorks was analyzed under different gravity vectors for further error compensation. In order to find the right location and path planning, the robot and the mask cassette model was imported into MOBIE model to perform different schemes simulation. And obtained the initial installation position and routing. Based on these initial parameters, IRB 140 robot was operated to simulate the path and estimate the mask exchange time. Meanwhile, MATLAB and ADAMS software were used to perform simulation analysis and optimize the route to acquire the kinematics parameters and compare with the experiment results. After simulation and experimental research mentioned in the paper, the theoretical reference was acquired which could high efficient improve the structure of the mask exchange system parameters optimization of the path and precision of the robot position.

Robotic Online Path Planning on Point Cloud.

PubMed

Liu, Ming

2016-05-01

This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.

Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Walter, Ulrich

2015-07-01

This paper investigates the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in free-floating mode. Due to the path dependent dynamic singularities, the volume of available workspace of the space robot is limited and enormous joint velocities are required when such singularities are met. In order to overcome this effect, the direct kinematics equations in conjunction with PSO are employed for trajectory planning of free-floating space robot. The joint trajectories are parametrized with the Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) redundant manipulator mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Path planning and parameter optimization of uniform removal in active feed polishing

NASA Astrophysics Data System (ADS)

Liu, Jian; Wang, Shaozhi; Zhang, Chunlei; Zhang, Linghua; Chen, Huanan

2015-06-01

A high-quality ultrasmooth surface is demanded in short-wave optical systems. However, the existing polishing methods have difficulties meeting the requirement on spherical or aspheric surfaces. As a new kind of small tool polishing method, active feed polishing (AFP) could attain a surface roughness of less than 0.3 nm (RMS) on spherical elements, although AFP may magnify the residual figure error or mid-frequency error. The purpose of this work is to propose an effective algorithm to realize uniform removal of the surface in the processing. At first, the principle of the AFP and the mechanism of the polishing machine are introduced. In order to maintain the processed figure error, a variable pitch spiral path planning algorithm and the dwell time-solving model are proposed. For suppressing the possible mid-frequency error, the uniformity of the synthesis tool path, which is generated by an arbitrary point at the polishing tool bottom, is analyzed and evaluated, and the angular velocity ratio of the tool spinning motion to the revolution motion is optimized. Finally, an experiment is conducted on a convex spherical surface and an ultrasmooth surface is finally acquired. In conclusion, a high-quality ultrasmooth surface can be successfully obtained with little degradation of the figure and mid-frequency errors by the algorithm.

Open source Modeling and optimization tools for Planning

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

Peles, S.

Open source modeling and optimization tools for planning The existing tools and software used for planning and analysis in California are either expensive, difficult to use, or not generally accessible to a large number of participants. These limitations restrict the availability of participants for larger scale energy and grid studies in the state. The proposed initiative would build upon federal and state investments in open source software, and create and improve open source tools for use in the state planning and analysis activities. Computational analysis and simulation frameworks in development at national labs and universities can be brought forward tomore » complement existing tools. An open source platform would provide a path for novel techniques and strategies to be brought into the larger community and reviewed by a broad set of stakeholders.« less

A novel method for trajectory planning of cooperative mobile manipulators.

PubMed

Bolandi, Hossein; Ehyaei, Amir Farhad

2011-01-01

We have designed a two-stage scheme to consider the trajectory planning problem of two mobile manipulators for cooperative transportation of a rigid body in the presence of static obstacles. In the first stage, with regard to the static obstacles, we develop a method that searches the workspace for the shortest possible path between the start and goal configurations, by constructing a graph on a portion of the configuration space that satisfies the collision and closure constraints. The final stage is to calculate a sequence of time-optimal trajectories to go between the consecutive points of the path, with regard to the nonholonomic constraints and the maximum allowed joint accelerations. This approach allows geometric constraints such as joint limits and closed-chain constraints, along with differential constraints such as nonholonomic velocity constraints and acceleration limits, to be incorporated into the planning scheme. The simulation results illustrate the effectiveness of the proposed method.

A Novel Method for Trajectory Planning of Cooperative Mobile Manipulators

PubMed Central

Bolandi, Hossein; Ehyaei, Amir Farhad

2011-01-01

We have designed a two-stage scheme to consider the trajectory planning problem of two mobile manipulators for cooperative transportation of a rigid body in the presence of static obstacles. In the first stage, with regard to the static obstacles, we develop a method that searches the workspace for the shortest possible path between the start and goal configurations, by constructing a graph on a portion of the configuration space that satisfies the collision and closure constraints. The final stage is to calculate a sequence of time-optimal trajectories to go between the consecutive points of the path, with regard to the nonholonomic constraints and the maximum allowed joint accelerations. This approach allows geometric constraints such as joint limits and closed-chain constraints, along with differential constraints such as nonholonomic velocity constraints and acceleration limits, to be incorporated into the planning scheme. The simulation results illustrate the effectiveness of the proposed method. PMID:22606656

Path planning on satellite images for unmanned surface vehicles

NASA Astrophysics Data System (ADS)

Yang, Joe-Ming; Tseng, Chien-Ming; Tseng, P. S.

2015-01-01

In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs) and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle A* algorithm (FAA*), an advanced A* algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV.

Time optimal paths for high speed maneuvering

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

Reister, D.B.; Lenhart, S.M.

1993-01-01

Recent theoretical results have completely solved the problem of determining the minimum length path for a vehicle with a minimum turning radius moving from an initial configuration to a final configuration. Time optimal paths for a constant speed vehicle are a subset of the minimum length paths. This paper uses the Pontryagin maximum principle to find time optimal paths for a constant speed vehicle. The time optimal paths consist of sequences of axes of circles and straight lines. The maximum principle introduces concepts (dual variables, bang-bang solutions, singular solutions, and transversality conditions) that provide important insight into the nature ofmore » the time optimal paths. We explore the properties of the optimal paths and present some experimental results for a mobile robot following an optimal path.« less

Optimal strategies for throwing accurately

PubMed Central

2017-01-01

The accuracy of throwing in games and sports is governed by how errors in planning and initial conditions are propagated by the dynamics of the projectile. In the simplest setting, the projectile path is typically described by a deterministic parabolic trajectory which has the potential to amplify noisy launch conditions. By analysing how parabolic trajectories propagate errors, we show how to devise optimal strategies for a throwing task demanding accuracy. Our calculations explain observed speed–accuracy trade-offs, preferred throwing style of overarm versus underarm, and strategies for games such as dart throwing, despite having left out most biological complexities. As our criteria for optimal performance depend on the target location, shape and the level of uncertainty in planning, they also naturally suggest an iterative scheme to learn throwing strategies by trial and error. PMID:28484641

Optimal strategies for throwing accurately

NASA Astrophysics Data System (ADS)

Venkadesan, M.; Mahadevan, L.

2017-04-01

The accuracy of throwing in games and sports is governed by how errors in planning and initial conditions are propagated by the dynamics of the projectile. In the simplest setting, the projectile path is typically described by a deterministic parabolic trajectory which has the potential to amplify noisy launch conditions. By analysing how parabolic trajectories propagate errors, we show how to devise optimal strategies for a throwing task demanding accuracy. Our calculations explain observed speed-accuracy trade-offs, preferred throwing style of overarm versus underarm, and strategies for games such as dart throwing, despite having left out most biological complexities. As our criteria for optimal performance depend on the target location, shape and the level of uncertainty in planning, they also naturally suggest an iterative scheme to learn throwing strategies by trial and error.

Optimal Path Determination for Flying Vehicle to Search an Object

NASA Astrophysics Data System (ADS)

Heru Tjahjana, R.; Heri Soelistyo U, R.; Ratnasari, L.; Irawanto, B.

2018-01-01

In this paper, a method to determine optimal path for flying vehicle to search an object is proposed. Background of the paper is controlling air vehicle to search an object. Optimal path determination is one of the most popular problem in optimization. This paper describe model of control design for a flying vehicle to search an object, and focus on the optimal path that used to search an object. In this paper, optimal control model is used to control flying vehicle to make the vehicle move in optimal path. If the vehicle move in optimal path, then the path to reach the searched object also optimal. The cost Functional is one of the most important things in optimal control design, in this paper the cost functional make the air vehicle can move as soon as possible to reach the object. The axis reference of flying vehicle uses N-E-D (North-East-Down) coordinate system. The result of this paper are the theorems which say that the cost functional make the control optimal and make the vehicle move in optimal path are proved analytically. The other result of this paper also shows the cost functional which used is convex. The convexity of the cost functional is use for guarantee the existence of optimal control. This paper also expose some simulations to show an optimal path for flying vehicle to search an object. The optimization method which used to find the optimal control and optimal path vehicle in this paper is Pontryagin Minimum Principle.

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Smith, E. V.; Yerazunis, S. W.

1973-01-01

Problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars are reported. Problem areas include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis, terrain modeling and path selection; and chemical analysis of specimens. These tasks are summarized: vehicle model design, mathematical model of vehicle dynamics, experimental vehicle dynamics, obstacle negotiation, electrochemical controls, remote control, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, and chromatograph model evaluation and improvement.

Optimal UAV Path Planning for Tracking a Moving Ground Vehicle with a Gimbaled Camera

DTIC Science & Technology

2014-03-27

micro SD card slot to record all video taken at 1080P resolution. This feature allows the team to record the high definition video taken by the...Inequality constraints 64 h=[]; %Equality constraints 104 Bibliography 1. “ DIY Drones: Official ArduPlane Repository”, 2013. URL https://code

Algorithms for Heterogeneous, Multiple Depot, Multiple Unmanned Vehicle Path Planning Problems

DOE PAGES

Sundar, Kaarthik; Rathinam, Sivakumar

2016-12-26

Unmanned vehicles, both aerial and ground, are being used in several monitoring applications to collect data from a set of targets. This article addresses a problem where a group of heterogeneous aerial or ground vehicles with different motion constraints located at distinct depots visit a set of targets. The vehicles also may be equipped with different sensors, and therefore, a target may not be visited by any vehicle. The objective is to find an optimal path for each vehicle starting and ending at its respective depot such that each target is visited at least once by some vehicle, the vehicle–targetmore » constraints are satisfied, and the sum of the length of the paths for all the vehicles is minimized. Two variants of this problem are formulated (one for ground vehicles and another for aerial vehicles) as mixed-integer linear programs and a branchand- cut algorithm is developed to compute an optimal solution to each of the variants. Computational results show that optimal solutions for problems involving 100 targets and 5 vehicles can be obtained within 300 seconds on average, further corroborating the effectiveness of the proposed approach.« less

Radial polar histogram: obstacle avoidance and path planning for robotic cognition and motion control

NASA Astrophysics Data System (ADS)

Wang, Po-Jen; Keyawa, Nicholas R.; Euler, Craig

2012-01-01

In order to achieve highly accurate motion control and path planning for a mobile robot, an obstacle avoidance algorithm that provided a desired instantaneous turning radius and velocity was generated. This type of obstacle avoidance algorithm, which has been implemented in California State University Northridge's Intelligent Ground Vehicle (IGV), is known as Radial Polar Histogram (RPH). The RPH algorithm utilizes raw data in the form of a polar histogram that is read from a Laser Range Finder (LRF) and a camera. A desired open block is determined from the raw data utilizing a navigational heading and an elliptical approximation. The left and right most radii are determined from the calculated edges of the open block and provide the range of possible radial paths the IGV can travel through. In addition, the calculated obstacle edge positions allow the IGV to recognize complex obstacle arrangements and to slow down accordingly. A radial path optimization function calculates the best radial path between the left and right most radii and is sent to motion control for speed determination. Overall, the RPH algorithm allows the IGV to autonomously travel at average speeds of 3mph while avoiding all obstacles, with a processing time of approximately 10ms.

Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Fang, Jing; Yuan, Jianping

2018-03-01

The existence of the path dependent dynamic singularities limits the volume of available workspace of free-floating space robot and induces enormous joint velocities when such singularities are met. In order to overcome this demerit, this paper presents an optimal joint trajectory planning method using forward kinematics equations of free-floating space robot, while joint motion laws are delineated with application of the concept of reaction null-space. Bézier curve, in conjunction with the null-space column vectors, are applied to describe the joint trajectories. Considering the forward kinematics equations of the free-floating space robot, the trajectory planning issue is consequently transferred to an optimization issue while the control points to construct the Bézier curve are the design variables. A constrained differential evolution (DE) scheme with premature handling strategy is implemented to find the optimal solution of the design variables while specific objectives and imposed constraints are satisfied. Differ from traditional methods, we synthesize null-space and specialized curve to provide a novel viewpoint for trajectory planning of free-floating space robot. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a free-floating spacecraft and demonstrate the feasibility and effectiveness of the proposed method.

Research on optimal path planning algorithm of task-oriented optical remote sensing satellites

NASA Astrophysics Data System (ADS)

Liu, Yunhe; Xu, Shengli; Liu, Fengjing; Yuan, Jingpeng

2015-08-01

GEO task-oriented optical remote sensing satellite, is very suitable for long-term continuous monitoring and quick access to imaging. With the development of high resolution optical payload technology and satellite attitude control technology, GEO optical remote sensing satellites will become an important developing trend for aerospace remote sensing satellite in the near future. In the paper, we focused on GEO optical remote sensing satellite plane array stare imaging characteristics and real-time leading mission of earth observation mode, targeted on satisfying needs of the user with the minimum cost of maneuver, and put forward the optimal path planning algorithm centered on transformation from geographic coordinate space to Field of plane, and finally reduced the burden of the control system. In this algorithm, bounded irregular closed area on the ground would be transformed based on coordinate transformation relations in to the reference plane for field of the satellite payload, and then using the branch and bound method to search for feasible solutions, cutting off the non-feasible solution in the solution space based on pruning strategy; and finally trimming some suboptimal feasible solutions based on the optimization index until a feasible solution for the global optimum. Simulation and visualization presentation software testing results verified the feasibility and effectiveness of the strategy.

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

Reister, D.B.; Lenhart, S.M.

Recent theoretical results have completely solved the problem of determining the minimum length path for a vehicle with a minimum turning radius moving from an initial configuration to a final configuration. Time optimal paths for a constant speed vehicle are a subset of the minimum length paths. This paper uses the Pontryagin maximum principle to find time optimal paths for a constant speed vehicle. The time optimal paths consist of sequences of axes of circles and straight lines. The maximum principle introduces concepts (dual variables, bang-bang solutions, singular solutions, and transversality conditions) that provide important insight into the nature ofmore » the time optimal paths. We explore the properties of the optimal paths and present some experimental results for a mobile robot following an optimal path.« less

Effect of High-speed Milling tool path strategies on the surface roughness of Stavax ESR mold insert machining

NASA Astrophysics Data System (ADS)

Mebrahitom, A.; Rizuan, D.; Azmir, M.; Nassif, M.

2016-02-01

High speed milling is one of the recent technologies used to produce mould inserts due to the need for high surface finish. It is a faster machining process where it uses a small side step and a small down step combined with very high spindle speed and feed rate. In order to effectively use the HSM capabilities, optimizing the tool path strategies and machining parameters is an important issue. In this paper, six different tool path strategies have been investigated on the surface finish and machining time of a rectangular cavities of ESR Stavax material. CAD/CAM application of CATIA V5 machining module for pocket milling of the cavities was used for process planning.

Spatial abstraction for autonomous robot navigation.

PubMed

Epstein, Susan L; Aroor, Anoop; Evanusa, Matthew; Sklar, Elizabeth I; Parsons, Simon

2015-09-01

Optimal navigation for a simulated robot relies on a detailed map and explicit path planning, an approach problematic for real-world robots that are subject to noise and error. This paper reports on autonomous robots that rely on local spatial perception, learning, and commonsense rationales instead. Despite realistic actuator error, learned spatial abstractions form a model that supports effective travel.

A Heuristic Algorithm for Planning Personalized Learning Paths for Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Kuo, Fan-Ray; Yin, Peng-Yeng; Chuang, Kuo-Hsien

2010-01-01

In a context-aware ubiquitous learning environment, learning systems can detect students' learning behaviors in the real-world with the help of context-aware (sensor) technology; that is, students can be guided to observe or operate real-world objects with personalized support from the digital world. In this study, an optimization problem that…

Ecological criteria, participant preferences and location models: A GIS approach toward ATV trail planning

Treesearch

Stephanie A. Snyder; Jay H. Whitmore; Ingrid E. Schneider; Dennis R. Becker

2008-01-01

This paper presents a geographic information system (GIS)-based method for recreational trail location for all-terrain vehicles (ATVs) which considers environmental factors, as well as rider preferences for trail attributes. The method utilizes the Least-Cost Path algorithm within a GIS framework to optimize trail location. The trail location algorithm considered trail...

Analysis and design of a capsule landing system and surface vehicle control system for Mars exporation

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Smith, E. J.; Yerazunis, S. W.

1972-01-01

The problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars were investigated. Problem areas receiving attention include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis; navigation, terrain modeling and path selection; and chemical analysis of specimens. The following specific tasks were studied: vehicle model design, mathematical modeling of dynamic vehicle, experimental vehicle dynamics, obstacle negotiation, electromechanical controls, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, chromatograph model evaluation and improvement and transport parameter evaluation.

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Smith, E. J.; Yerazunis, S. W.

1972-01-01

Investigation of problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars has been undertaken. Problem areas receiving attention include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis; terrain modeling and path selection; and chemical analysis of specimens. The following specific tasks have been under study: vehicle model design, mathematical modeling of a dynamic vehicle, experimental vehicle dynamics, obstacle negotiation, electromechanical controls, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer sybsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, chromatograph model evaluation and improvement.

To connect or not to connect? Modelling the optimal degree of centralisation for wastewater infrastructures.

PubMed

Eggimann, Sven; Truffer, Bernhard; Maurer, Max

2015-11-01

The strong reliance of most utility services on centralised network infrastructures is becoming increasingly challenged by new technological advances in decentralised alternatives. However, not enough effort has been made to develop planning tools designed to address the implications of these new opportunities and to determine the optimal degree of centralisation of these infrastructures. We introduce a planning tool for sustainable network infrastructure planning (SNIP), a two-step techno-economic heuristic modelling approach based on shortest path-finding and hierarchical-agglomerative clustering algorithms to determine the optimal degree of centralisation in the field of wastewater management. This SNIP model optimises the distribution of wastewater treatment plants and the sewer network outlay relative to several cost and sewer-design parameters. Moreover, it allows us to construct alternative optimal wastewater system designs taking into account topography, economies of scale as well as the full size range of wastewater treatment plants. We quantify and confirm that the optimal degree of centralisation decreases with increasing terrain complexity and settlement dispersion while showing that the effect of the latter exceeds that of topography. Case study results for a Swiss community indicate that the calculated optimal degree of centralisation is substantially lower than the current level. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sensorimotor adaptation of point-to-point arm movements after spaceflight: the role of internal representation of gravity force in trajectory planning.

PubMed

Gaveau, Jérémie; Paizis, Christos; Berret, Bastien; Pozzo, Thierry; Papaxanthis, Charalambos

2011-08-01

After an exposure to weightlessness, the central nervous system operates under new dynamic and sensory contexts. To find optimal solutions for rapid adaptation, cosmonauts have to decide whether parameters from the world or their body have changed and to estimate their properties. Here, we investigated sensorimotor adaptation after a spaceflight of 10 days. Five cosmonauts performed forward point-to-point arm movements in the sagittal plane 40 days before and 24 and 72 h after the spaceflight. We found that, whereas the shape of hand velocity profiles remained unaffected after the spaceflight, hand path curvature significantly increased 1 day after landing and returned to the preflight level on the third day. Control experiments, carried out by 10 subjects under normal gravity conditions, showed that loading the arm with varying loads (from 0.3 to 1.350 kg) did not affect path curvature. Therefore, changes in path curvature after spaceflight cannot be the outcome of a control process based on the subjective feeling that arm inertia was increased. By performing optimal control simulations, we found that arm kinematics after exposure to microgravity corresponded to a planning process that overestimated the gravity level and optimized movements in a hypergravity environment (∼1.4 g). With time and practice, the sensorimotor system was recalibrated to Earth's gravity conditions, and cosmonauts progressively generated accurate estimations of the body state, gravity level, and sensory consequences of the motor commands (72 h). These observations provide novel insights into how the central nervous system evaluates body (inertia) and environmental (gravity) states during sensorimotor adaptation of point-to-point arm movements after an exposure to weightlessness.

An improved reaction path optimization method using a chain of conformations

NASA Astrophysics Data System (ADS)

Asada, Toshio; Sawada, Nozomi; Nishikawa, Takuya; Koseki, Shiro

2018-05-01

The efficient fast path optimization (FPO) method is proposed to optimize the reaction paths on energy surfaces by using chains of conformations. No artificial spring force is used in the FPO method to ensure the equal spacing of adjacent conformations. The FPO method is applied to optimize the reaction path on two model potential surfaces. The use of this method enabled the optimization of the reaction paths with a drastically reduced number of optimization cycles for both potentials. It was also successfully utilized to define the MEP of the isomerization of the glycine molecule in water by FPO method.

WE-AB-BRA-09: Sensitivity of Plan Re-Optimization to Errors in Deformable Image Registration in Online Adaptive Image-Guided Radiation Therapy

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

McClain, B; Olsen, J; Green, O

2015-06-15

Purpose: Online adaptive therapy (ART) relies on auto-contouring using deformable image registration (DIR). DIR’s inherent uncertainties require user intervention and manual edits while the patient is on the table. We investigated the dosimetric impact of DIR errors on the quality of re-optimized plans, and used the findings to establish regions for focusing manual edits to where DIR errors can Result in clinically relevant dose differences. Methods: Our clinical implementation of online adaptive MR-IGRT involves using DIR to transfer contours from CT to daily MR, followed by a physicians’ edits. The plan is then re-optimized to meet the organs at riskmore » (OARs) constraints. Re-optimized abdomen and pelvis plans generated based on physician edited OARs were selected as the baseline for evaluation. Plans were then re-optimized on auto-deformed contours with manual edits limited to pre-defined uniform rings (0 to 5cm) around the PTV. A 0cm ring indicates that the auto-deformed OARs were used without editing. The magnitude of the variations caused by the non-deterministic optimizer was quantified by repeat re-optimizations on the same geometry to determine the mean and standard deviation (STD). For each re-optimized plan, various volumetric parameters for the PTV, the OARs were extracted along with DVH and isodose evaluation. A plan was deemed acceptable if the variation from the baseline plan was within one STD. Results: Initial results show that for abdomen and pancreas cases, a minimum of 5cm margin around the PTV is required for contour corrections, while for pelvic and liver cases a 2–3 cm margin is sufficient. Conclusion: Focusing manual contour edits to regions of dosimetric relevance can reduce contouring time in the online ART process while maintaining a clinically comparable plan. Future work will further refine the contouring region by evaluating the path along the beams, dose gradients near the target and OAR dose metrics.« less

Path planning in uncertain flow fields using ensemble method

NASA Astrophysics Data System (ADS)

Wang, Tong; Le Maître, Olivier P.; Hoteit, Ibrahim; Knio, Omar M.

2016-10-01

An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.

Case report highlighting how wound path identification on CT can help identify organ damage in abdominal blast injuries.

PubMed

Fischer, Tatjana V; Folio, Les R; Backus, Christopher E; Bunger, Rolf

2012-01-01

Penetrating trauma is frequently encountered in forward deployed military combat hospitals. Abdominal blast injuries represent nearly 11% of combat injuries, and multiplanar computed tomography imaging is optimal for injury assessment and surgical planning. We describe a multiplanar approach to assessment of blast and ballistic injuries, which allows for more expeditious detection of missile tracts and damage caused along the path. Precise delineation of the trajectory path and localization of retained fragments enables time-saving and detailed evaluation of associated tissue and vascular injury. For consistent and reproducible documentation of fragment locations in the body, we propose a localization scheme based on Cartesian coordinates to report 3-dimensional locations of fragments and demonstrating the application in three cases of abdominal blast injury.

Lagrangian coherent structure assisted path planning for transoceanic autonomous underwater vehicle missions.

PubMed

Ramos, A G; García-Garrido, V J; Mancho, A M; Wiggins, S; Coca, J; Glenn, S; Schofield, O; Kohut, J; Aragon, D; Kerfoot, J; Haskins, T; Miles, T; Haldeman, C; Strandskov, N; Allsup, B; Jones, C; Shapiro, J

2018-03-15

Transoceanic Gliders are Autonomous Underwater Vehicles (AUVs) for which there is a developing and expanding range of applications in open-seas research, technology and underwater clean transport. Mature glider autonomy, operating depth (0-1000 meters) and low energy consumption without a CO 2 footprint enable evolutionary access across ocean basins. Pursuant to the first successful transatlantic glider crossing in December 2009, the Challenger Mission has opened the door to long-term, long-distance routine transoceanic AUV missions. These vehicles, which glide through the water column between 0 and 1000 meters depth, are highly sensitive to the ocean current field. Consequently, it is essential to exploit the complex space-time structure of the ocean current field in order to plan a path that optimizes scientific payoff and navigation efficiency. This letter demonstrates the capability of dynamical system theory for achieving this goal by realizing the real-time navigation strategy for the transoceanic AUV named Silbo, which is a Slocum deep-glider (0-1000 m), that crossed the North Atlantic from April 2016 to March 2017. Path planning in real time based on this approach has facilitated an impressive speed up of the AUV to unprecedented velocities resulting in major battery savings on the mission, offering the potential for routine transoceanic long duration missions.

Autonomous Collision-Free Navigation of Microvehicles in Complex and Dynamically Changing Environments.

PubMed

Li, Tianlong; Chang, Xiaocong; Wu, Zhiguang; Li, Jinxing; Shao, Guangbin; Deng, Xinghong; Qiu, Jianbin; Guo, Bin; Zhang, Guangyu; He, Qiang; Li, Longqiu; Wang, Joseph

2017-09-26

Self-propelled micro- and nanoscale robots represent a rapidly emerging and fascinating robotics research area. However, designing autonomous and adaptive control systems for operating micro/nanorobotics in complex and dynamically changing environments, which is a highly demanding feature, is still an unmet challenge. Here we describe a smart microvehicle for precise autonomous navigation in complicated environments and traffic scenarios. The fully autonomous navigation system of the smart microvehicle is composed of a microscope-coupled CCD camera, an artificial intelligence planner, and a magnetic field generator. The microscope-coupled CCD camera provides real-time localization of the chemically powered Janus microsphere vehicle and environmental detection for path planning to generate optimal collision-free routes, while the moving direction of the microrobot toward a reference position is determined by the external electromagnetic torque. Real-time object detection offers adaptive path planning in response to dynamically changing environments. We demonstrate that the autonomous navigation system can guide the vehicle movement in complex patterns, in the presence of dynamically changing obstacles, and in complex biological environments. Such a navigation system for micro/nanoscale vehicles, relying on vision-based close-loop control and path planning, is highly promising for their autonomous operation in complex dynamic settings and unpredictable scenarios expected in a variety of realistic nanoscale scenarios.

Near real-time automated dose restoration in IMPT to compensate for daily tissue density variations in prostate cancer

NASA Astrophysics Data System (ADS)

Jagt, Thyrza; Breedveld, Sebastiaan; van de Water, Steven; Heijmen, Ben; Hoogeman, Mischa

2017-06-01

Proton therapy is very sensitive to daily density changes along the pencil beam paths. The purpose of this study is to develop and evaluate an automated method for adaptation of IMPT plans to compensate for these daily tissue density variations. A two-step restoration method for ‘densities-of-the-day’ was created: (1) restoration of spot positions (Bragg peaks) by adapting the energy of each pencil beam to the new water equivalent path length; and (2) re-optimization of pencil beam weights by minimizing the dosimetric difference with the planned dose distribution, using a fast and exact quadratic solver. The method was developed and evaluated using 8-10 repeat CT scans of 10 prostate cancer patients. Experiments demonstrated that giving a high weight to the PTV in the re-optimization resulted in clinically acceptable restorations. For all scans we obtained V 95%  ⩾  98% and V 107%  ⩽  2%. For the bladder, the differences between the restored and the intended treatment plan were below  +2 Gy and  +2%-point. The rectum differences were below  +2 Gy and  +2%-point for 90% of the scans. In the remaining scans the rectum was filled with air, which partly overlapped with the PTV. The air cavity distorted the Bragg peak resulting in less favorable rectum doses.

Enhanced Fuel-Optimal Trajectory-Generation Algorithm for Planetary Pinpoint Landing

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Blackmore, James C.; Scharf, Daniel P.

2011-01-01

An enhanced algorithm is developed that builds on a previous innovation of fuel-optimal powered-descent guidance (PDG) for planetary pinpoint landing. The PDG problem is to compute constrained, fuel-optimal trajectories to land a craft at a prescribed target on a planetary surface, starting from a parachute cut-off point and using a throttleable descent engine. The previous innovation showed the minimal-fuel PDG problem can be posed as a convex optimization problem, in particular, as a Second-Order Cone Program, which can be solved to global optimality with deterministic convergence properties, and hence is a candidate for onboard implementation. To increase the speed and robustness of this convex PDG algorithm for possible onboard implementation, the following enhancements are incorporated: 1) Fast detection of infeasibility (i.e., control authority is not sufficient for soft-landing) for subsequent fault response. 2) The use of a piecewise-linear control parameterization, providing smooth solution trajectories and increasing computational efficiency. 3) An enhanced line-search algorithm for optimal time-of-flight, providing quicker convergence and bounding the number of path-planning iterations needed. 4) An additional constraint that analytically guarantees inter-sample satisfaction of glide-slope and non-sub-surface flight constraints, allowing larger discretizations and, hence, faster optimization. 5) Explicit incorporation of Mars rotation rate into the trajectory computation for improved targeting accuracy. These enhancements allow faster convergence to the fuel-optimal solution and, more importantly, remove the need for a "human-in-the-loop," as constraints will be satisfied over the entire path-planning interval independent of step-size (as opposed to just at the discrete time points) and infeasible initial conditions are immediately detected. Finally, while the PDG stage is typically only a few minutes, ignoring the rotation rate of Mars can introduce 10s of meters of error. By incorporating it, the enhanced PDG algorithm becomes capable of pinpoint targeting.

Computing the optimal path in stochastic dynamical systems

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

Bauver, Martha; Forgoston, Eric, E-mail: eric.forgoston@montclair.edu; Billings, Lora

2016-08-15

In stochastic systems, one is often interested in finding the optimal path that maximizes the probability of escape from a metastable state or of switching between metastable states. Even for simple systems, it may be impossible to find an analytic form of the optimal path, and in high-dimensional systems, this is almost always the case. In this article, we formulate a constructive methodology that is used to compute the optimal path numerically. The method utilizes finite-time Lyapunov exponents, statistical selection criteria, and a Newton-based iterative minimizing scheme. The method is applied to four examples. The first example is a two-dimensionalmore » system that describes a single population with internal noise. This model has an analytical solution for the optimal path. The numerical solution found using our computational method agrees well with the analytical result. The second example is a more complicated four-dimensional system where our numerical method must be used to find the optimal path. The third example, although a seemingly simple two-dimensional system, demonstrates the success of our method in finding the optimal path where other numerical methods are known to fail. In the fourth example, the optimal path lies in six-dimensional space and demonstrates the power of our method in computing paths in higher-dimensional spaces.« less

Two arm robot path planning in a static environment using polytopes and string stretching. Thesis

NASA Technical Reports Server (NTRS)

Schima, Francis J., III

1990-01-01

The two arm robot path planning problem has been analyzed and reduced into components to be simplified. This thesis examines one component in which two Puma-560 robot arms are simultaneously holding a single object. The problem is to find a path between two points around obstacles which is relatively fast and minimizes the distance. The thesis involves creating a structure on which to form an advanced path planning algorithm which could ideally find the optimum path. An actual path planning method is implemented which is simple though effective in most common situations. Given the limits of computer technology, a 'good' path is currently found. Objects in the workspace are modeled with polytopes. These are used because they can be used for rapid collision detection and still provide a representation which is adequate for path planning.

Autonomous search and surveillance with small fixed wing aircraft

NASA Astrophysics Data System (ADS)

McGee, Timothy Garland

Small unmanned aerial vehicles (UAVs) have the potential to act as low cost tools in a variety of both civilian and military applications including traffic monitoring, border patrol, and search and rescue. While most current operational UAV systems require human operators, advances in autonomy will allow these systems to reach their full potential as sensor platforms. This dissertation specifically focuses on developing advanced control, path planning, search, and image processing techniques that allow small fixed wing aircraft to autonomously collect data. The problems explored were motivated by experience with the development and experimental flight testing of a fleet of small autonomous fixed wing aircraft. These issues, which have not been fully addressed in past work done on ground vehicles or autonomous helicopters, include the influence of wind and turning rate constraints, the non-negligible velocity of ground targets relative to the aircraft velocity, and limitations on sensor size and processing power on small vehicles. Several contributions for the autonomous operation of small fixed wing aircraft are presented. Several sliding surface controllers are designed which extend previous techniques to include variable sliding surface coefficients and the use of spatial vehicle dynamics. These advances eliminate potential singularities in the control laws to follow spatially defined paths and allow smooth transition between controllers. The optimal solution for the problem of path planning through an ordered set of points for an aircraft with a bounded turning rate in the presence of a constant wind is then discussed. Path planning strategies are also explored to guarantee that a searcher will travel within sensing distance of a mobile ground target. This work assumes only a maximum velocity of the target and is designed to succeed for any possible path of the target. Closed-loop approximations of both the path planning and search techniques, using the sliding surface controllers already discussed, are also studied. Finally, a novel method is presented to detect obstacles by segmenting an image into sky and non-sky regions. The feasibility of this method is demonstrated experimentally on an aircraft test bed.

Computational path planner for product assembly in complex environments

NASA Astrophysics Data System (ADS)

Shang, Wei; Liu, Jianhua; Ning, Ruxin; Liu, Mi

2013-03-01

Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.

Cooperative organic mine avoidance path planning

NASA Astrophysics Data System (ADS)

McCubbin, Christopher B.; Piatko, Christine D.; Peterson, Adam V.; Donnald, Creighton R.; Cohen, David

2005-06-01

The JHU/APL Path Planning team has developed path planning techniques to look for paths that balance the utility and risk associated with different routes through a minefield. Extending on previous years' efforts, we investigated real-world Naval mine avoidance requirements and developed a tactical decision aid (TDA) that satisfies those requirements. APL has developed new mine path planning techniques using graph based and genetic algorithms which quickly produce near-minimum risk paths for complicated fitness functions incorporating risk, path length, ship kinematics, and naval doctrine. The TDA user interface, a Java Swing application that obtains data via Corba interfaces to path planning databases, allows the operator to explore a fusion of historic and in situ mine field data, control the path planner, and display the planning results. To provide a context for the minefield data, the user interface also renders data from the Digital Nautical Chart database, a database created by the National Geospatial-Intelligence Agency containing charts of the world's ports and coastal regions. This TDA has been developed in conjunction with the COMID (Cooperative Organic Mine Defense) system. This paper presents a description of the algorithms, architecture, and application produced.

Vervet monkeys use paths consistent with context-specific spatial movement heuristics.

PubMed

Teichroeb, Julie A

2015-10-01

Animal foraging routes are analogous to the computationally demanding "traveling salesman problem" (TSP), where individuals must find the shortest path among several locations before returning to the start. Humans approximate solutions to TSPs using simple heuristics or "rules of thumb," but our knowledge of how other animals solve multidestination routing problems is incomplete. Most nonhuman primate species have shown limited ability to route plan. However, captive vervets were shown to solve a TSP for six sites. These results were consistent with either planning three steps ahead or a risk-avoidance strategy. I investigated how wild vervet monkeys (Chlorocebus pygerythrus) solved a path problem with six, equally rewarding food sites; where site arrangement allowed assessment of whether vervets found the shortest route and/or used paths consistent with one of three simple heuristics to navigate. Single vervets took the shortest possible path in fewer than half of the trials, usually in ways consistent with the most efficient heuristic (the convex hull). When in competition, vervets' paths were consistent with different, more efficient heuristics dependent on their dominance rank (a cluster strategy for dominants and the nearest neighbor rule for subordinates). These results suggest that, like humans, vervets may solve multidestination routing problems by applying simple, adaptive, context-specific "rules of thumb." The heuristics that were consistent with vervet paths in this study are the same as some of those asserted to be used by humans. These spatial movement strategies may have common evolutionary roots and be part of a universal mental navigational toolkit. Alternatively, they may have emerged through convergent evolution as the optimal way to solve multidestination routing problems.

Death of the (traveling) salesman: primates do not show clear evidence of multi-step route planning.

PubMed

Janson, Charles

2014-05-01

Several comparative studies have linked larger brain size to a fruit-eating diet in primates and other animals. The general explanation for this correlation is that fruit is a complex resource base, consisting of many discrete patches of many species, each with distinct nutritional traits, the production of which changes predictably both within and between seasons. Using this information to devise optimal spatial foraging strategies is among the most difficult problems to solve in all of mathematics, a version of the famous Traveling Salesman Problem. Several authors have suggested that primates might use their large brains and complex cognition to plan foraging strategies that approximate optimal solutions to this problem. Three empirical studies have examined how captive primates move when confronted with the simplest version of the problem: a spatial array of equally valuable goals. These studies have all concluded that the subjects remember many food source locations and show very efficient travel paths; some authors also inferred that the subjects may plan their movements based on considering combinations of three or more future goals at a time. This analysis re-examines critically the claims of planned movement sequences from the evidence presented. The efficiency of observed travel paths is largely consistent with use of the simplest of foraging rules, such as visiting the nearest unused "known" resource. Detailed movement sequences by test subjects are most consistent with a rule that mentally sums spatial information from all unused resources in a given trial into a single "gravity" measure that guides movements to one destination at a time. © 2013 Wiley Periodicals, Inc.

Uncertainty reduction in intensity modulated proton therapy by inverse Monte Carlo treatment planning

NASA Astrophysics Data System (ADS)

Morávek, Zdenek; Rickhey, Mark; Hartmann, Matthias; Bogner, Ludwig

2009-08-01

Treatment plans for intensity-modulated proton therapy may be sensitive to some sources of uncertainty. One source is correlated with approximations of the algorithms applied in the treatment planning system and another one depends on how robust the optimization is with regard to intra-fractional tissue movements. The irradiated dose distribution may substantially deteriorate from the planning when systematic errors occur in the dose algorithm. This can influence proton ranges and lead to improper modeling of the Braggpeak degradation in heterogeneous structures or particle scatter or the nuclear interaction part. Additionally, systematic errors influence the optimization process, which leads to the convergence error. Uncertainties with regard to organ movements are related to the robustness of a chosen beam setup to tissue movements on irradiation. We present the inverse Monte Carlo treatment planning system IKO for protons (IKO-P), which tries to minimize the errors described above to a large extent. Additionally, robust planning is introduced by beam angle optimization according to an objective function penalizing paths representing strongly longitudinal and transversal tissue heterogeneities. The same score function is applied to optimize spot planning by the selection of a robust choice of spots. As spots can be positioned on different energy grids or on geometric grids with different space filling factors, a variety of grids were used to investigate the influence on the spot-weight distribution as a result of optimization. A tighter distribution of spot weights was assumed to result in a more robust plan with respect to movements. IKO-P is described in detail and demonstrated on a test case and a lung cancer case as well. Different options of spot planning and grid types are evaluated, yielding a superior plan quality with dose delivery to the spots from all beam directions over optimized beam directions. This option shows a tighter spot-weight distribution and should therefore be less sensitive to movements compared to optimized directions. But accepting a slight loss in plan quality, the latter choice could potentially improve robustness even further by accepting only spots from the most proper direction. The choice of a geometric grid instead of an energy grid for spot positioning has only a minor influence on the plan quality, at least for the investigated lung case.

Power Distribution System Planning with GIS Consideration

NASA Astrophysics Data System (ADS)

Wattanasophon, Sirichai; Eua-Arporn, Bundhit

This paper proposes a method for solving radial distribution system planning problems taking into account geographical information. The proposed method can automatically determine appropriate location and size of a substation, routing of feeders, and sizes of conductors while satisfying all constraints, i.e. technical constraints (voltage drop and thermal limit) and geographical constraints (obstacle, existing infrastructure, and high-cost passages). Sequential quadratic programming (SQP) and minimum path algorithm (MPA) are applied to solve the planning problem based on net price value (NPV) consideration. In addition this method integrates planner's experience and optimization process to achieve an appropriate practical solution. The proposed method has been tested with an actual distribution system, from which the results indicate that it can provide satisfactory plans.

Automated generation of weld path trajectories.

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

Sizemore, John M.; Hinman-Sweeney, Elaine Marie; Ames, Arlo Leroy

2003-06-01

AUTOmated GENeration of Control Programs for Robotic Welding of Ship Structure (AUTOGEN) is software that automates the planning and compiling of control programs for robotic welding of ship structure. The software works by evaluating computer representations of the ship design and the manufacturing plan. Based on this evaluation, AUTOGEN internally identifies and appropriately characterizes each weld. Then it constructs the robot motions necessary to accomplish the welds and determines for each the correct assignment of process control values. AUTOGEN generates these robot control programs completely without manual intervention or edits except to correct wrong or missing input data. Most shipmore » structure assemblies are unique or at best manufactured only a few times. Accordingly, the high cost inherent in all previous methods of preparing complex control programs has made robot welding of ship structures economically unattractive to the U.S. shipbuilding industry. AUTOGEN eliminates the cost of creating robot control programs. With programming costs eliminated, capitalization of robots to weld ship structures becomes economically viable. Robot welding of ship structures will result in reduced ship costs, uniform product quality, and enhanced worker safety. Sandia National Laboratories and Northrop Grumman Ship Systems worked with the National Shipbuilding Research Program to develop a means of automated path and process generation for robotic welding. This effort resulted in the AUTOGEN program, which has successfully demonstrated automated path generation and robot control. Although the current implementation of AUTOGEN is optimized for welding applications, the path and process planning capability has applicability to a number of industrial applications, including painting, riveting, and adhesive delivery.« less

Feasible Path Generation Using Bezier Curves for Car-Like Vehicle

NASA Astrophysics Data System (ADS)

Latip, Nor Badariyah Abdul; Omar, Rosli

2017-08-01

When planning a collision-free path for an autonomous vehicle, the main criteria that have to be considered are the shortest distance, lower computation time and completeness, i.e. a path can be found if one exists. Besides that, a feasible path for the autonomous vehicle is also crucial to guarantee that the vehicle can reach the target destination considering its kinematic constraints such as non-holonomic and minimum turning radius. In order to address these constraints, Bezier curves is applied. In this paper, Bezier curves are modeled and simulated using Matlab software and the feasibility of the resulting path is analyzed. Bezier curve is derived from a piece-wise linear pre-planned path. It is found that the Bezier curves has the capability of making the planned path feasible and could be embedded in a path planning algorithm for an autonomous vehicle with kinematic constraints. It is concluded that the length of segments of the pre-planned path have to be greater than a nominal value, derived from the vehicle wheelbase, maximum steering angle and maximum speed to ensure the path for the autonomous car is feasible.

Fast exploration of an optimal path on the multidimensional free energy surface

PubMed Central

Chen, Changjun

2017-01-01

In a reaction, determination of an optimal path with a high reaction rate (or a low free energy barrier) is important for the study of the reaction mechanism. This is a complicated problem that involves lots of degrees of freedom. For simple models, one can build an initial path in the collective variable space by the interpolation method first and then update the whole path constantly in the optimization. However, such interpolation method could be risky in the high dimensional space for large molecules. On the path, steric clashes between neighboring atoms could cause extremely high energy barriers and thus fail the optimization. Moreover, performing simulations for all the snapshots on the path is also time-consuming. In this paper, we build and optimize the path by a growing method on the free energy surface. The method grows a path from the reactant and extends its length in the collective variable space step by step. The growing direction is determined by both the free energy gradient at the end of the path and the direction vector pointing at the product. With fewer snapshots on the path, this strategy can let the path avoid the high energy states in the growing process and save the precious simulation time at each iteration step. Applications show that the presented method is efficient enough to produce optimal paths on either the two-dimensional or the twelve-dimensional free energy surfaces of different small molecules. PMID:28542475

On computing the global time-optimal motions of robotic manipulators in the presence of obstacles

NASA Technical Reports Server (NTRS)

Shiller, Zvi; Dubowsky, Steven

1991-01-01

A method for computing the time-optimal motions of robotic manipulators is presented that considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacles. The optimization problem is reduced to a search for the time-optimal path in the n-dimensional position space. A small set of near-optimal paths is first efficiently selected from a grid, using a branch and bound search and a series of lower bound estimates on the traveling time along a given path. These paths are further optimized with a local path optimization to yield the global optimal solution. Obstacles are considered by eliminating the collision points from the tessellated space and by adding a penalty function to the motion time in the local optimization. The computational efficiency of the method stems from the reduced dimensionality of the searched spaced and from combining the grid search with a local optimization. The method is demonstrated in several examples for two- and six-degree-of-freedom manipulators with obstacles.

Smooth Sensor Motion Planning for Robotic Cyber Physical Social Sensing (CPSS)

PubMed Central

Tang, Hong; Li, Liangzhi; Xiao, Nanfeng

2017-01-01

Although many researchers have begun to study the area of Cyber Physical Social Sensing (CPSS), few are focused on robotic sensors. We successfully utilize robots in CPSS, and propose a sensor trajectory planning method in this paper. Trajectory planning is a fundamental problem in mobile robotics. However, traditional methods are not suited for robotic sensors, because of their low efficiency, instability, and non-smooth-generated paths. This paper adopts an optimizing function to generate several intermediate points and regress these discrete points to a quintic polynomial which can output a smooth trajectory for the robotic sensor. Simulations demonstrate that our approach is robust and efficient, and can be well applied in the CPSS field. PMID:28218649

Research on optimal investment path of transmission corridor under the global energy Internet

NASA Astrophysics Data System (ADS)

Huang, Yuehui; Li, Pai; Wang, Qi; Liu, Jichun; Gao, Han

2018-02-01

Under the background of the global energy Internet, the investment planning of transmission corridor from XinJiang to Germany is studied in this article, which passes through four countries: Kazakhstan, Russia, Belarus and Poland. Taking the specific situation of different countries into account, including the length of transmission line, unit construction cost, completion time, transmission price, state tariff, inflation rate and so on, this paper constructed a power transmission investment model. Finally, the dynamic programming method is used to simulate the example, and the optimal strategies under different objective functions are obtained.

Remedial training for the radiology resident: a template for optimization of the learning plan.

PubMed

Mar, Colin; Chang, Silvia; Forster, Bruce

2015-02-01

All radiology residency programs should strive for the early identification of individuals in need of remedial training and have an approach ready to address this situation. This article provides a template for a step-by-step approach which is team based. It includes definition of the learning or performance issues, creation of suitable learning objectives and learning plan, facilitation of feedback and assessment, and definition of outcomes. Using such a template will assist the resident in returning to the path toward a safe and competent radiologist. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Overconfidence, preview, and probability in strategic planning

NASA Technical Reports Server (NTRS)

Wickens, Christopher D.; Pizarro, David; Bell, Brian

1991-01-01

The performance of eight subjects in a 'rescue' video game requiring choices as to which node they should fly to in order to rescue the simulated casualties is presently studied with a view to biases and display support criteria in strategic planning. After each choice, the subjects needed to fly a challenging tracking dynamic along a path to reach the next node. The results obtained indicate that the choices of the subjects were less optimal when full preview was offered, perhaps due to subjects' reliance on the simple strategy of choosing routes with the greatest number of casualties.

Pedestrian Pathfinding in Urban Environments: Preliminary Results

NASA Astrophysics Data System (ADS)

López-Pazos, G.; Balado, J.; Díaz-Vilariño, L.; Arias, P.; Scaioni, M.

2017-12-01

With the rise of urban population, many initiatives are focused upon the smart city concept, in which mobility of citizens arises as one of the main components. Updated and detailed spatial information of outdoor environments is needed to accurate path planning for pedestrians, especially for people with reduced mobility, in which physical barriers should be considered. This work presents a methodology to use point clouds to direct path planning. The starting point is a classified point cloud in which ground elements have been previously classified as roads, sidewalks, crosswalks, curbs and stairs. The remaining points compose the obstacle class. The methodology starts by individualizing ground elements and simplifying them into representative points, which are used as nodes in the graph creation. The region of influence of obstacles is used to refine the graph. Edges of the graph are weighted according to distance between nodes and according to their accessibility for wheelchairs. As a result, we obtain a very accurate graph representing the as-built environment. The methodology has been tested in a couple of real case studies and Dijkstra algorithm was used to pathfinding. The resulting paths represent the optimal according to motor skills and safety.

Optimization of educational paths for higher education

NASA Astrophysics Data System (ADS)

Tarasyev, Alexandr A.; Agarkov, Gavriil; Medvedev, Aleksandr

2017-11-01

In our research, we combine the theory of economic behavior and the methodology of increasing efficiency of the human capital to estimate the optimal educational paths. We provide an optimization model for higher education process to analyze possible educational paths for each rational individual. The preferences of each rational individual are compared to the best economically possible educational path. The main factor of the individual choice, which is formed by the formation of optimal educational path, deals with higher salaries level in the chosen economic sector after graduation. Another factor that influences on the economic profit is the reduction of educational costs or the possibility of the budget support for the student. The main outcome of this research consists in correction of the governmental policy of investment in human capital based on the results of educational paths optimal control.

A continuous arc delivery optimization algorithm for CyberKnife m6.

PubMed

Kearney, Vasant; Descovich, Martina; Sudhyadhom, Atchar; Cheung, Joey P; McGuinness, Christopher; Solberg, Timothy D

2018-06-01

This study aims to reduce the delivery time of CyberKnife m6 treatments by allowing for noncoplanar continuous arc delivery. To achieve this, a novel noncoplanar continuous arc delivery optimization algorithm was developed for the CyberKnife m6 treatment system (CyberArc-m6). CyberArc-m6 uses a five-step overarching strategy, in which an initial set of beam geometries is determined, the robotic delivery path is calculated, direct aperture optimization is conducted, intermediate MLC configurations are extracted, and the final beam weights are computed for the continuous arc radiation source model. This algorithm was implemented on five prostate and three brain patients, previously planned using a conventional step-and-shoot CyberKnife m6 delivery technique. The dosimetric quality of the CyberArc-m6 plans was assessed using locally confined mutual information (LCMI), conformity index (CI), heterogeneity index (HI), and a variety of common clinical dosimetric objectives. Using conservative optimization tuning parameters, CyberArc-m6 plans were able to achieve an average CI difference of 0.036 ± 0.025, an average HI difference of 0.046 ± 0.038, and an average LCMI of 0.920 ± 0.030 compared with the original CyberKnife m6 plans. Including a 5 s per minute image alignment time and a 5-min setup time, conservative CyberArc-m6 plans achieved an average treatment delivery speed up of 1.545x ± 0.305x compared with step-and-shoot plans. The CyberArc-m6 algorithm was able to achieve dosimetrically similar plans compared to their step-and-shoot CyberKnife m6 counterparts, while simultaneously reducing treatment delivery times. © 2018 American Association of Physicists in Medicine.

An Innovative Multi-Agent Search-and-Rescue Path Planning Approach

DTIC Science & Technology

2015-03-09

search problems from search theory and artificial intelligence /distributed robotic control, and pursuit-evasion problem perspectives may be found in...Dissanayake, “Probabilistic search for a moving target in an indoor environment”, In Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, 2006, pp...3393-3398. [7] H. Lau, and G. Dissanayake, “Optimal search for multiple targets in a built environment”, In Proc. IEEE/RSJ Int. Conf. Intelligent

Physics-Aware Informative Coverage Planning for Autonomous Vehicles

DTIC Science & Technology

2014-06-01

environment and find the optimal path connecting fixed nodes, which is equivalent to solving the Traveling Salesman Problem (TSP). While TSP is an NP...intended for application to USV harbor patrolling, it is applicable to many different domains. The problem of traveling over an area and gathering...environment. I. INTRODUCTION There are many applications that need persistent monitor- ing of a given area, requiring repeated travel over the area to

Campaign-level dynamic network modelling for spaceflight logistics for the flexible path concept

NASA Astrophysics Data System (ADS)

Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

2016-06-01

This paper develops a network optimization formulation for dynamic campaign-level space mission planning. Although many past space missions have been designed mainly from a mission-level perspective, a campaign-level perspective will be important for future space exploration. In order to find the optimal campaign-level space transportation architecture, a mixed-integer linear programming (MILP) formulation with a generalized multi-commodity flow and a time-expanded network is developed. Particularly, a new heuristics-based method, a partially static time-expanded network, is developed to provide a solution quickly. The developed method is applied to a case study containing human exploration of a near-Earth object (NEO) and Mars, related to the concept of the Flexible Path. The numerical results show that using the specific combinations of propulsion technologies, in-situ resource utilization (ISRU), and other space infrastructure elements can reduce the initial mass in low-Earth orbit (IMLEO) significantly. In addition, the case study results also show that we can achieve large IMLEO reduction by designing NEO and Mars missions together as a campaign compared with designing them separately owing to their common space infrastructure pre-deployment. This research will be an important step toward efficient and flexible campaign-level space mission planning.

Distribution path robust optimization of electric vehicle with multiple distribution centers

PubMed Central

Hao, Wei; He, Ruichun; Jia, Xiaoyan; Pan, Fuquan; Fan, Jing; Xiong, Ruiqi

2018-01-01

To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas’ theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model. PMID:29518169

Investigating Education and Immediate Career Paths of Master's and Doctoral Graduates over the Past Few Decades

NASA Astrophysics Data System (ADS)

Wilson, C. E.; Keane, C. M.

2016-12-01

Students enter into geoscience graduate degree programs have specific expectations of the type of career they are working towards. Are the graduate degree programs effectively serving these students through the development of necessary skills and experiences for their desired career pathway? This question is of particular interest to parties like the National Science Foundation and other STEM agencies who are concerned about the optimal investment in the development of the science and engineering workforce. To address this question, investigation on the general trends of education and immediate career paths over time is needed. The National Science Foundation has been collecting data on education and career paths of science and engineering graduates for decades. Since 2013, AGI has been collecting data from geoscience graduates since 2013 on their education, skills development, and immediate plans after graduation through AGI's Geoscience Student Exit Survey. This presentation synthesizes the data from these two sources related to geoscience master's and doctoral graduates to look at education and career paths over time to see how they have changed over the past few decades, as well as look specifically at the immediate plans of recent graduates as they enter the geoscience workforce. This data will also give some indication of the development of skills gained from these programs through activities such as field work and research.

Simulation techniques in hyperthermia treatment planning

PubMed Central

Paulides, MM; Stauffer, PR; Neufeld, E; Maccarini, P; Kyriakou, A; Canters, RAM; Diederich, C; Bakker, JF; Van Rhoon, GC

2013-01-01

Clinical trials have shown that hyperthermia (HT), i.e. an increase of tissue temperature to 39-44°C, significantly enhance radiotherapy and chemotherapy effectiveness (1). Driven by the developments in computational techniques and computing power, personalized hyperthermia treatment planning (HTP) has matured and has become a powerful tool for optimizing treatment quality. Electromagnetic, ultrasound, and thermal simulations using realistic clinical setups are now being performed to achieve patient-specific treatment optimization. In addition, extensive studies aimed to properly implement novel HT tools and techniques, and to assess the quality of HT, are becoming more common. In this paper, we review the simulation tools and techniques developed for clinical hyperthermia, and evaluate their current status on the path from “model” to “clinic”. In addition, we illustrate the major techniques employed for validation and optimization. HTP has become an essential tool for improvement, control, and assessment of HT treatment quality. As such, it plays a pivotal role in the quest to establish HT as an efficacious addition to multi-modality treatment of cancer. PMID:23672453

Land use-based landscape planning and restoration in mine closure areas.

PubMed

Zhang, Jianjun; Fu, Meichen; Hassani, Ferri P; Zeng, Hui; Geng, Yuhuan; Bai, Zhongke

2011-05-01

Landscape planning and restoration in mine closure areas is not only an inevitable choice to sustain mining areas but also an important path to maximize landscape resources and to improve ecological function in mine closure areas. The analysis of the present mine development shows that many mines are unavoidably facing closures in China. This paper analyzes the periodic impact of mining activities on landscapes and then proposes planning concepts and principles. According to the landscape characteristics in mine closure areas, this paper classifies available landscape resources in mine closure areas into the landscape for restoration, for limited restoration and for protection, and then summarizes directions for their uses. This paper establishes the framework of spatial control planning and design of landscape elements from "macro control, medium allocation and micro optimization" for the purpose of managing and using this kind of special landscape resources. Finally, this paper applies the theories and methods to a case study in Wu'an from two aspects: the construction of a sustainable land-use pattern on a large scale and the optimized allocation of typical mine landscape resources on a small scale.

Research on Collection System Optimal Design of Wind Farm with Obstacles

NASA Astrophysics Data System (ADS)

Huang, W.; Yan, B. Y.; Tan, R. S.; Liu, L. F.

2017-05-01

To the collection system optimal design of offshore wind farm, the factors considered are not only the reasonable configuration of the cable and switch, but also the influence of the obstacles on the topology design of the offshore wind farm. This paper presents a concrete topology optimization algorithm with obstacles. The minimal area rectangle encasing box of the obstacle is obtained by using the method of minimal area encasing box. Then the optimization algorithm combining the advantages of Dijkstra algorithm and Prim algorithm is used to gain the scheme of avoidance obstacle path planning. Finally a fuzzy comprehensive evaluation model based on the analytic hierarchy process is constructed to compare the performance of the different topologies. Case studies demonstrate the feasibility of the proposed algorithm and model.

Efficient sampling of parsimonious inversion histories with application to genome rearrangement in Yersinia.

PubMed

Miklós, István; Darling, Aaron E

2009-06-22

Inversions are among the most common mutations acting on the order and orientation of genes in a genome, and polynomial-time algorithms exist to obtain a minimal length series of inversions that transform one genome arrangement to another. However, the minimum length series of inversions (the optimal sorting path) is often not unique as many such optimal sorting paths exist. If we assume that all optimal sorting paths are equally likely, then statistical inference on genome arrangement history must account for all such sorting paths and not just a single estimate. No deterministic polynomial algorithm is known to count the number of optimal sorting paths nor sample from the uniform distribution of optimal sorting paths. Here, we propose a stochastic method that uniformly samples the set of all optimal sorting paths. Our method uses a novel formulation of parallel Markov chain Monte Carlo. In practice, our method can quickly estimate the total number of optimal sorting paths. We introduce a variant of our approach in which short inversions are modeled to be more likely, and we show how the method can be used to estimate the distribution of inversion lengths and breakpoint usage in pathogenic Yersinia pestis. The proposed method has been implemented in a program called "MC4Inversion." We draw comparison of MC4Inversion to the sampler implemented in BADGER and a previously described importance sampling (IS) technique. We find that on high-divergence data sets, MC4Inversion finds more optimal sorting paths per second than BADGER and the IS technique and simultaneously avoids bias inherent in the IS technique.

JPL-ANTOPT antenna structure optimization program

NASA Technical Reports Server (NTRS)

Strain, D. M.

1994-01-01

New antenna path-length error and pointing-error structure optimization codes were recently added to the MSC/NASTRAN structural analysis computer program. Path-length and pointing errors are important measured of structure-related antenna performance. The path-length and pointing errors are treated as scalar displacements for statics loading cases. These scalar displacements can be subject to constraint during the optimization process. Path-length and pointing-error calculations supplement the other optimization and sensitivity capabilities of NASTRAN. The analysis and design functions were implemented as 'DMAP ALTERs' to the Design Optimization (SOL 200) Solution Sequence of MSC-NASTRAN, Version 67.5.

Hopfield networks for solving Tower of Hanoi problems

NASA Astrophysics Data System (ADS)

Kaplan, G. B.; Güzeliş, Cüneyt

2001-08-01

In this paper, Hopfield neural networks have been considered in solving the Tower of Hanoi test which is used in the determining of deficit of planning capability of the human prefrontal cortex. The main difference between this paper and the ones in the literature which use neural networks is that the Tower of Hanoi problem has been formulated here as a special shortest-path problem. In the literature, some Hopfield networks are developed for solving the shortest path problem which is a combinatorial optimization problem having a diverse field of application. The approach given in this paper gives the possibility of solving the Tower of Hanoi problem using these Hopfield networks. Also, the paper proposes new Hopfield network models for the shortest path and hence the Tower of Hanoi problems and compares them to the available ones in terms of the memory and time (number of steps) needed in the simulations.

From the physics of interacting polymers to optimizing routes on the London Underground

PubMed Central

Yeung, Chi Ho; Saad, David; Wong, K. Y. Michael

2013-01-01

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise. PMID:23898198

From the physics of interacting polymers to optimizing routes on the London Underground.

PubMed

Yeung, Chi Ho; Saad, David; Wong, K Y Michael

2013-08-20

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise.

Multigeneration data migration from legacy systems

NASA Astrophysics Data System (ADS)

Ratib, Osman M.; Liu, Brent J.; Kho, Hwa T.; Tao, Wenchao; Wang, Cun; McCoy, J. Michael

2003-05-01

The migration of image data from different generations of legacy archive systems represents a technical challenge and in incremental cost in transitions to newer generations of PACS. UCLA medical center has elected to completely replace the existing PACS infrastructure encompassing several generations of legacy systems by a new commercial system providing enterprise-wide image management and communication. One of the most challenging parts of the project was the migration of large volumes of legacy images into the new system. Planning of the migration required the development of specialized software and hardware, and included different phases of data mediation from existing databases to the new PACS database prior to the migration of the image data. The project plan included a detailed analysis of resources and cost of data migration to optimize the process and minimize the delay of a hybrid operation where the legacy systems need to remain operational. Our analysis and project planning showed that the data migration represents the most critical path in the process of PACS renewal. Careful planning and optimization of the project timeline and resources allocated is critical to minimize the financial impact and the time delays that such migrations can impose on the implementation plan.

The application of Markov decision process in restaurant delivery robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Hu, Zhen; Wang, Ying

2017-05-01

As the restaurant delivery robot is often in a dynamic and complex environment, including the chairs inadvertently moved to the channel and customers coming and going. The traditional path planning algorithm is not very ideal. To solve this problem, this paper proposes the Markov dynamic state immediate reward (MDR) path planning algorithm according to the traditional Markov decision process. First of all, it uses MDR to plan a global path, then navigates along this path. When the sensor detects there is no obstructions in front state, increase its immediate state reward value; when the sensor detects there is an obstacle in front, plan a global path that can avoid obstacle with the current position as the new starting point and reduce its state immediate reward value. This continues until the target is reached. When the robot learns for a period of time, it can avoid those places where obstacles are often present when planning the path. By analyzing the simulation experiment, the algorithm has achieved good results in the global path planning under the dynamic environment.

Planning 3-D collision-free paths using spheres

NASA Technical Reports Server (NTRS)

Bonner, Susan; Kelley, Robert B.

1989-01-01

A scheme for the representation of objects, the Successive Spherical Approximation (SSA), facilitates the rapid planning of collision-free paths in a 3-D, dynamic environment. The hierarchical nature of the SSA allows collision-free paths to be determined efficiently while still providing for the exact representation of dynamic objects. The concept of a freespace cell is introduced to allow human 3-D conceptual knowledge to be used in facilitating satisfying choices for paths. Collisions can be detected at a rate better than 1 second per environment object per path. This speed enables the path planning process to apply a hierarchy of rules to create a heuristically satisfying collision-free path.

Neural Architectures for Control

NASA Technical Reports Server (NTRS)

Peterson, James K.

1991-01-01

The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

Methodology for Augmenting Existing Paths with Additional Parallel Transects

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

Wilson, John E.

2013-09-30

Visual Sample Plan (VSP) is sample planning software that is used, among other purposes, to plan transect sampling paths to detect areas that were potentially used for munition training. This module was developed for application on a large site where existing roads and trails were to be used as primary sampling paths. Gap areas between these primary paths needed to found and covered with parallel transect paths. These gap areas represent areas on the site that are more than a specified distance from a primary path. These added parallel paths needed to optionally be connected together into a single path—themore » shortest path possible. The paths also needed to optionally be attached to existing primary paths, again with the shortest possible path. Finally, the process must be repeatable and predictable so that the same inputs (primary paths, specified distance, and path options) will result in the same set of new paths every time. This methodology was developed to meet those specifications.« less

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

PubMed Central

Robinson, Y. Harold; Rajaram, M.

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique. PMID:26819966

Efficient Sampling of Parsimonious Inversion Histories with Application to Genome Rearrangement in Yersinia

PubMed Central

Darling, Aaron E.

2009-01-01

Inversions are among the most common mutations acting on the order and orientation of genes in a genome, and polynomial-time algorithms exist to obtain a minimal length series of inversions that transform one genome arrangement to another. However, the minimum length series of inversions (the optimal sorting path) is often not unique as many such optimal sorting paths exist. If we assume that all optimal sorting paths are equally likely, then statistical inference on genome arrangement history must account for all such sorting paths and not just a single estimate. No deterministic polynomial algorithm is known to count the number of optimal sorting paths nor sample from the uniform distribution of optimal sorting paths. Here, we propose a stochastic method that uniformly samples the set of all optimal sorting paths. Our method uses a novel formulation of parallel Markov chain Monte Carlo. In practice, our method can quickly estimate the total number of optimal sorting paths. We introduce a variant of our approach in which short inversions are modeled to be more likely, and we show how the method can be used to estimate the distribution of inversion lengths and breakpoint usage in pathogenic Yersinia pestis. The proposed method has been implemented in a program called “MC4Inversion.” We draw comparison of MC4Inversion to the sampler implemented in BADGER and a previously described importance sampling (IS) technique. We find that on high-divergence data sets, MC4Inversion finds more optimal sorting paths per second than BADGER and the IS technique and simultaneously avoids bias inherent in the IS technique. PMID:20333186

A Sequential Linear Quadratic Approach for Constrained Nonlinear Optimal Control with Adaptive Time Discretization and Application to Higher Elevation Mars Landing Problem

NASA Astrophysics Data System (ADS)

Sandhu, Amit

A sequential quadratic programming method is proposed for solving nonlinear optimal control problems subject to general path constraints including mixed state-control and state only constraints. The proposed algorithm further develops on the approach proposed in [1] with objective to eliminate the use of a high number of time intervals for arriving at an optimal solution. This is done by introducing an adaptive time discretization to allow formation of a desirable control profile without utilizing a lot of intervals. The use of fewer time intervals reduces the computation time considerably. This algorithm is further used in this thesis to solve a trajectory planning problem for higher elevation Mars landing.

Avionic Architecture for Model Predictive Control Application in Mars Sample & Return Rendezvous Scenario

NASA Astrophysics Data System (ADS)

Saponara, M.; Tramutola, A.; Creten, P.; Hardy, J.; Philippe, C.

2013-08-01

Optimization-based control techniques such as Model Predictive Control (MPC) are considered extremely attractive for space rendezvous, proximity operations and capture applications that require high level of autonomy, optimal path planning and dynamic safety margins. Such control techniques require high-performance computational needs for solving large optimization problems. The development and implementation in a flight representative avionic architecture of a MPC based Guidance, Navigation and Control system has been investigated in the ESA R&T study “On-line Reconfiguration Control System and Avionics Architecture” (ORCSAT) of the Aurora programme. The paper presents the baseline HW and SW avionic architectures, and verification test results obtained with a customised RASTA spacecraft avionics development platform from Aeroflex Gaisler.

Simulating Mission Command for Planning and Analysis

DTIC Science & Technology

2015-06-01

mission plan. 14. SUBJECT TERMS Mission Planning, CPM , PERT, Simulation, DES, Simkit, Triangle Distribution, Critical Path 15. NUMBER OF...Battalion Task Force CO Company CPM Critical Path Method DES Discrete Event Simulation FA BAT Field Artillery Battalion FEL Future Event List FIST...management tools that can be utilized to find the critical path in military projects. These are the Critical Path Method ( CPM ) and the Program Evaluation and

Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths.

PubMed

Payne, Velma L; Medvedeva, Olga; Legowski, Elizabeth; Castine, Melissa; Tseytlin, Eugene; Jukic, Drazen; Crowley, Rebecca S

2009-11-01

Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from learning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on learning gains when using a tutor with limited enforcement. Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goal states and solution paths were determined. Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path. Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path.

Grid Visualization Tool

NASA Technical Reports Server (NTRS)

Chouinard, Caroline; Fisher, Forest; Estlin, Tara; Gaines, Daniel; Schaffer, Steven

2005-01-01

The Grid Visualization Tool (GVT) is a computer program for displaying the path of a mobile robotic explorer (rover) on a terrain map. The GVT reads a map-data file in either portable graymap (PGM) or portable pixmap (PPM) format, representing a gray-scale or color map image, respectively. The GVT also accepts input from path-planning and activity-planning software. From these inputs, the GVT generates a map overlaid with one or more rover path(s), waypoints, locations of targets to be explored, and/or target-status information (indicating success or failure in exploring each target). The display can also indicate different types of paths or path segments, such as the path actually traveled versus a planned path or the path traveled to the present position versus planned future movement along a path. The program provides for updating of the display in real time to facilitate visualization of progress. The size of the display and the map scale can be changed as desired by the user. The GVT was written in the C++ language using the Open Graphics Library (OpenGL) software. It has been compiled for both Sun Solaris and Linux operating systems.

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.

An improved multi-paths optimization method for video stabilization

NASA Astrophysics Data System (ADS)

Qin, Tao; Zhong, Sheng

2018-03-01

For video stabilization, the difference between original camera motion path and the optimized one is proportional to the cropping ratio and warping ratio. A good optimized path should preserve the moving tendency of the original one meanwhile the cropping ratio and warping ratio of each frame should be kept in a proper range. In this paper we use an improved warping-based motion representation model, and propose a gauss-based multi-paths optimization method to get a smoothing path and obtain a stabilized video. The proposed video stabilization method consists of two parts: camera motion path estimation and path smoothing. We estimate the perspective transform of adjacent frames according to warping-based motion representation model. It works well on some challenging videos where most previous 2D methods or 3D methods fail for lacking of long features trajectories. The multi-paths optimization method can deal well with parallax, as we calculate the space-time correlation of the adjacent grid, and then a kernel of gauss is used to weigh the motion of adjacent grid. Then the multi-paths are smoothed while minimize the crop ratio and the distortion. We test our method on a large variety of consumer videos, which have casual jitter and parallax, and achieve good results.

Research on cutting path optimization of sheet metal parts based on ant colony algorithm

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Ling, H.; Li, L.; Wu, L. H.; Liu, N. B.

2017-09-01

In view of the disadvantages of the current cutting path optimization methods of sheet metal parts, a new method based on ant colony algorithm was proposed in this paper. The cutting path optimization problem of sheet metal parts was taken as the research object. The essence and optimization goal of the optimization problem were presented. The traditional serial cutting constraint rule was improved. The cutting constraint rule with cross cutting was proposed. The contour lines of parts were discretized and the mathematical model of cutting path optimization was established. Thus the problem was converted into the selection problem of contour lines of parts. Ant colony algorithm was used to solve the problem. The principle and steps of the algorithm were analyzed.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

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

Reister, D.B.

This paper uses the Pontryagin maximum principle to find time optimal paths for a constant speed unicycle. The time optimal paths consist of sequences of arcs of circles and straight lines. The maximum principle introduced concepts (dual variables, bang-bang solutions, singular solutions, and transversality conditions) that provide important insight into the nature of the time optimal paths. 10 refs., 6 figs.

Automated Planning and Scheduling for Planetary Rover Distributed Operations

NASA Technical Reports Server (NTRS)

Backes, Paul G.; Rabideau, Gregg; Tso, Kam S.; Chien, Steve

1999-01-01

Automated planning and Scheduling, including automated path planning, has been integrated with an Internet-based distributed operations system for planetary rover operations. The resulting prototype system enables faster generation of valid rover command sequences by a distributed planetary rover operations team. The Web Interface for Telescience (WITS) provides Internet-based distributed collaboration, the Automated Scheduling and Planning Environment (ASPEN) provides automated planning and scheduling, and an automated path planner provided path planning. The system was demonstrated on the Rocky 7 research rover at JPL.

Terrain classification in navigation of an autonomous mobile robot

NASA Astrophysics Data System (ADS)

Dodds, David R.

1991-03-01

In this paper we describe a method of path planning that integrates terrain classification (by means of fractals) the certainty grid method of spatial representation Kehtarnavaz Griswold collision-zones Dubois Prade fuzzy temporal and spatial knowledge and non-point sized qualitative navigational planning. An initially planned (" end-to-end" ) path is piece-wise modified to accommodate known and inferred moving obstacles and includes attention to time-varying multiple subgoals which may influence a section of path at a time after the robot has begun traversing that planned path.

Planning paths through a spatial hierarchy - Eliminating stair-stepping effects

NASA Technical Reports Server (NTRS)

Slack, Marc G.

1989-01-01

Stair-stepping effects are a result of the loss of spatial continuity resulting from the decomposition of space into a grid. This paper presents a path planning algorithm which eliminates stair-stepping effects induced by the grid-based spatial representation. The algorithm exploits a hierarchical spatial model to efficiently plan paths for a mobile robot operating in dynamic domains. The spatial model and path planning algorithm map to a parallel machine, allowing the system to operate incrementally, thereby accounting for unexpected events in the operating space.

An Efficient Method to Design Premature End-of-Life Trajectories: A Hypothetical Alternate Fate for Cassini

NASA Technical Reports Server (NTRS)

Vaquero, Mar; Senent, Juan

2015-01-01

What would happen if, hypothetically, the highly successful Cassini mission were to end prematurely due to lack of propellant or sudden subsystem failure? A solid plan to quickly produce a solution for any given scenario, regardless of where the spacecraft is along its reference path, must be in place to safely dispose of the spacecraft and meet all planetary protection requirements. As a contingency plan for this hypothetical situation, a method to design viable high-fidelity terminating trajectories based on a hybrid approach that exploits two-body and three-body flyby transfers combined with a numerical optimization scheme is detailed in this paper.

Optimized planning of in-service inspections of local flow-accelerated corrosion of pipeline elements used in the secondary coolant circuit of the VVER-440-based units at the Novovoronezh NPP

NASA Astrophysics Data System (ADS)

Tomarov, G. V.; Povarov, V. P.; Shipkov, A. A.; Gromov, A. F.; Budanov, V. A.; Golubeva, T. N.

2015-03-01

Matters concerned with making efficient use of the information-analytical system on the flow-accelerated corrosion problem in setting up in-service examination of the metal of pipeline elements operating in the secondary coolant circuit of the VVER-440-based power units at the Novovoronezh NPP are considered. The principles used to select samples of pipeline elements in planning ultrasonic thickness measurements for timely revealing metal thinning due to flow-accelerated corrosion along with reducing the total amount of measurements in the condensate-feedwater path are discussed.

Optimizing Mars Airplane Trajectory with the Application Navigation System

NASA Technical Reports Server (NTRS)

Frumkin, Michael; Riley, Derek

2004-01-01

Planning complex missions requires a number of programs to be executed in concert. The Application Navigation System (ANS), developed in the NAS Division, can execute many interdependent programs in a distributed environment. We show that the ANS simplifies user effort and reduces time in optimization of the trajectory of a martian airplane. We use a software package, Cart3D, to evaluate trajectories and a shortest path algorithm to determine the optimal trajectory. ANS employs the GridScape to represent the dynamic state of the available computer resources. Then, ANS uses a scheduler to dynamically assign ready task to machine resources and the GridScape for tracking available resources and forecasting completion time of running tasks. We demonstrate system capability to schedule and run the trajectory optimization application with efficiency exceeding 60% on 64 processors.

SU-F-T-198: Dosimetric Comparison of Carbon and Proton Radiotherapy for Recurrent Nasopharynx Carcinoma

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

Sheng, Y; Zhao, J; Wang, W

2016-06-15

Purpose: Various radiotherapy planning methods for locally recurrent nasopharynx carcinoma (R-NPC) have been proposed. The purpose of this study was to compare carbon and proton therapy for the treatment of R-NPC in terms of dose coverage for target volume and sparing for organs at risk (OARs). Methods: Six patients who were suffering from R-NPC and treated using carbon therapy were selected for this study. Treatment plans with a total dose of 57.5Gy (RBE) in 23 fractions were made using SIEMENS Syngo V11. An intensity-modulated radiotherapy optimization method was chosen for carbon plans (IMCT) while for proton plans both intensity-modulated radiotherapymore » (IMPT) and single beam optimization (proton-SBO) methods were chosen. Dose distributions, dose volume parameters, and selected dosimetric indices for target volumes and OARs were compared for all treatment plans. Results: All plans provided comparable PTV coverage. The volume covered by 95% of the prescribed dose was comparable for all three plans. The average values were 96.11%, 96.24% and 96.11% for IMCT, IMPT, and proton-SBO respectively. A significant reduction of the 80% and 50% dose volumes were observed for the IMCT plans compared to the IMPT and proton-SBO plans. Critical organs lateral to the target such as brain stem and spinal cord were better spared by IMPT than by proton-SBO, while IMCT spared those organs best. For organs in the beam path, such as parotid glands, the mean dose results were similar for all three plans. Conclusion: Carbon plans yielded better dose conformity than proton plans. They provided similar or better target coverage while significantly lowering the dose for normal tissues. Dose sparing for critical organs in IMPT plans was better than proton-SBO, however, IMPT is known to be more sensitive to range uncertainties. For proton plans it is essential to find a balance between the two optimization methods.« less

Optimal impulsive time-fixed orbital rendezvous and interception with path constraints

NASA Technical Reports Server (NTRS)

Taur, D.-R.; Prussing, J. E.; Coverstone-Carroll, V.

1990-01-01

Minimum-fuel, impulsive, time-fixed solutions are obtained for the problem of orbital rendezvous and interception with interior path constraints. Transfers between coplanar circular orbits in an inverse-square gravitational field are considered, subject to a circular path constraint representing a minimum or maximum permissible orbital radius. Primer vector theory is extended to incorporate path constraints. The optimal number of impulses, their times and positions, and the presence of initial or final coasting arcs are determined. The existence of constraint boundary arcs and boundary points is investigated as well as the optimality of a class of singular arc solutions. To illustrate the complexities introduced by path constraints, an analysis is made of optimal rendezvous in field-free space subject to a minimum radius constraint.

Rotational-path decomposition based recursive planning for spacecraft attitude reorientation

NASA Astrophysics Data System (ADS)

Xu, Rui; Wang, Hui; Xu, Wenming; Cui, Pingyuan; Zhu, Shengying

2018-02-01

The spacecraft reorientation is a common task in many space missions. With multiple pointing constraints, it is greatly difficult to solve the constrained spacecraft reorientation planning problem. To deal with this problem, an efficient rotational-path decomposition based recursive planning (RDRP) method is proposed in this paper. The uniform pointing-constraint-ignored attitude rotation planning process is designed to solve all rotations without considering pointing constraints. Then the whole path is checked node by node. If any pointing constraint is violated, the nearest critical increment approach will be used to generate feasible alternative nodes in the process of rotational-path decomposition. As the planning path of each subdivision may still violate pointing constraints, multiple decomposition is needed and the reorientation planning is designed as a recursive manner. Simulation results demonstrate the effectiveness of the proposed method. The proposed method has been successfully applied in two SPARK microsatellites to solve onboard constrained attitude reorientation planning problem, which were developed by the Shanghai Engineering Center for Microsatellites and launched on 22 December 2016.

Analyzing Study of Path loss Propagation Models in Wireless Communications at 0.8 GHz

NASA Astrophysics Data System (ADS)

Kadhim Hoomod, Haider; Al-Mejibli, Intisar; Issa Jabboory, Abbas

2018-05-01

The paths loss propagation model is an important tool in wireless network planning, allowing network planner to optimize the cell towers distribution and meet expected service level requirements. However, each type of path loss propagation model is designed to predict path loss in a particular environment that may be inaccurate in other different environment. In this research different propagation models (Hata Model, ICC-33 Model, Ericson Model and Coast-231 Model) have been analyzed and compared based on the measured data. The measured data represent signal strength of two cell towers placed in two different environments which obtained by a drive test of them. First one in AL-Habebea represents an urban environment (high-density region) and the second in AL-Hindea district represents a rural environment (low-density region) with operating frequency 0.8 GHz. The results of performing the analysis and comparison conclude that Hata model and Ericsson model shows small deviation from real measurements in urban environment and Hata model generally gives better prediction in the rural environment.

MGA trajectory planning with an ACO-inspired algorithm

NASA Astrophysics Data System (ADS)

Ceriotti, Matteo; Vasile, Massimiliano

2010-11-01

Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by two-dimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by ant colony optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter. Solutions are compared to those found through more traditional genetic-algorithm techniques.

Research on NC laser combined cutting optimization model of sheet metal parts

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Zhang, Y. L.; Li, L.; Wu, L. H.; Liu, N. B.

2017-09-01

The optimization problem for NC laser combined cutting of sheet metal parts was taken as the research object in this paper. The problem included two contents: combined packing optimization and combined cutting path optimization. In the problem of combined packing optimization, the method of “genetic algorithm + gravity center NFP + geometric transformation” was used to optimize the packing of sheet metal parts. In the problem of combined cutting path optimization, the mathematical model of cutting path optimization was established based on the parts cutting constraint rules of internal contour priority and cross cutting. The model played an important role in the optimization calculation of NC laser combined cutting.

A linguistic geometry for 3D strategic planning

NASA Technical Reports Server (NTRS)

Stilman, Boris

1995-01-01

This paper is a new step in the development and application of the Linguistic Geometry. This formal theory is intended to discover the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing Linguistic Geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in this paper on the new pilot example of the solution of the extremely complex 3D optimization problem of strategic planning for the space combat of autonomous vehicles. This example demonstrates deep and highly selective search in comparison with conventional search algorithms.

Three-dimensional path planning software-assisted transjugular intrahepatic portosystemic shunt: a technical modification.

PubMed

Tsauo, Jiaywei; Luo, Xuefeng; Ye, Linchao; Li, Xiao

2015-06-01

This study was designed to report our results with a modified technique of three-dimensional (3D) path planning software assisted transjugular intrahepatic portosystemic shunt (TIPS). 3D path planning software was recently developed to facilitate TIPS creation by using two carbon dioxide portograms acquired at least 20° apart to generate a 3D path for overlay needle guidance. However, one shortcoming is that puncturing along the overlay would be technically impossible if the angle of the liver access set and the angle of the 3D path are not the same. To solve this problem, a prototype 3D path planning software was fitted with a utility to calculate the angle of the 3D path. Using this, we modified the angle of the liver access set accordingly during the procedure in ten patients. Failure for technical reasons occurred in three patients (unsuccessful wedged hepatic venography in two cases, software technical failure in one case). The procedure was successful in the remaining seven patients, and only one needle pass was required to obtain portal vein access in each case. The course of puncture was comparable to the 3D path in all patients. No procedure-related complication occurred following the procedures. Adjusting the angle of the liver access set to match the angle of the 3D path determined by the software appears to be a favorable modification to the technique of 3D path planning software assisted TIPS.

Toward a preoperative planning tool for brain tumor resection therapies.

PubMed

Coffey, Aaron M; Miga, Michael I; Chen, Ishita; Thompson, Reid C

2013-01-01

Neurosurgical procedures involving tumor resection require surgical planning such that the surgical path to the tumor is determined to minimize the impact on healthy tissue and brain function. This work demonstrates a predictive tool to aid neurosurgeons in planning tumor resection therapies by finding an optimal model-selected patient orientation that minimizes lateral brain shift in the field of view. Such orientations may facilitate tumor access and removal, possibly reduce the need for retraction, and could minimize the impact of brain shift on image-guided procedures. In this study, preoperative magnetic resonance images were utilized in conjunction with pre- and post-resection laser range scans of the craniotomy and cortical surface to produce patient-specific finite element models of intraoperative shift for 6 cases. These cases were used to calibrate a model (i.e., provide general rules for the application of patient positioning parameters) as well as determine the current model-based framework predictive capabilities. Finally, an objective function is proposed that minimizes shift subject to patient position parameters. Patient positioning parameters were then optimized and compared to our neurosurgeon as a preliminary study. The proposed model-driven brain shift minimization objective function suggests an overall reduction of brain shift by 23 % over experiential methods. This work recasts surgical simulation from a trial-and-error process to one where options are presented to the surgeon arising from an optimization of surgical goals. To our knowledge, this is the first realization of an evaluative tool for surgical planning that attempts to optimize surgical approach by means of shift minimization in this manner.

An engineering optimization method with application to STOL-aircraft approach and landing trajectories

NASA Technical Reports Server (NTRS)

Jacob, H. G.

1972-01-01

An optimization method has been developed that computes the optimal open loop inputs for a dynamical system by observing only its output. The method reduces to static optimization by expressing the inputs as series of functions with parameters to be optimized. Since the method is not concerned with the details of the dynamical system to be optimized, it works for both linear and nonlinear systems. The method and the application to optimizing longitudinal landing paths for a STOL aircraft with an augmented wing are discussed. Noise, fuel, time, and path deviation minimizations are considered with and without angle of attack, acceleration excursion, flight path, endpoint, and other constraints.

A novel representation for planning 3-D collision-free paths

NASA Technical Reports Server (NTRS)

Bonner, Susan; Kelley, Robert B.

1990-01-01

A new scheme for the representation of objects, the successive spherical approximation (SSA), facilitates the rapid planning of collision-free paths in a dynamic three-dimensional environment. The hierarchical nature of the SSA allows collisions to be determined efficiently while still providing an exact representation of objects. The rapidity with which collisions can be detected, less than 1 sec per environment object per path, makes it possible to use a generate-and-test path-planning strategy driven by human conceptual knowledge to determine collision-free paths in a matter of seconds on a Sun 3/180 computer. A hierarchy of rules, based on the concept of a free space cell, is used to find heuristically satisfying collision-free paths in a structured environment.

Investigation of progressive failure robustness and alternate load paths for damage tolerant structures

NASA Astrophysics Data System (ADS)

Marhadi, Kun Saptohartyadi

Structural optimization for damage tolerance under various unforeseen damage scenarios is computationally challenging. It couples non-linear progressive failure analysis with sampling-based stochastic analysis of random damage. The goal of this research was to understand the relationship between alternate load paths available in a structure and its damage tolerance, and to use this information to develop computationally efficient methods for designing damage tolerant structures. Progressive failure of a redundant truss structure subjected to small random variability was investigated to identify features that correlate with robustness and predictability of the structure's progressive failure. The identified features were used to develop numerical surrogate measures that permit computationally efficient deterministic optimization to achieve robustness and predictability of progressive failure. Analysis of damage tolerance on designs with robust progressive failure indicated that robustness and predictability of progressive failure do not guarantee damage tolerance. Damage tolerance requires a structure to redistribute its load to alternate load paths. In order to investigate the load distribution characteristics that lead to damage tolerance in structures, designs with varying degrees of damage tolerance were generated using brute force stochastic optimization. A method based on principal component analysis was used to describe load distributions (alternate load paths) in the structures. Results indicate that a structure that can develop alternate paths is not necessarily damage tolerant. The alternate load paths must have a required minimum load capability. Robustness analysis of damage tolerant optimum designs indicates that designs are tailored to specified damage. A design Optimized under one damage specification can be sensitive to other damages not considered. Effectiveness of existing load path definitions and characterizations were investigated for continuum structures. A load path definition using a relative compliance change measure (U* field) was demonstrated to be the most useful measure of load path. This measure provides quantitative information on load path trajectories and qualitative information on the effectiveness of the load path. The use of the U* description of load paths in optimizing structures for effective load paths was investigated.

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo (Editor)

1990-01-01

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Motion planning: A journey of robots, molecules, digital actors, and other artifacts

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

Latombe, J.C.

1999-11-01

During the past three decades, motion planning has emerged as a crucial and productive research area in robotics. In the mid-1980s, the most advanced planners were barely able to compute collision-free paths for objects crawling in planar workspaces. Today, planners efficiently deal with robots with many degrees of freedom in complex environments. Techniques also exist to generate quasi-optimal trajectories, coordinate multiple robots, deal with dynamic and kinematic constraints, and handle dynamic environments. This paper describes some of these achievements, presents new problems that have recently emerged, discusses applications likely to motivate future research, and finally gives expectations for the comingmore » years. It stresses the fact that nonrobotics applications (e.g., graphic animation, surgical planning, computational biology) are growing in importance and are likely to shape future motion-planning research more than robotics itself.« less

EGU2013 SM1.4/GI1.6 session: "Improving seismic networks performances: from site selection to data integration"

NASA Astrophysics Data System (ADS)

Pesaresi, D.; Busby, R.

2013-08-01

The number and quality of seismic stations and networks in Europe continually improves, nevertheless there is always scope to optimize their performance. In this session we welcomed contributions from all aspects of seismic network installation, operation and management. This includes site selection; equipment testing and installation; planning and implementing communication paths; policies for redundancy in data acquisition, processing and archiving; and integration of different datasets including GPS and OBS.

Vane Pump Casing Machining of Dumpling Machine Based on CAD/CAM

NASA Astrophysics Data System (ADS)

Huang, Yusen; Li, Shilong; Li, Chengcheng; Yang, Zhen

Automatic dumpling forming machine is also called dumpling machine, which makes dumplings through mechanical motions. This paper adopts the stuffing delivery mechanism featuring the improved and specially-designed vane pump casing, which can contribute to the formation of dumplings. Its 3D modeling in Pro/E software, machining process planning, milling path optimization, simulation based on UG and compiling post program were introduced and verified. The results indicated that adoption of CAD/CAM offers firms the potential to pursue new innovative strategies.

Traffic Patrol Service Platform Scheduling and Containment Optimization Strategy

NASA Astrophysics Data System (ADS)

Wang, Tiane; Niu, Taiyang; Wan, Baocheng; Li, Jian

This article is based on the traffic and patrol police service platform settings and scheduling, in order to achieve the main purpose of rapid containment for the suspect in an emergency event. Proposing new boundary definition based on graph theory, using 0-1 programming, Dijkstra algorithm, the shortest path tree (SPT) and some of the related knowledge establish a containment model. Finally, making a combination with a city-specific data and using this model obtain the best containment plan.

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

Khan, S; Chin, E; Xing, L

Purpose: The integration of couch motion during arc delivery is necessitated to enable irradiation trajectories such as coronal arcs, and to enhance the geometrical sampling for dynamic deliveries to the highest extent. To enable such capability, a platform of Trajectory Modulated Arc Therapy (TMAT) is developed in conjunction with standardized noncollisional dynamic path-set for irradiation of intracranial lesions. Methods: A generalized path-set was constructed through the combination of sagittal arcs (45 degrees from the CAX), axial arcs, and coronal arcs produced through modulation of the dynamic rotation of couch. The standardized path was implemented in a contiguous manner enabling themore » formation of fully automated sub-trajectories to provide maximal geometrical convergence with minimal number of arcs. Progressive sampling technique is used for direct aperture optimization of the MLCs and the selection of couch positions across the control points. Dosimetry of the resulting plans was assessed relative to clinically delivered plans. Using the TrueBeam Developer Mode, plan deliverability was tested. Results: Treatment planning of TMAT sub-trajectories for central, anterior and posterior tumor sites with volumes ranging from 4.75cc to 107cc demonstrated radically reduced doses to the critical OARs when compared to the clinically treated VMAT. Specifically, percentage reduction in mean dose for critical organs such as brainstem, cochlea, and optic nerve are found to be as low as 74±15%, 50±26% and 74±30% respectively as compared to VMAT. Conformity Index, defined as the ratio of tumor volume (VPTV) and 100% dose volume (V(D100%)), was reduced up to 12% while the Gradient Index, defined as V(D100%)/V(D50%), was concurrently improved by up to 14%. Conclusion: An automated standardized trajectory with dynamically modulated couch-gantry arcs has been developed for intracranial radiotherapy. Through the incorporation of coronal arcs, it is demonstrated that significantly reduced OAR doses can be achieved relative to clinically treated patient plans via VMAT. Research Grant Funding Support by Varian Medical Systems.« less

Three-Dimensional Path Planning Software-Assisted Transjugular Intrahepatic Portosystemic Shunt: A Technical Modification

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

Tsauo, Jiaywei, E-mail: 80732059@qq.com; Luo, Xuefeng, E-mail: luobo-913@126.com; Ye, Linchao, E-mail: linchao.ye@siemens.com

2015-06-15

PurposeThis study was designed to report our results with a modified technique of three-dimensional (3D) path planning software assisted transjugular intrahepatic portosystemic shunt (TIPS).Methods3D path planning software was recently developed to facilitate TIPS creation by using two carbon dioxide portograms acquired at least 20° apart to generate a 3D path for overlay needle guidance. However, one shortcoming is that puncturing along the overlay would be technically impossible if the angle of the liver access set and the angle of the 3D path are not the same. To solve this problem, a prototype 3D path planning software was fitted with a utility to calculate themore » angle of the 3D path. Using this, we modified the angle of the liver access set accordingly during the procedure in ten patients.ResultsFailure for technical reasons occurred in three patients (unsuccessful wedged hepatic venography in two cases, software technical failure in one case). The procedure was successful in the remaining seven patients, and only one needle pass was required to obtain portal vein access in each case. The course of puncture was comparable to the 3D path in all patients. No procedure-related complication occurred following the procedures.ConclusionsAdjusting the angle of the liver access set to match the angle of the 3D path determined by the software appears to be a favorable modification to the technique of 3D path planning software assisted TIPS.« less

Research and application of genetic algorithm in path planning of logistics distribution vehicle

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

The core of the logistics distribution system is the vehicle routing planning, research path planning problem, provide a better solution has become an important issue. In order to provide the decision support for logistics and distribution operations, this paper studies the problem of vehicle routing with capacity constraints (CVRP). By establishing a mathematical model, the genetic algorithm is used to plan the path of the logistics vehicle to meet the minimum logistics and transportation costs.

Rapid Onboard Trajectory Design for Autonomous Spacecraft in Multibody Systems

NASA Astrophysics Data System (ADS)

Trumbauer, Eric Michael

This research develops automated, on-board trajectory planning algorithms in order to support current and new mission concepts. These include orbiter missions to Phobos or Deimos, Outer Planet Moon orbiters, and robotic and crewed missions to small bodies. The challenges stem from the limited on-board computing resources which restrict full trajectory optimization with guaranteed convergence in complex dynamical environments. The approach taken consists of leveraging pre-mission computations to create a large database of pre-computed orbits and arcs. Such a database is used to generate a discrete representation of the dynamics in the form of a directed graph, which acts to index these arcs. This allows the use of graph search algorithms on-board in order to provide good approximate solutions to the path planning problem. Coupled with robust differential correction and optimization techniques, this enables the determination of an efficient path between any boundary conditions with very little time and computing effort. Furthermore, the optimization methods developed here based on sequential convex programming are shown to have provable convergence properties, as well as generating feasible major iterates in case of a system interrupt -- a key requirement for on-board application. The outcome of this project is thus the development of an algorithmic framework which allows the deployment of this approach in a variety of specific mission contexts. Test cases related to missions of interest to NASA and JPL such as a Phobos orbiter and a Near Earth Asteroid interceptor are demonstrated, including the results of an implementation on the RAD750 flight processor. This method fills a gap in the toolbox being developed to create fully autonomous space exploration systems.

Time optimized path-choice in the termite hunting ant Megaponera analis.

PubMed

Frank, Erik T; Hönle, Philipp O; Linsenmair, K Eduard

2018-05-10

Trail network systems among ants have received a lot of scientific attention due to their various applications in problem solving of networks. Recent studies have shown that ants select the fastest available path when facing different velocities on different substrates, rather than the shortest distance. The progress of decision-making by these ants is determined by pheromone-based maintenance of paths, which is a collective decision. However, path optimization through individual decision-making remains mostly unexplored. Here we present the first study of time-optimized path selection via individual decision-making by scout ants. Megaponera analis scouts search for termite foraging sites and lead highly organized raid columns to them. The path of the scout determines the path of the column. Through installation of artificial roads around M. analis nests we were able to influence the pathway choice of the raids. After road installation 59% of all recorded raids took place completely or partly on the road, instead of the direct, i.e. distance-optimized, path through grass from the nest to the termites. The raid velocity on the road was more than double the grass velocity, the detour thus saved 34.77±23.01% of the travel time compared to a hypothetical direct path. The pathway choice of the ants was similar to a mathematical model of least time allowing us to hypothesize the underlying mechanisms regulating the behavior. Our results highlight the importance of individual decision-making in the foraging behavior of ants and show a new procedure of pathway optimization. © 2018. Published by The Company of Biologists Ltd.

Smell Detection Agent Based Optimization Algorithm

NASA Astrophysics Data System (ADS)

Vinod Chandra, S. S.

2016-09-01

In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

Optimizing a mobile robot control system using GPU acceleration

NASA Astrophysics Data System (ADS)

Tuck, Nat; McGuinness, Michael; Martin, Fred

2012-01-01

This paper describes our attempt to optimize a robot control program for the Intelligent Ground Vehicle Competition (IGVC) by running computationally intensive portions of the system on a commodity graphics processing unit (GPU). The IGVC Autonomous Challenge requires a control program that performs a number of different computationally intensive tasks ranging from computer vision to path planning. For the 2011 competition our Robot Operating System (ROS) based control system would not run comfortably on the multicore CPU on our custom robot platform. The process of profiling the ROS control program and selecting appropriate modules for porting to run on a GPU is described. A GPU-targeting compiler, Bacon, is used to speed up development and help optimize the ported modules. The impact of the ported modules on overall performance is discussed. We conclude that GPU optimization can free a significant amount of CPU resources with minimal effort for expensive user-written code, but that replacing heavily-optimized library functions is more difficult, and a much less efficient use of time.

Codification of scan path parameters and development of perimeter scan strategies for 3D bowl-shaped laser forming

NASA Astrophysics Data System (ADS)

Tavakoli, A.; Naeini, H. Moslemi; Roohi, Amir H.; Gollo, M. Hoseinpour; Shahabad, Sh. Imani

2018-01-01

In the 3D laser forming process, developing an appropriate laser scan pattern for producing specimens with high quality and uniformity is critical. This study presents certain principles for developing scan paths. Seven scan path parameters are considered, including: (1) combined linear or curved path; (2) type of combined linear path; (3) order of scan sequences; (4) the position of the start point in each scan; (5) continuous or discontinuous scan path; (6) direction of scan path; and (7) angular arrangement of combined linear scan paths. Regarding these path parameters, ten combined linear scan patterns are presented. Numerical simulations show continuous hexagonal, scan pattern, scanning from outer to inner path, is the optimized. In addition, it is observed the position of the start point and the angular arrangement of scan paths is the most effective path parameters. Also, further experimentations show four sequences due to creat symmetric condition enhance the height of the bowl-shaped products and uniformity. Finally, the optimized hexagonal pattern was compared with the similar circular one. In the hexagonal scan path, distortion value and standard deviation rather to edge height of formed specimen is very low, and the edge height despite of decreasing length of scan path increases significantly compared to the circular scan path. As a result, four-sequence hexagonal scan pattern is proposed as the optimized perimeter scan path to produce bowl-shaped product.

Self-organization and solution of shortest-path optimization problems with memristive networks

NASA Astrophysics Data System (ADS)

Pershin, Yuriy V.; Di Ventra, Massimiliano

2013-07-01

We show that memristive networks, namely networks of resistors with memory, can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time nonlocality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.

MO-F-CAMPUS-T-05: SQL Database Queries to Determine Treatment Planning Resource Usage

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

Fox, C; Gladstone, D

2015-06-15

Purpose: A radiation oncology clinic’s treatment capacity is traditionally thought to be limited by the number of machines in the clinic. As the number of fractions per course decrease and the number of adaptive plans increase, the question of how many treatment plans a clinic can plan becomes increasingly important. This work seeks to lay the ground work for assessing treatment planning resource usage. Methods: Care path templates were created using the Aria 11 care path interface. Care path tasks included key steps in the treatment planning process from the completion of CT simulation through the first radiation treatment. SQLmore » Server Management Studio was used to run SQL queries to extract task completion time stamps along with care path template information and diagnosis codes from the Aria database. 6 months of planning cycles were evaluated. Elapsed time was evaluated in terms of work hours within Monday – Friday, 7am to 5pm. Results: For the 195 validated treatment planning cycles, the average time for planning and MD review was 22.8 hours. Of those cases 33 were categorized as urgent. The average planning time for urgent plans was 5 hours. A strong correlation between diagnosis code and range of elapsed planning time was as well as between elapsed time and select diagnosis codes was observed. It was also observed that tasks were more likely to be completed on the date due than the time that they were due. Follow-up confirmed that most users did not look at the due time. Conclusion: Evaluation of elapsed planning time and other tasks suggest that care paths should be adjusted to allow for different contouring and planning times for certain diagnosis codes and urgent cases. Additional clinic training around task due times vs dates or a structuring of care paths around due dates is also needed.« less

A multi-criteria approach to camera motion design for volume data animation.

PubMed

Hsu, Wei-Hsien; Zhang, Yubo; Ma, Kwan-Liu

2013-12-01

We present an integrated camera motion design and path generation system for building volume data animations. Creating animations is an essential task in presenting complex scientific visualizations. Existing visualization systems use an established animation function based on keyframes selected by the user. This approach is limited in providing the optimal in-between views of the data. Alternatively, computer graphics and virtual reality camera motion planning is frequently focused on collision free movement in a virtual walkthrough. For semi-transparent, fuzzy, or blobby volume data the collision free objective becomes insufficient. Here, we provide a set of essential criteria focused on computing camera paths to establish effective animations of volume data. Our dynamic multi-criteria solver coupled with a force-directed routing algorithm enables rapid generation of camera paths. Once users review the resulting animation and evaluate the camera motion, they are able to determine how each criterion impacts path generation. In this paper, we demonstrate how incorporating this animation approach with an interactive volume visualization system reduces the effort in creating context-aware and coherent animations. This frees the user to focus on visualization tasks with the objective of gaining additional insight from the volume data.

Optimization of OSPF Routing in IP Networks

NASA Astrophysics Data System (ADS)

Bley, Andreas; Fortz, Bernard; Gourdin, Eric; Holmberg, Kaj; Klopfenstein, Olivier; Pióro, Michał; Tomaszewski, Artur; Ümit, Hakan

The Internet is a huge world-wide packet switching network comprised of more than 13,000 distinct subnetworks, referred to as Autonomous Systems (ASs) autonomous system AS . They all rely on the Internet Protocol (IP) internet protocol IP for transport of packets across the network. And most of them use shortest path routing protocols shortest path routing!protocols , such as OSPF or IS-IS, to control the routing of IP packets routing!of IP packets within an AS. The idea of the routing is extremely simple — every packet is forwarded on IP links along the shortest route between its source and destination nodes of the AS. The AS network administrator can manage the routing of packets in the AS by supplying the so-called administrative weights of IP links, which specify the link lengths that are used by the routing protocols for their shortest path computations. The main advantage of the shortest path routing policy is its simplicity, allowing for little administrative overhead. From the network engineering perspective, however, shortest path routing can pose problems in achieving satisfactory traffic handling efficiency. As all routing paths depend on the same routing metric routing!metric , it is not possible to configure the routing paths for the communication demands between different pairs of nodes explicitly or individually; the routing can be controlled only indirectly and only as a whole by modifying the routing metric. Thus, one of the main tasks when planning such networks is to find administrative link weights that induce a globally efficient traffic routing traffic!routing configuration of an AS. It turns out that this task leads to very difficult mathematical optimization problems. In this chapter, we discuss and describe exact integer programming models and solution approaches as well as practically efficient smart heuristics for such shortest path routing problems shortest path routing!problems .

Quad-rotor flight path energy optimization

NASA Astrophysics Data System (ADS)

Kemper, Edward

Quad-Rotor unmanned areal vehicles (UAVs) have been a popular area of research and development in the last decade, especially with the advent of affordable microcontrollers like the MSP 430 and the Raspberry Pi. Path-Energy Optimization is an area that is well developed for linear systems. In this thesis, this idea of path-energy optimization is extended to the nonlinear model of the Quad-rotor UAV. The classical optimization technique is adapted to the nonlinear model that is derived for the problem at hand, coming up with a set of partial differential equations and boundary value conditions to solve these equations. Then, different techniques to implement energy optimization algorithms are tested using simulations in Python. First, a purely nonlinear approach is used. This method is shown to be computationally intensive, with no practical solution available in a reasonable amount of time. Second, heuristic techniques to minimize the energy of the flight path are tested, using Ziegler-Nichols' proportional integral derivative (PID) controller tuning technique. Finally, a brute force look-up table based PID controller is used. Simulation results of the heuristic method show that both reliable control of the system and path-energy optimization are achieved in a reasonable amount of time.

Constrained VPH+: a local path planning algorithm for a bio-inspired crawling robot with customized ultrasonic scanning sensor.

PubMed

Rao, Akshay; Elara, Mohan Rajesh; Elangovan, Karthikeyan

This paper aims to develop a local path planning algorithm for a bio-inspired, reconfigurable crawling robot. A detailed description of the robotic platform is first provided, and the suitability for deployment of each of the current state-of-the-art local path planners is analyzed after an extensive literature review. The Enhanced Vector Polar Histogram algorithm is described and reformulated to better fit the requirements of the platform. The algorithm is deployed on the robotic platform in crawling configuration and favorably compared with other state-of-the-art local path planning algorithms.

Teleconnection Paths via Climate Network Direct Link Detection.

PubMed

Zhou, Dong; Gozolchiani, Avi; Ashkenazy, Yosef; Havlin, Shlomo

2015-12-31

Teleconnections describe remote connections (typically thousands of kilometers) of the climate system. These are of great importance in climate dynamics as they reflect the transportation of energy and climate change on global scales (like the El Niño phenomenon). Yet, the path of influence propagation between such remote regions, and weighting associated with different paths, are only partially known. Here we propose a systematic climate network approach to find and quantify the optimal paths between remotely distant interacting locations. Specifically, we separate the correlations between two grid points into direct and indirect components, where the optimal path is found based on a minimal total cost function of the direct links. We demonstrate our method using near surface air temperature reanalysis data, on identifying cross-latitude teleconnections and their corresponding optimal paths. The proposed method may be used to quantify and improve our understanding regarding the emergence of climate patterns on global scales.

Assessing the Performance of Human-Automation Collaborative Planning Systems

DTIC Science & Technology

2011-06-01

process- ing and incorporating vast amounts of incoming information into their solutions. How- ever, these algorithms are brittle and unable to account for...planning system, a descriptive Mission Performance measure may address the total travel time on the path or the cost of the path (e.g. total work...minimizing costs or collisions [4, 32, 33]. Error measures for such a path planning system may track how many collisions occur or how much threat

Path optimization method for the sign problem

NASA Astrophysics Data System (ADS)

Ohnishi, Akira; Mori, Yuto; Kashiwa, Kouji

2018-03-01

We propose a path optimization method (POM) to evade the sign problem in the Monte-Carlo calculations for complex actions. Among many approaches to the sign problem, the Lefschetz-thimble path-integral method and the complex Langevin method are promising and extensively discussed. In these methods, real field variables are complexified and the integration manifold is determined by the flow equations or stochastically sampled. When we have singular points of the action or multiple critical points near the original integral surface, however, we have a risk to encounter the residual and global sign problems or the singular drift term problem. One of the ways to avoid the singular points is to optimize the integration path which is designed not to hit the singular points of the Boltzmann weight. By specifying the one-dimensional integration-path as z = t +if(t)(f ɛ R) and by optimizing f(t) to enhance the average phase factor, we demonstrate that we can avoid the sign problem in a one-variable toy model for which the complex Langevin method is found to fail. In this proceedings, we propose POM and discuss how we can avoid the sign problem in a toy model. We also discuss the possibility to utilize the neural network to optimize the path.

Design of Quiet Rotorcraft Approach Trajectories

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Burley, Casey L.; Boyd, D. Douglas, Jr.; Marcolini, Michael A.

2009-01-01

A optimization procedure for identifying quiet rotorcraft approach trajectories is proposed and demonstrated. The procedure employs a multi-objective genetic algorithm in order to reduce noise and create approach paths that will be acceptable to pilots and passengers. The concept is demonstrated by application to two different helicopters. The optimized paths are compared with one another and to a standard 6-deg approach path. The two demonstration cases validate the optimization procedure but highlight the need for improved noise prediction techniques and for additional rotorcraft acoustic data sets.

Converging Towards the Optimal Path to Extinction

DTIC Science & Technology

2011-01-01

the reproductive rate R0 should be greater than but very close to 1. However, most real diseases have R0 larger than 1.5, which translates into a...can analytically find an expression for the action along the optimal path. The expression for the action is a function of k and the reproductive number...the optimal path for a range of values of the reproductive number R0. In contrast to the prior two examples, here the action must be computed

Path planning for persistent surveillance applications using fixed-wing unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Keller, James F.

This thesis addresses coordinated path planning for fixed-wing Unmanned Aerial Vehicles (UAVs) engaged in persistent surveillance missions. While uniquely suited to this mission, fixed wing vehicles have maneuver constraints that can limit their performance in this role. Current technology vehicles are capable of long duration flight with a minimal acoustic footprint while carrying an array of cameras and sensors. Both military tactical and civilian safety applications can benefit from this technology. We make three main contributions: C1 A sequential path planner that generates a C 2 flight plan to persistently acquire a covering set of data over a user designated area of interest. The planner features the following innovations: • A path length abstraction that embeds kino-dynamic motion constraints to estimate feasible path length. • A Traveling Salesman-type planner to generate a covering set route based on the path length abstraction. • A smooth path generator that provides C 2 routes that satisfy user specified curvature constraints. C2 A set of algorithms to coordinate multiple UAVs, including mission commencement from arbitrary locations to the start of a coordinated mission and de-confliction of paths to avoid collisions with other vehicles and fixed obstacles. C3 A numerically robust toolbox of spline-based algorithms tailored for vehicle routing validated through flight test experiments on multiple platforms. A variety of tests and platforms are discussed. The algorithms presented are based on a technical approach with approximately equal emphasis on analysis, computation, dynamic simulation, and flight test experimentation. Our planner (C1) directly takes into account vehicle maneuverability and agility constraints that could otherwise render simple solutions infeasible. This is especially important when surveillance objectives elevate the importance of optimized paths. Researchers have developed a diverse range of solutions for persistent surveillance applications but few directly address dynamic maneuver constraints. The key feature of C1 is a two stage sequential solution that discretizes the problem so that graph search techniques can be combined with parametric polynomial curve generation. A method to abstract the kino-dynamics of the aerial platforms is then presented so that a graph search solution can be adapted for this application. An A* Traveling Salesman Problem (TSP) algorithm is developed to search the discretized space using the abstract distance metric to acquire more data or avoid obstacles. Results of the graph search are then transcribed into smooth paths based on vehicle maneuver constraints. A complete solution for a single vehicle periodic tour of the area is developed using the results of the graph search algorithm. To execute the mission, we present a simultaneous arrival algorithm (C2) to coordinate execution by multiple vehicles to satisfy data refresh requirements and to ensure there are no collisions at any of the path intersections. We present a toolbox of spline-based algorithms (C3) to streamline the development of C2 continuous paths with numerical stability. These tools are applied to an aerial persistent surveillance application to illustrate their utility. Comparisons with other parametric polynomial approaches are highlighted to underscore the benefits of the B-spline framework. Performance limits with respect to feasibility constraints are documented.

Evolutionistic or revolutionary paths? A PACS maturity model for strategic situational planning.

PubMed

van de Wetering, Rogier; Batenburg, Ronald; Lederman, Reeva

2010-07-01

While many hospitals are re-evaluating their current Picture Archiving and Communication System (PACS), few have a mature strategy for PACS deployment. Furthermore, strategies for implementation, strategic and situational planning methods for the evolution of PACS maturity are scarce in the scientific literature. Consequently, in this paper we propose a strategic planning method for PACS deployment. This method builds upon a PACS maturity model (PMM), based on the elaboration of the strategic alignment concept and the maturity growth path concept previously developed in the PACS domain. First, we review the literature on strategic planning for information systems and information technology and PACS maturity. Secondly, the PMM is extended by applying four different strategic perspectives of the Strategic Alignment Framework whereupon two types of growth paths (evolutionistic and revolutionary) are applied that focus on a roadmap for PMM. This roadmap builds a path to get from one level of maturity and evolve to the next. An extended method for PACS strategic planning is developed. This method defines eight distinctive strategies for PACS strategic situational planning that allow decision-makers in hospitals to decide which approach best suits their hospitals' current situation and future ambition and what in principle is needed to evolve through the different maturity levels. The proposed method allows hospitals to strategically plan for PACS maturation. It is situational in that the required investments and activities depend on the alignment between the hospital strategy and the selected growth path. The inclusion of both strategic alignment and maturity growth path concepts make the planning method rigorous, and provide a framework for further empirical research and clinical practice.

Spacecraft Attitude Maneuver Planning Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Kornfeld, Richard P.

2004-01-01

A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.

A path planning method used in fluid jet polishing eliminating lightweight mirror imprinting effect

NASA Astrophysics Data System (ADS)

Li, Wenzong; Fan, Bin; Shi, Chunyan; Wang, Jia; Zhuo, Bin

2014-08-01

With the development of space technology, the design of optical system tends to large aperture lightweight mirror with high dimension-thickness ratio. However, when the lightweight mirror PV value is less than λ/10 , the surface will show wavy imprinting effect obviously. Imprinting effect introduced by head-tool pressure has become a technological barrier in high-precision lightweight mirror manufacturing. Fluid jet polishing can exclude outside pressure. Presently, machining tracks often used are grating type path, screw type path and pseudo-random path. On the edge of imprinting error, the speed of adjacent path points changes too fast, which causes the machine hard to reflect quickly, brings about new path error, and increases the polishing time due to superfluous path. This paper presents a new planning path method to eliminate imprinting effect. Simulation results show that the path of the improved grating path can better eliminate imprinting effect compared to the general path.

Distributed Optimization of Multi-Agent Systems: Framework, Local Optimizer, and Applications

NASA Astrophysics Data System (ADS)

Zu, Yue

Convex optimization problem can be solved in a centralized or distributed manner. Compared with centralized methods based on single-agent system, distributed algorithms rely on multi-agent systems with information exchanging among connected neighbors, which leads to great improvement on the system fault tolerance. Thus, a task within multi-agent system can be completed with presence of partial agent failures. By problem decomposition, a large-scale problem can be divided into a set of small-scale sub-problems that can be solved in sequence/parallel. Hence, the computational complexity is greatly reduced by distributed algorithm in multi-agent system. Moreover, distributed algorithm allows data collected and stored in a distributed fashion, which successfully overcomes the drawbacks of using multicast due to the bandwidth limitation. Distributed algorithm has been applied in solving a variety of real-world problems. Our research focuses on the framework and local optimizer design in practical engineering applications. In the first one, we propose a multi-sensor and multi-agent scheme for spatial motion estimation of a rigid body. Estimation performance is improved in terms of accuracy and convergence speed. Second, we develop a cyber-physical system and implement distributed computation devices to optimize the in-building evacuation path when hazard occurs. The proposed Bellman-Ford Dual-Subgradient path planning method relieves the congestion in corridor and the exit areas. At last, highway traffic flow is managed by adjusting speed limits to minimize the fuel consumption and travel time in the third project. Optimal control strategy is designed through both centralized and distributed algorithm based on convex problem formulation. Moreover, a hybrid control scheme is presented for highway network travel time minimization. Compared with no controlled case or conventional highway traffic control strategy, the proposed hybrid control strategy greatly reduces total travel time on test highway network.

Robot path planning using a genetic algorithm

NASA Technical Reports Server (NTRS)

Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu

1988-01-01

Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.

Scheduling of House Development Projects with CPM and PERT Method for Time Efficiency (Case Study: House Type 36)

NASA Astrophysics Data System (ADS)

Kholil, Muhammad; Nurul Alfa, Bonitasari; Hariadi, Madjumsyah

2018-04-01

Network planning is one of the management techniques used to plan and control the implementation of a project, which shows the relationship between activities. The objective of this research is to arrange network planning on house construction project on CV. XYZ and to know the role of network planning in increasing the efficiency of time so that can be obtained the optimal project completion period. This research uses descriptive method, where the data collected by direct observation to the company, interview, and literature study. The result of this research is optimal time planning in project work. Based on the results of the research, it can be concluded that the use of the both methods in scheduling of house construction project gives very significant effect on the completion time of the project. The company’s CPM (Critical Path Method) method can complete the project with 131 days, PERT (Program Evaluation Review and Technique) Method takes 136 days. Based on PERT calculation obtained Z = -0.66 or 0,2546 (from normal distribution table), and also obtained the value of probability or probability is 74,54%. This means that the possibility of house construction project activities can be completed on time is high enough. While without using both methods the project completion time takes 173 days. So using the CPM method, the company can save time up to 42 days and has time efficiency by using network planning.

MO-F-CAMPUS-T-03: Continuous Dose Delivery with Gamma Knife Perfexion

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

Ghobadi,; Li, W; Chung, C

2015-06-15

Purpose: We propose continuous dose delivery techniques for stereotactic treatments delivered by Gamma Knife Perfexion using inverse treatment planning system that can be applied to various tumour sites in the brain. We test the accuracy of the plans on Perfexion’s planning system (GammaPlan) to ensure the obtained plans are viable. This approach introduces continuous dose delivery for Perefxion, as opposed to the currently employed step-and-shoot approaches, for different tumour sites. Additionally, this is the first realization of automated inverse planning on GammaPlan. Methods: The inverse planning approach is divided into two steps of identifying a quality path inside the target,more » and finding the best collimator composition for the path. To find a path, we select strategic regions inside the target volume and find a path that visits each region exactly once. This path is then passed to a mathematical model which finds the best combination of collimators and their durations. The mathematical model minimizes the dose spillage to the surrounding tissues while ensuring the prescribed dose is delivered to the target(s). Organs-at-risk and their corresponding allowable doses can also be added to the model to protect adjacent organs. Results: We test this approach on various tumour sizes and sites. The quality of the obtained treatment plans are comparable or better than forward plans and inverse plans that use step- and-shoot technique. The conformity indices in the obtained continuous dose delivery plans are similar to those of forward plans while the beam-on time is improved on average (see Table 1 in supporting document). Conclusion: We employ inverse planning for continuous dose delivery in Perfexion for brain tumours. The quality of the obtained plans is similar to forward and inverse plans that use conventional step-and-shoot technique. We tested the inverse plans on GammaPlan to verify clinical relevance. This research was partially supported by Elekta, Sweden (vendor of Gamma Knife Perfexion)« less

Globally Optimal Path Planning with Anisotropic Running Costs

DTIC Science & Technology

2013-03-01

contours were generated on a 3852 Cartesian grid. . . . . . . . . . . . . . . . 33 A1 The N(i, j ) set shown mapped onto Ωh as large dots for the case...function NF(x) near front set as a function of x ∈ Ωh NF(i, j ) near front set as a function of (i, j ) ∈ ΩZh Υ(x) anisotropy function at x δ Cartesian...discriminant of the polynomial p, see Equation (26) argmin argument of the minimum of a function V yz(x, ζ) see Equation (28) T (i, j ) the triplet {(i, j ), V

Path planning during combustion mode switch

DOEpatents

Jiang, Li; Ravi, Nikhil

2015-12-29

Systems and methods are provided for transitioning between a first combustion mode and a second combustion mode in an internal combustion engine. A current operating point of the engine is identified and a target operating point for the internal combustion engine in the second combustion mode is also determined. A predefined optimized transition operating point is selected from memory. While operating in the first combustion mode, one or more engine actuator settings are adjusted to cause the operating point of the internal combustion engine to approach the selected optimized transition operating point. When the engine is operating at the selected optimized transition operating point, the combustion mode is switched from the first combustion mode to the second combustion mode. While operating in the second combustion mode, one or more engine actuator settings are adjusted to cause the operating point of the internal combustion to approach the target operating point.

Robust Flight Path Determination for Mars Precision Landing Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Bayard, David S.; Kohen, Hamid

1997-01-01

This paper documents the application of genetic algorithms (GAs) to the problem of robust flight path determination for Mars precision landing. The robust flight path problem is defined here as the determination of the flight path which delivers a low-lift open-loop controlled vehicle to its desired final landing location while minimizing the effect of perturbations due to uncertainty in the atmospheric model and entry conditions. The genetic algorithm was capable of finding solutions which reduced the landing error from 111 km RMS radial (open-loop optimal) to 43 km RMS radial (optimized with respect to perturbations) using 200 hours of computation on an Ultra-SPARC workstation. Further reduction in the landing error is possible by going to closed-loop control which can utilize the GA optimized paths as nominal trajectories for linearization.

Path to Market for Compact Modular Fusion Power Cores

NASA Astrophysics Data System (ADS)

Woodruff, Simon; Baerny, Jennifer K.; Mattor, Nathan; Stoulil, Don; Miller, Ronald; Marston, Theodore

2012-08-01

The benefits of an energy source whose reactants are plentiful and whose products are benign is hard to measure, but at no time in history has this energy source been more needed. Nuclear fusion continues to promise to be this energy source. However, the path to market for fusion systems is still regularly a matter for long-term (20 + year) plans. This white paper is intended to stimulate discussion of faster commercialization paths, distilling guidance from investors, utilities, and the wider energy research community (including from ARPA-E). There is great interest in a small modular fusion system that can be developed quickly and inexpensively. A simple model shows how compact modular fusion can produce a low cost development path by optimizing traditional systems that burn deuterium and tritium, operating not only at high magnetic field strength, but also by omitting some components that allow for the core to become more compact and easier to maintain. The dominant hurdles to the development of low cost, practical fusion systems are discussed, primarily in terms of the constraints placed on the cost of development stages in the private sector. The main finding presented here is that the bridge from DOE Office of Science to the energy market can come at the Proof of Principle development stage, providing the concept is sufficiently compact and inexpensive that its development allows for a normal technology commercialization path.

In Search of the Optimal Path: How Learners at Task Use an Online Dictionary

ERIC Educational Resources Information Center

Hamel, Marie-Josee

2012-01-01

We have analyzed circa 180 navigation paths followed by six learners while they performed three language encoding tasks at the computer using an online dictionary prototype. Our hypothesis was that learners who follow an "optimal path" while navigating within the dictionary, using its search and look-up functions, would have a high chance of…

Comparative Investigation on Tool Wear during End Milling of AISI H13 Steel with Different Tool Path Strategies

NASA Astrophysics Data System (ADS)

Adesta, Erry Yulian T.; Riza, Muhammad; Avicena

2018-03-01

Tool wear prediction plays a significant role in machining industry for proper planning and control machining parameters and optimization of cutting conditions. This paper aims to investigate the effect of tool path strategies that are contour-in and zigzag tool path strategies applied on tool wear during pocket milling process. The experiments were carried out on CNC vertical machining centre by involving PVD coated carbide inserts. Cutting speed, feed rate and depth of cut were set to vary. In an experiment with three factors at three levels, Response Surface Method (RSM) design of experiment with a standard called Central Composite Design (CCD) was employed. Results obtained indicate that tool wear increases significantly at higher range of feed per tooth compared to cutting speed and depth of cut. This result of this experimental work is then proven statistically by developing empirical model. The prediction model for the response variable of tool wear for contour-in strategy developed in this research shows a good agreement with experimental work.

A systematic FPGA acceleration design for applications based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Dong, Hao; Jiang, Li; Li, Tianjian; Liang, Xiaoyao

2018-04-01

Most FPGA accelerators for convolutional neural network are designed to optimize the inner acceleration and are ignored of the optimization for the data path between the inner accelerator and the outer system. This could lead to poor performance in applications like real time video object detection. We propose a brand new systematic FPFA acceleration design to solve this problem. This design takes the data path optimization between the inner accelerator and the outer system into consideration and optimizes the data path using techniques like hardware format transformation, frame compression. It also takes fixed-point, new pipeline technique to optimize the inner accelerator. All these make the final system's performance very good, reaching about 10 times the performance comparing with the original system.

Systems Engineering Management Plan NASA Traffic Aware Planner Integration Into P-180 Airborne Test-Bed

NASA Technical Reports Server (NTRS)

Maris, John

2015-01-01

NASA's Traffic Aware Planner (TAP) is a cockpit decision support tool that provides aircrew with vertical and lateral flight-path optimizations with the intent of achieving significant fuel and time savings, while automatically avoiding traffic, weather, and restricted airspace conflicts. A key step towards the maturation and deployment of TAP concerned its operational evaluation in a representative flight environment. This Systems Engineering Management Plan (SEMP) addresses the test-vehicle design, systems integration, and flight-test planning for the first TAP operational flight evaluations, which were successfully completed in November 2013. The trial outcomes are documented in the Traffic Aware Planner (TAP) flight evaluation paper presented at the 14th AIAA Aviation Technology, Integration, and Operations Conference, Atlanta, GA. (AIAA-2014-2166, Maris, J. M., Haynes, M. A., Wing, D. J., Burke, K. A., Henderson, J., & Woods, S. E., 2014).

Incentive-Compatible Robust Line Planning

NASA Astrophysics Data System (ADS)

Bessas, Apostolos; Kontogiannis, Spyros; Zaroliagis, Christos

The problem of robust line planning requests for a set of origin-destination paths (lines) along with their frequencies in an underlying railway network infrastructure, which are robust to fluctuations of real-time parameters of the solution. In this work, we investigate a variant of robust line planning stemming from recent regulations in the railway sector that introduce competition and free railway markets, and set up a new application scenario: there is a (potentially large) number of line operators that have their lines fixed and operate as competing entities issuing frequency requests, while the management of the infrastructure itself remains the responsibility of a single entity, the network operator. The line operators are typically unwilling to reveal their true incentives, while the network operator strives to ensure a fair (or socially optimal) usage of the infrastructure, e.g., by maximizing the (unknown to him) aggregate incentives of the line operators.

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

Optimal Propellant Maneuver Flight Demonstrations on ISS

NASA Technical Reports Server (NTRS)

Bhatt, Sagar; Bedrossian, Nazareth; Longacre, Kenneth; Nguyen, Louis

2013-01-01

In this paper, first ever flight demonstrations of Optimal Propellant Maneuver (OPM), a method of propulsive rotational state transition for spacecraft controlled using thrusters, is presented for the International Space Station (ISS). On August 1, 2012, two ISS reorientations of about 180deg each were performed using OPMs. These maneuvers were in preparation for the same-day launch and rendezvous of a Progress vehicle, also a first for ISS visiting vehicles. The first maneuver used 9.7 kg of propellant, whereas the second used 10.2 kg. Identical maneuvers performed without using OPMs would have used approximately 151.1kg and 150.9kg respectively. The OPM method is to use a pre-planned attitude command trajectory to accomplish a rotational state transition. The trajectory is designed to take advantage of the complete nonlinear system dynamics. The trajectory choice directly influences the cost of the maneuver, in this case, propellant. For example, while an eigenaxis maneuver is kinematically the shortest path between two orientations, following that path requires overcoming the nonlinear system dynamics, thereby increasing the cost of the maneuver. The eigenaxis path is used for ISS maneuvers using thrusters. By considering a longer angular path, the path dependence of the system dynamics can be exploited to reduce the cost. The benefits of OPM for the ISS include not only reduced lifetime propellant use, but also reduced loads, erosion, and contamination from thrusters due to fewer firings. Another advantage of the OPM is that it does not require ISS flight software modifications since it is a set of commands tailored to the specific attitude control architecture. The OPM takes advantage of the existing ISS control system architecture for propulsive rotation called USTO control mode1. USTO was originally developed to provide ISS Orbiter stack attitude control capability for a contingency tile-repair scenario, where the Orbiter is maneuvered using its robotic manipulator relative to the ISS. Since 2005 USTO has been used for nominal ISS operations.

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective.

PubMed

Faigl, Jan

2016-01-01

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to "see" the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

PubMed Central

Faigl, Jan

2016-01-01

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning. PMID:27340395

Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization

PubMed Central

Ling, Teresa Wai Ching; Yeung, Wing Kwan

2017-01-01

This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources. PMID:29104748

Optimizing surveillance for livestock disease spreading through animal movements

PubMed Central

Bajardi, Paolo; Barrat, Alain; Savini, Lara; Colizza, Vittoria

2012-01-01

The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems. PMID:22728387

Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization.

PubMed

Lin, Carrie Ka Yuk; Ling, Teresa Wai Ching; Yeung, Wing Kwan

2017-01-01

This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.

Efficiency and robustness of different bus network designs

NASA Astrophysics Data System (ADS)

Pang, John Zhen Fu; Bin Othman, Nasri; Ng, Keng Meng; Monterola, Christopher

2015-07-01

We compare the efficiencies and robustness of four transport networks that can be possibly formed as a result of deliberate city planning. The networks are constructed based on their spatial resemblance to the cities of Manhattan (lattice), Sudan (random), Beijing (single-blob) and Greater Cairo (dual-blob). For a given type, a genetic algorithm is employed to obtain an optimized set of the bus routes. We then simulate how commuter travels using Yen's algorithms for k shortest paths on an adjacency matrix. The cost of traveling such as walking between stations is captured by varying the weighted sums of matrices. We also consider the number of transfers a posteriori by looking at the computed shortest paths. With consideration to distances via radius of gyration, redundancies of travel and number of bus transfers, our simulations indicate that random and dual-blob are more efficient than single-blob and lattice networks. Moreover, dual-blob type is least robust when node removals are targeted but is most resilient when node failures are random. The work hopes to guide and provide technical perspectives on how geospatial distribution of a city limits the optimality of transport designs.

Plan demographics, participants' saving behavior, and target-date fund investments.

PubMed

Park, Youngkyun

2009-05-01

This analysis explores (1) whether plan demographic characteristics would affect individual participant contribution rates and target-date fund investments and (2) equity glide paths for participants in relation to plan demographics by considering target replacement income and its success rate. PLAN DEMOGRAPHIC CHARACTERISTICS IN PARTICIPANT CONTRIBUTION RATES: This study finds empirical evidence that 401(k) plan participants' contribution rates differ by plan demographics based on participants' income and/or tenure. In particular, participants in 401(k) plans dominated by those with low income and short tenure tend to contribute less than those in plans dominated by participants with high income and long tenure. Future research will explore how participant contribution behavior may also be influenced by incentives provided by employers through matching formulae. PLAN DEMOGRAPHIC CHARACTERISTICS IN TARGET-DATE FUND INVESTMENTS: The study also finds empirical evidence that participants' investments in target-date funds with different equity allocations differ by plan demographics based on participants' income and/or tenure. In particular, target-date fund users with 90 percent or more of their account balances in target-date funds who are in 401(k) plans dominated by low-income and short-tenure participants tend to hold target-date funds with lower equity allocations, compared with their counterparts in plans dominated by high-income and long-tenure participants. Future research will focus on the extent to which these characteristics might influence the selection of target-date funds by plan sponsors. EQUITY GLIDE PATHS: Several stylized equity glide paths as well as alternative asset allocations are compared for participants at various starting ages to demonstrate the interaction between plan demographics and equity glide paths/asset allocations in terms of success rates in meeting various replacement income targets. The equity glide path/asset allocation providing the highest success rate at a particular replacement rate target will vary with the assumed starting date of the participant (see Figure 17). Given the highly stylized nature of the simulations in this Issue Brief it is important to note that the results are not intended to provide a single equity glide path solution in relation to plan demographics. Instead, they serve as a framework to be considered when plan sponsors make a selection concerning which target-date funds to include in their plan. IMPORTANCE OF PARTICIPANT CONTRIBUTION RATES: This analysis finds that although target-date funds with different equity glide paths affect the retirement income replacement success rate, participant contribution rates corresponding to different plan demographic characteristics have a stronger impact. AUTO FEATURES OF THE PPA: This Issue Brief provides a stylized study using observed contribution rates as of the 2007 plan year. However, with the passage of the Pension Protection Act of 2006 and its likely impact on plan design in the future (increased utilization of automatic enrollment and automatic contribution escalations), it is likely that contribution rates among the participants may become more homogenous. In such a scenario, it may be more likely that a single equity glide path would meet a wide range of demographic profiles.

On-board autonomous attitude maneuver planning for planetary spacecraft using genetic algorithms

NASA Technical Reports Server (NTRS)

Kornfeld, Richard P.

2003-01-01

A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This paper presents an approach for attitude path planning that makes full use of a priori constraint knowledge and is computationally tractable enough to be executed on-board a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used 'as is' or as an initial solution to initialize additional deterministic optimization algorithms. A number of example simulations are presented including the case examples of a generic Europa Orbiter spacecraft in cruise as well as in orbit around Europa. The search times are typically on the order of minutes, thus demonstrating the viability of the presented approach. The results are applicable to all future deep space missions where greater spacecraft autonomy is required. In addition, onboard autonomous attitude planning greatly facilitates navigation and science observation planning, benefiting thus all missions to planet Earth as well.

Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization

NASA Astrophysics Data System (ADS)

Liu, Zexi

2018-01-01

Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.

SU-F-T-356: DosimetricComparison of VMAT Vs Step and Shoot IMRT Plans for Stage III Lung CancerPatients with Mediastinal Involvement

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

Pearson, D; Bogue, J

Purpose: For Stage III lung cancers that entail treatment of some or all of the mediastinum, anterior-posterior focused Step and Shoot IMRT (SS-IMRT) and VMAT plans have been clinically used to deliver the prescribed dose while working to minimize lung dose and avoid other critical structures. A comparison between the two planning methods was completed to see which treatment method is superior and minimizes dose to healthy lung tissue. Methods: Ten patients who were recently treated with SS-IMRT or VMAT plans for Stage III lung cancer with mediastinal involvement were selected. All patients received a simulation CT for treatment planning,more » as well as a 4D CT and PET/CT fusion for target delineation. Plans were prescribed 6250 cGy in 25 fractions and normalized such that 100% of the prescription dose covered 95% of the PTV. Clinically approved SS-IMRT or VMAT plans were then copied and planned using the alternative modality with identical optimization criteria. SS-IMRT plans utilized seven to nine beams distributed around the patient while the VMAT plans consisted of two full 360 degree arcs. Plans were compared for the lung volume receiving 20 Gy (V20). Results: Both SS-IMRT and VMAT can be used to achieve clinical treatment plans for patients with Stage III Lung cancer with targets encompassing the mediastinum. VMAT plans produced an average V20 of 23.0+/−8.3% and SS-IMRT produced an average of 24.2+/−10.0%. Conclusion: Results indicate that either method can achieve comparable dose distributions, however, VMAT can allow the optimizer to distribute dose over paths of minimal lung tissue and reduce the V20. Therefore, creating a VMAT with constraints identical to an SS-IMRT plan could help to reduce the V20 in clinical treatment plans.« less

FTS evolution

NASA Technical Reports Server (NTRS)

Provost, David E.

1990-01-01

Viewgraphs on flight telerobotic servicer evolution are presented. Topics covered include: paths for FTS evolution; frequently performed actions; primary task states; EPS radiator panel installation; generic task definitions; path planning; non-contact alignment; contact planning and control; and human operator interface.

Bim-Based Indoor Path Planning Considering Obstacles

NASA Astrophysics Data System (ADS)

Xu, M.; Wei, S.; Zlatanova, S.; Zhang, R.

2017-09-01

At present, 87 % of people's activities are in indoor environment; indoor navigation has become a research issue. As the building structures for people's daily life are more and more complex, many obstacles influence humans' moving. Therefore it is essential to provide an accurate and efficient indoor path planning. Nowadays there are many challenges and problems in indoor navigation. Most existing path planning approaches are based on 2D plans, pay more attention to the geometric configuration of indoor space, often ignore rich semantic information of building components, and mostly consider simple indoor layout without taking into account the furniture. Addressing the above shortcomings, this paper uses BIM (IFC) as the input data and concentrates on indoor navigation considering obstacles in the multi-floor buildings. After geometric and semantic information are extracted, 2D and 3D space subdivision methods are adopted to build the indoor navigation network and to realize a path planning that avoids obstacles. The 3D space subdivision is based on triangular prism. The two approaches are verified by the experiments.

Visually based path-planning by Japanese monkeys.

PubMed

Mushiake, H; Saito, N; Sakamoto, K; Sato, Y; Tanji, J

2001-03-01

To construct an animal model of strategy formation, we designed a maze path-finding task. First, we asked monkeys to capture a goal in the maze by moving a cursor on the screen. Cursor movement was linked to movements of each wrist. When the animals learned the association between cursor movement and wrist movement, we established a start and a goal in the maze, and asked them to find a path between them. We found that the animals took the shortest pathway, rather than approaching the goal randomly. We further found that the animals adopted a strategy of selecting a fixed intermediate point in the visually presented maze to select one of the shortest pathways, suggesting a visually based path planning. To examine their capacity to use that strategy flexibly, we transformed the task by blocking pathways in the maze, providing a problem to solve. The animals then developed a strategy of solving the problem by planning a novel shortest path from the start to the goal and rerouting the path to bypass the obstacle.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

PubMed Central

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently. PMID:28255297

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

PubMed

Ni, Jianjun; Wu, Liuying; Shi, Pengfei; Yang, Simon X

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Reasoning on the Self-Organizing Incremental Associative Memory for Online Robot Path Planning

NASA Astrophysics Data System (ADS)

Kawewong, Aram; Honda, Yutaro; Tsuboyama, Manabu; Hasegawa, Osamu

Robot path-planning is one of the important issues in robotic navigation. This paper presents a novel robot path-planning approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN). By the proposed method, an environment is first autonomously divided into a set of path-fragments by junctions. Each fragment is represented by a sequence of preliminarily generated common patterns (CPs). In an online manner, a robot regards the current path as the associative path-fragments, each connected by junctions. The reasoning technique is additionally proposed for decision making at each junction to speed up the exploration time. Distinct from other methods, our method does not ignore the important information about the regions between junctions (path-fragments). The resultant number of path-fragments is also less than other method. Evaluation is done via Webots physical 3D-simulated and real robot experiments, where only distance sensors are available. Results show that our method can represent the environment effectively; it enables the robot to solve the goal-oriented navigation problem in only one episode, which is actually less than that necessary for most of the Reinforcement Learning (RL) based methods. The running time is proved finite and scales well with the environment. The resultant number of path-fragments matches well to the environment.

Optimization of protocol design: a path to efficient, lower cost clinical trial execution

PubMed Central

Malikova, Marina A

2016-01-01

Managing clinical trials requires strategic planning and efficient execution. In order to achieve a timely delivery of important clinical trials’ outcomes, it is useful to establish standardized trial management guidelines and develop robust scoring methodology for evaluation of study protocol complexity. This review will explore the challenges clinical teams face in developing protocols to ensure that the right patients are enrolled and the right data are collected to demonstrate that a drug is safe and efficacious, while managing study costs and study complexity based on proposed comprehensive scoring model. Key factors to consider when developing protocols and techniques to minimize complexity will be discussed. A methodology to identify processes at planning phase, approaches to increase fiscal return and mitigate fiscal compliance risk for clinical trials will be addressed. PMID:28031939

Method of interplanetary trajectory optimization for the spacecraft with low thrust and swing-bys

NASA Astrophysics Data System (ADS)

Konstantinov, M. S.; Thein, M.

2017-07-01

The method developed to avoid the complexity of solving the multipoint boundary value problem while optimizing interplanetary trajectories of the spacecraft with electric propulsion and a sequence of swing-bys is presented in the paper. This method is based on the use of the preliminary problem solutions for the impulsive trajectories. The preliminary problem analyzed at the first stage of the study is formulated so that the analysis and optimization of a particular flight path is considered as the unconstrained minimum in the space of the selectable parameters. The existing methods can effectively solve this problem and make it possible to identify rational flight paths (the sequence of swing-bys) to receive the initial approximation for the main characteristics of the flight path (dates, values of the hyperbolic excess velocity, etc.). These characteristics can be used to optimize the trajectory of the spacecraft with electric propulsion. The special feature of the work is the introduction of the second (intermediate) stage of the research. At this stage some characteristics of the analyzed flight path (e.g. dates of swing-bys) are fixed and the problem is formulated so that the trajectory of the spacecraft with electric propulsion is optimized on selected sites of the flight path. The end-to-end optimization is carried out at the third (final) stage of the research. The distinctive feature of this stage is the analysis of the full set of optimal conditions for the considered flight path. The analysis of the characteristics of the optimal flight trajectories to Jupiter with Earth, Venus and Mars swing-bys for the spacecraft with electric propulsion are presented. The paper shows that the spacecraft weighing more than 7150 kg can be delivered into the vicinity of Jupiter along the trajectory with two Earth swing-bys by use of the space transportation system based on the "Angara A5" rocket launcher, the chemical upper stage "KVTK" and the electric propulsion system with input electrical power of 100 kW.

Optimization of magnet end-winding geometry

NASA Astrophysics Data System (ADS)

Reusch, Michael F.; Weissenburger, Donald W.; Nearing, James C.

1994-03-01

A simple, almost entirely analytic, method for the optimization of stress-reduced magnet-end winding paths for ribbon-like superconducting cable is presented. This technique is based on characterization of these paths as developable surfaces, i.e., surfaces whose intrinsic geometry is flat. The method is applicable to winding mandrels of arbitrary geometry. Computational searches for optimal winding paths are easily implemented via the technique. Its application to the end configuration of cylindrical Superconducting Super Collider (SSC)-type magnets is discussed. The method may be useful for other engineering problems involving the placement of thin sheets of material.

Project Scheduling Based on Risk of Gas Transmission Pipe

NASA Astrophysics Data System (ADS)

Silvianita; Nurbaity, A.; Mulyadi, Y.; Suntoyo; Chamelia, D. M.

2018-03-01

The planning of a project has a time limit on which must be completed before or right at a predetermined time. Thus, in a project planning, it is necessary to have scheduling management that is useful for completing a project to achieve maximum results by considering the constraints that will exists. Scheduling management is undertaken to deal with uncertainties and negative impacts of time and cost in project completion. This paper explains about scheduling management in gas transmission pipeline project Gresik-Semarang to find out which scheduling plan is most effectively used in accordance with its risk value. Scheduling management in this paper is assissted by Microsoft Project software to find the critical path of existing project scheduling planning data. Critical path is the longest scheduling path with the fastest completion time. The result is found a critical path on project scheduling with completion time is 152 days. Furthermore, the calculation of risk is done by using House of Risk (HOR) method and it is found that the critical path has a share of 40.98 percent of all causes of the occurence of risk events that will be experienced.

UAVSAR Flight-Planning System

NASA Technical Reports Server (NTRS)

2008-01-01

A system of software partly automates planning of a flight of the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) -- a polarimetric synthetic-aperture radar system aboard an unpiloted or minimally piloted airplane. The software constructs a flight plan that specifies not only the intended flight path but also the setup of the radar system at each point along the path.

Distributed Method to Optimal Profile Descent

NASA Astrophysics Data System (ADS)

Kim, Geun I.

Current ground automation tools for Optimal Profile Descent (OPD) procedures utilize path stretching and speed profile change to maintain proper merging and spacing requirements at high traffic terminal area. However, low predictability of aircraft's vertical profile and path deviation during decent add uncertainty to computing estimated time of arrival, a key information that enables the ground control center to manage airspace traffic effectively. This paper uses an OPD procedure that is based on a constant flight path angle to increase the predictability of the vertical profile and defines an OPD optimization problem that uses both path stretching and speed profile change while largely maintaining the original OPD procedure. This problem minimizes the cumulative cost of performing OPD procedures for a group of aircraft by assigning a time cost function to each aircraft and a separation cost function to a pair of aircraft. The OPD optimization problem is then solved in a decentralized manner using dual decomposition techniques under inter-aircraft ADS-B mechanism. This method divides the optimization problem into more manageable sub-problems which are then distributed to the group of aircraft. Each aircraft solves its assigned sub-problem and communicate the solutions to other aircraft in an iterative process until an optimal solution is achieved thus decentralizing the computation of the optimization problem.

Grasp planning under uncertainty

NASA Technical Reports Server (NTRS)

Erkmen, A. M.; Stephanou, H. E.

1989-01-01

The planning of dexterous grasps for multifingered robot hands operating in uncertain environments is covered. A sensor-based approach to the planning of a reach path prior to grasping is first described. An on-line, joint space finger path planning algorithm for the enclose phase of grasping was then developed. The algorithm minimizes the impact momentum of the hand. It uses a Preshape Jacobian matrix to map task-level hand preshape requirements into kinematic constraints. A master slave scheme avoids inter-finger collisions and reduces the dimensionality of the planning problem.

Planning paths to multiple targets: memory involvement and planning heuristics in spatial problem solving.

PubMed

Wiener, J M; Ehbauer, N N; Mallot, H A

2009-09-01

For large numbers of targets, path planning is a complex and computationally expensive task. Humans, however, usually solve such tasks quickly and efficiently. We present experiments studying human path planning performance and the cognitive processes and heuristics involved. Twenty-five places were arranged on a regular grid in a large room. Participants were repeatedly asked to solve traveling salesman problems (TSP), i.e., to find the shortest closed loop connecting a start location with multiple target locations. In Experiment 1, we tested whether humans employed the nearest neighbor (NN) strategy when solving the TSP. Results showed that subjects outperform the NN-strategy, suggesting that it is not sufficient to explain human route planning behavior. As a second possible strategy we tested a hierarchical planning heuristic in Experiment 2, demonstrating that participants first plan a coarse route on the region level that is refined during navigation. To test for the relevance of spatial working memory (SWM) and spatial long-term memory (LTM) for planning performance and the planning heuristics applied, we varied the memory demands between conditions in Experiment 2. In one condition the target locations were directly marked, such that no memory was required; a second condition required participants to memorize the target locations during path planning (SWM); in a third condition, additionally, the locations of targets had to retrieved from LTM (SWM and LTM). Results showed that navigation performance decreased with increasing memory demands while the dependence on the hierarchical planning heuristic increased.

System and Method for Aiding Pilot Preview, Rehearsal, Review, and Real-Time Visual Acquisition of Flight Mission Progress

NASA Technical Reports Server (NTRS)

Prinzel, III, Lawrence J. (Inventor); Pope, Alan T. (Inventor); Williams, Steven P. (Inventor); Bailey, Randall E. (Inventor); Arthur, Jarvis J. (Inventor); Kramer, Lynda J. (Inventor); Schutte, Paul C. (Inventor)

2012-01-01

Embodiments of the invention permit flight paths (current and planned) to be viewed from various orientations to provide improved path and terrain awareness via graphical two-dimensional or three-dimensional perspective display formats. By coupling the flight path information with a terrain database, uncompromising terrain awareness relative to the path and ownship is provided. In addition, missed approaches, path deviations, and any navigational path can be reviewed and rehearsed before performing the actual task. By rehearsing a particular mission, check list items can be reviewed, terrain awareness can be highlighted, and missed approach procedures can be discussed by the flight crew. Further, the use of Controller Pilot Datalink Communications enables data-linked path, flight plan changes, and Air Traffic Control requests to be integrated into the flight display of the present invention.

The approach for shortest paths in fire succor based on component GIS technology

NASA Astrophysics Data System (ADS)

Han, Jie; Zhao, Yong; Dai, K. W.

2007-06-01

Fire safety is an important issue for the national economy and people's living. Efficiency and exactness of fire department succor directly relate to safety of peoples' lives and property. Many disadvantages of the traditional fire system have been emerged in practical applications. The preparation of pumpers is guided by wireless communication or wire communication, so its real-time and accurate performances are much poorer. The information about the reported fire, such as the position, disaster and map, et al., for alarm and command was processed by persons, which slows the reaction speed and delays the combat opportunity. In order to solve these disadvantages, it has an important role to construct a modern fire command center based on high technology. The construction of modern fire command center can realize the modernization and automation of fire command and management. It will play a great role in protecting safety of peoples' lives and property. The center can enhance battle ability and can reduce the direct and indirect loss of fire damage at most. With the development of science technology, Geographic Information System (GIS) has becoming a new information industry for hardware production, software development, data collection, space analysis and counseling. With the popularization of computers and the development of GIS, GIS has gained increasing broad applications for its strong functionality. Network analysis is one of the most important functions of GIS, and the most elementary and pivotal issue of network analysis is the calculation of shortest paths. The shortest paths are mostly applied to some emergent systems such as 119 fire alarms. These systems mainly require that the computation time of the optimal path should be 1-3 seconds. And during traveling, the next running path of the vehicles should be calculated in time. So the implement of the shortest paths must have a high efficiency. In this paper, the component GIS technology was applied to collect and record the data information (such as, the situation of this disaster, map and road status et al) of the reported fire firstly. The ant colony optimization was used to calculate the shortest path of fire succor secondly. The optimization results were sent to the pumpers, which can let pumpers choose the shortest paths intelligently and come to fire position with least time. The programming method for shortest paths is proposed in section 3. There are three parts in this section. The elementary framework of the proposed programming method is presented in part one. The systematic framework of GIS component is described in part two. The ant colony optimization employed is presented in part three. In section 4, a simple application instance was presented to demonstrate the proposed programming method. There are three parts in this section. The distributed Web application based on component GIS was described in part one. The optimization results without traffic constraint were presented in part two. The optimization results with traffic constraint were presented in part three. The contributions of this paper can be summarized as follows. (1) It proposed an effective approach for shortest paths in fire succor based on component GIS technology. This proposed approach can achieve the real-time decisions of shortest paths for fire succor. (2) It applied the ant colony optimization to implement the shortest path decision. The traffic information was considered in the shortest path decision using ant colony optimization. The final application instance suggests that the proposed approach is feasible, correct and valid.

Neighboring extremals of dynamic optimization problems with path equality constraints

NASA Technical Reports Server (NTRS)

Lee, A. Y.

1988-01-01

Neighboring extremals of dynamic optimization problems with path equality constraints and with an unknown parameter vector are considered in this paper. With some simplifications, the problem is reduced to solving a linear, time-varying two-point boundary-value problem with integral path equality constraints. A modified backward sweep method is used to solve this problem. Two example problems are solved to illustrate the validity and usefulness of the solution technique.

Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands.

PubMed

Popoola, Segun I; Atayero, Aderemi A; Faruk, Nasir

2018-02-01

The behaviour of radio wave signals in a wireless channel depends on the local terrain profile of the propagation environments. In view of this, Received Signal Strength (RSS) of transmitted signals are measured at different points in space for radio network planning and optimization. However, these important data are often not publicly available for wireless channel characterization and propagation model development. In this data article, RSS data of a commercial base station operating at 900 and 1800 MHz were measured along three different routes of Lagos-Badagry Highway, Nigeria. In addition, local terrain profile data of the study area (terrain elevation, clutter height, altitude, and the distance of the mobile station from the base station) are extracted from Digital Terrain Map (DTM) to account for the unique environmental features. Statistical analyses and probability distributions of the RSS data are presented in tables and graphs. Furthermore, the degree of correlations (and the corresponding significance) between the RSS and the local terrain parameters were computed and analyzed for proper interpretations. The data provided in this article will help radio network engineers to: predict signal path loss; estimate radio coverage; efficiently reuse limited frequencies; avoid interferences; optimize handover; and adjust transmitted power level.

Analytical and numerical analysis of inverse optimization problems: conditions of uniqueness and computational methods

PubMed Central

Zatsiorsky, Vladimir M.

2011-01-01

One of the key problems of motor control is the redundancy problem, in particular how the central nervous system (CNS) chooses an action out of infinitely many possible. A promising way to address this question is to assume that the choice is made based on optimization of a certain cost function. A number of cost functions have been proposed in the literature to explain performance in different motor tasks: from force sharing in grasping to path planning in walking. However, the problem of uniqueness of the cost function(s) was not addressed until recently. In this article, we analyze two methods of finding additive cost functions in inverse optimization problems with linear constraints, so-called linear-additive inverse optimization problems. These methods are based on the Uniqueness Theorem for inverse optimization problems that we proved recently (Terekhov et al., J Math Biol 61(3):423–453, 2010). Using synthetic data, we show that both methods allow for determining the cost function. We analyze the influence of noise on the both methods. Finally, we show how a violation of the conditions of the Uniqueness Theorem may lead to incorrect solutions of the inverse optimization problem. PMID:21311907

Robot path planning algorithm based on symbolic tags in dynamic environment

NASA Astrophysics Data System (ADS)

Vokhmintsev, A.; Timchenko, M.; Melnikov, A.; Kozko, A.; Makovetskii, A.

2017-09-01

The present work will propose a new heuristic algorithms for path planning of a mobile robot in an unknown dynamic space that have theoretically approved estimates of computational complexity and are approbated for solving specific applied problems.

Managing search complexity in linguistic geometry.

PubMed

Stilman, B

1997-01-01

This paper is a new step in the development of linguistic geometry. This formal theory is intended to discover and generalize the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper, we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing linguistic geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in the paper on two pilot examples of the solution of complex optimization problems. The first example is a problem of strategic planning for the air combat, in which concurrent actions of four vehicles are simulated as serial interleaving moves. The second example is a problem of strategic planning for the space comb of eight autonomous vehicles (with interleaving moves) that requires generation of the search tree of the depth 25 with the branching factor 30. This is beyond the capabilities of modern and conceivable future computers (employing conventional approaches). In both examples the linguistic geometry tools showed deep and highly selective searches in comparison with conventional search algorithms. For the first example a sketch of the proof of optimality of the solution is considered.

The study of optimization on process parameters of high-accuracy computerized numerical control polishing

NASA Astrophysics Data System (ADS)

Huang, Wei-Ren; Huang, Shih-Pu; Tsai, Tsung-Yueh; Lin, Yi-Jyun; Yu, Zong-Ru; Kuo, Ching-Hsiang; Hsu, Wei-Yao; Young, Hong-Tsu

2017-09-01

Spherical lenses lead to forming spherical aberration and reduced optical performance. Consequently, in practice optical system shall apply a combination of spherical lenses for aberration correction. Thus, the volume of the optical system increased. In modern optical systems, aspherical lenses have been widely used because of their high optical performance with less optical components. However, aspherical surfaces cannot be fabricated by traditional full aperture polishing process due to their varying curvature. Sub-aperture computer numerical control (CNC) polishing is adopted for aspherical surface fabrication in recent years. By using CNC polishing process, mid-spatial frequency (MSF) error is normally accompanied during this process. And the MSF surface texture of optics decreases the optical performance for high precision optical system, especially for short-wavelength applications. Based on a bonnet polishing CNC machine, this study focuses on the relationship between MSF surface texture and CNC polishing parameters, which include feed rate, head speed, track spacing and path direction. The power spectral density (PSD) analysis is used to judge the MSF level caused by those polishing parameters. The test results show that controlling the removal depth of single polishing path, through the feed rate, and without same direction polishing path for higher total removal depth can efficiently reduce the MSF error. To verify the optical polishing parameters, we divided a correction polishing process to several polishing runs with different direction polishing paths. Compare to one shot polishing run, multi-direction path polishing plan could produce better surface quality on the optics.

Expert system for neurosurgical treatment planning

NASA Astrophysics Data System (ADS)

Cheng, Andrew Y. S.; Chung, Sally S. Y.; Kwok, John C. K.

1996-04-01

A specially designed expert system is in development for neurosurgical treatment planning. The knowledge base contains knowledge and experiences on neurosurgical treatment planning from neurosurgeon consultants, who also determine the risks of different regions in human brains. When completed, the system can simulate the decision making process of neurosurgeons to determine the safest probing path for operation. The Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scan images for each patient are grabbed as the input. The system also allows neurosurgeons to include for any particular patient the additional information, such as how the tumor affects its neighboring functional regions, which is also important for calculating the safest probing path. It can then consider all the relevant information and find the most suitable probing path on the patient's brain. A 3D brain model is constructed for each set of the CT/MRI scan images and is displayed real-time together with the possible probing paths found. The precise risk value of each path is shown as a number between 0 and 1, together with its possible damages in text. Neurosurgeons can view more than one possible path simultaneously, and make the final decision on the selected path for operation.

Extended shortest path selection for package routing of complex networks

NASA Astrophysics Data System (ADS)

Ye, Fan; Zhang, Lei; Wang, Bing-Hong; Liu, Lu; Zhang, Xing-Yi

The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.

Simulation based optimized beam velocity in additive manufacturing

NASA Astrophysics Data System (ADS)

Vignat, Frédéric; Béraud, Nicolas; Villeneuve, François

2017-08-01

Manufacturing good parts with additive technologies rely on melt pool dimension and temperature and are controlled by manufacturing strategies often decided on machine side. Strategies are built on beam path and variable energy input. Beam path are often a mix of contour and hatching strategies filling the contours at each slice. Energy input depend on beam intensity and speed and is determined from simple thermal models to control melt pool dimensions and temperature and ensure porosity free material. These models take into account variation in thermal environment such as overhanging surfaces or back and forth hatching path. However not all the situations are correctly handled and precision is limited. This paper proposes new method to determine energy input from full built chamber 3D thermal simulation. Using the results of the simulation, energy is modified to keep melt pool temperature in a predetermined range. The paper present first an experimental method to determine the optimal range of temperature. In a second part the method to optimize the beam speed from the simulation results is presented. Finally, the optimized beam path is tested in the EBM machine and built part are compared with part built with ordinary beam path.

Generalized gradient algorithm for trajectory optimization

NASA Technical Reports Server (NTRS)

Zhao, Yiyuan; Bryson, A. E.; Slattery, R.

1990-01-01

The generalized gradient algorithm presented and verified as a basis for the solution of trajectory optimization problems improves the performance index while reducing path equality constraints, and terminal equality constraints. The algorithm is conveniently divided into two phases, of which the first, 'feasibility' phase yields a solution satisfying both path and terminal constraints, while the second, 'optimization' phase uses the results of the first phase as initial guesses.

A Method to Analyze and Optimize the Load Sharing of Split Path Transmissions

NASA Technical Reports Server (NTRS)

Krantz, Timothy L.

1996-01-01

Split-path transmissions are promising alternatives to the common planetary transmissions for rotorcraft. Heretofore, split-path designs proposed for or used in rotorcraft have featured load-sharing devices that add undesirable weight and complexity to the designs. A method was developed to analyze and optimize the load sharing in split-path transmissions without load-sharing devices. The method uses the clocking angle as a design parameter to optimize for equal load sharing. In addition, the clocking angle tolerance necessary to maintain acceptable load sharing can be calculated. The method evaluates the effects of gear-shaft twisting and bending, tooth bending, Hertzian deformations within bearings, and movement of bearing supports on load sharing. It was used to study the NASA split-path test gearbox and the U.S. Army's Comanche helicopter main rotor gearbox. Acceptable load sharing was found to be achievable and maintainable by using proven manufacturing processes. The analytical results compare favorably to available experimental data.

Path planning for planetary rover using extended elevation map

NASA Technical Reports Server (NTRS)

Nakatani, Ichiro; Kubota, Takashi; Yoshimitsu, Tetsuo

1994-01-01

This paper describes a path planning method for planetary rovers to search for paths on planetary surfaces. The planetary rover is required to travel safely over a long distance for many days over unfamiliar terrain. Hence it is very important how planetary rovers process sensory information in order to understand the planetary environment and to make decisions based on that information. As a new data structure for informational mapping, an extended elevation map (EEM) has been introduced, which includes the effect of the size of the rover. The proposed path planning can be conducted in such a way as if the rover were a point while the size of the rover is automatically taken into account. The validity of the proposed methods is verified by computer simulations.

Autonomous Path Planning for On-Orbit Servicing Vehicles

NASA Astrophysics Data System (ADS)

McInnes, C. R.

On-orbit servicing has long been considered as a means of reducing mission costs. While automated on-orbit servicing of satellites in LEO and GEO has yet to be realised, the International Space Station (ISS) will require servicing in a number of forms for re-supply, external visual inspection and maintenance. This paper will discuss a unified approach to path planning for such servicing vehicles using artificial potential field methods. In particular, path constrained rendezvous and docking of the ESA Automated Transfer Vehicle (ATV) at the ISS will be investigated as will mission and path planning tools for the Daimler-Chrysler Aerospace ISS Inspector free-flying camera. Future applications for free-flying microcameras and co-operative control between multiple free-flyers for on-orbit assembly will also be considered.

Path-following control of wheeled planetary exploration robots moving on deformable rough terrain.

PubMed

Ding, Liang; Gao, Hai-bo; Deng, Zong-quan; Li, Zhijun; Xia, Ke-rui; Duan, Guang-ren

2014-01-01

The control of planetary rovers, which are high performance mobile robots that move on deformable rough terrain, is a challenging problem. Taking lateral skid into account, this paper presents a rough terrain model and nonholonomic kinematics model for planetary rovers. An approach is proposed in which the reference path is generated according to the planned path by combining look-ahead distance and path updating distance on the basis of the carrot following method. A path-following strategy for wheeled planetary exploration robots incorporating slip compensation is designed. Simulation results of a four-wheeled robot on deformable rough terrain verify that it can be controlled to follow a planned path with good precision, despite the fact that the wheels will obviously skid and slip.

Path-Following Control of Wheeled Planetary Exploration Robots Moving on Deformable Rough Terrain

PubMed Central

Ding, Liang; Gao, Hai-bo; Deng, Zong-quan; Li, Zhijun; Xia, Ke-rui; Duan, Guang-ren

2014-01-01

The control of planetary rovers, which are high performance mobile robots that move on deformable rough terrain, is a challenging problem. Taking lateral skid into account, this paper presents a rough terrain model and nonholonomic kinematics model for planetary rovers. An approach is proposed in which the reference path is generated according to the planned path by combining look-ahead distance and path updating distance on the basis of the carrot following method. A path-following strategy for wheeled planetary exploration robots incorporating slip compensation is designed. Simulation results of a four-wheeled robot on deformable rough terrain verify that it can be controlled to follow a planned path with good precision, despite the fact that the wheels will obviously skid and slip. PMID:24790582

Singular-Arc Time-Optimal Trajectory of Aircraft in Two-Dimensional Wind Field

NASA Technical Reports Server (NTRS)

Nguyen, Nhan

2006-01-01

This paper presents a study of a minimum time-to-climb trajectory analysis for aircraft flying in a two-dimensional altitude dependent wind field. The time optimal control problem possesses a singular control structure when the lift coefficient is taken as a control variable. A singular arc analysis is performed to obtain an optimal control solution on the singular arc. Using a time-scale separation with the flight path angle treated as a fast state, the dimensionality of the optimal control solution is reduced by eliminating the lift coefficient control. A further singular arc analysis is used to decompose the original optimal control solution into the flight path angle solution and a trajectory solution as a function of the airspeed and altitude. The optimal control solutions for the initial and final climb segments are computed using a shooting method with known starting values on the singular arc The numerical results of the shooting method show that the optimal flight path angle on the initial and final climb segments are constant. The analytical approach provides a rapid means for analyzing a time optimal trajectory for aircraft performance.

Online POMDP Algorithms for Very Large Observation Spaces

DTIC Science & Technology

2017-06-06

stochastic optimization: From sets to paths." In Advances in Neural Information Processing Systems, pp. 1585- 1593 . 2015. • Luo, Yuanfu, Haoyu Bai...and Wee Sun Lee. "Adaptive stochastic optimization: From sets to paths." In Advances in Neural Information Processing Systems, pp. 1585- 1593 . 2015

Routing and spectrum assignment based on ant colony optimization of minimum consecutiveness loss in elastic optical networks

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Xin, Xiangjun; Tian, Qinghua; Zhang, Qi; Rao, Lan; Tian, Feng; Luo, Biao; Liu, Yingjun; Tang, Bao

2016-10-01

Elastic Optical Networks are considered to be a promising technology for future high-speed network. In this paper, we propose a RSA algorithm based on the ant colony optimization of minimum consecutiveness loss (ACO-MCL). Based on the effect of the spectrum consecutiveness loss on the pheromone in the ant colony optimization, the path and spectrum of the minimal impact on the network are selected for the service request. When an ant arrives at the destination node from the source node along a path, we assume that this path is selected for the request. We calculate the consecutiveness loss of candidate-neighbor link pairs along this path after the routing and spectrum assignment. Then, the networks update the pheromone according to the value of the consecutiveness loss. We save the path with the smallest value. After multiple iterations of the ant colony optimization, the final selection of the path is assigned for the request. The algorithms are simulated in different networks. The results show that ACO-MCL algorithm performs better in blocking probability and spectrum efficiency than other algorithms. Moreover, the ACO-MCL algorithm can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness. Compared with other algorithms, the ACO-MCL algorithm can reduce the blocking rate by at least 5.9% in heavy load.

Sensory Agreement Guides Kinetic Energy Optimization of Arm Movements during Object Manipulation.

PubMed

Farshchiansadegh, Ali; Melendez-Calderon, Alejandro; Ranganathan, Rajiv; Murphey, Todd D; Mussa-Ivaldi, Ferdinando A

2016-04-01

The laws of physics establish the energetic efficiency of our movements. In some cases, like locomotion, the mechanics of the body dominate in determining the energetically optimal course of action. In other tasks, such as manipulation, energetic costs depend critically upon the variable properties of objects in the environment. Can the brain identify and follow energy-optimal motions when these motions require moving along unfamiliar trajectories? What feedback information is required for such optimal behavior to occur? To answer these questions, we asked participants to move their dominant hand between different positions while holding a virtual mechanical system with complex dynamics (a planar double pendulum). In this task, trajectories of minimum kinetic energy were along curvilinear paths. Our findings demonstrate that participants were capable of finding the energy-optimal paths, but only when provided with veridical visual and haptic information pertaining to the object, lacking which the trajectories were executed along rectilinear paths.

Optimism and depression: a new look at social support as a mediator among women at risk for breast cancer.

PubMed

Garner, Melissa J; McGregor, Bonnie A; Murphy, Karly M; Koenig, Alex L; Dolan, Emily D; Albano, Denise

2015-12-01

Breast cancer risk is a chronic stressor associated with depression. Optimism is associated with lower levels of depression among breast cancer survivors. However, to our knowledge, no studies have explored the relationship between optimism and depression among women at risk for breast cancer. We hypothesized that women at risk for breast cancer who have higher levels of optimism would report lower levels of depression and that social support would mediate this relationship. Participants (N = 199) with elevated distress were recruited from the community and completed self-report measures of depression, optimism, and social support. Participants were grouped based on their family history of breast cancer. Path analysis was used to examine the cross-sectional relationship between optimism, social support, and depressive symptoms in each group. Results indicated that the variance in depressive symptoms was partially explained through direct paths from optimism and social support among women with a family history of breast cancer. The indirect path from optimism to depressive symptoms via social support was significant (β = -.053; 90% CI = -.099 to -.011, p = .037) in this group. However, among individuals without a family history of breast cancer, the indirect path from optimism to depressive symptoms via social support was not significant. These results suggest that social support partially mediates the relationship between optimism and depression among women at risk for breast cancer. Social support may be an important intervention target to reduce depression among women at risk for breast cancer. Copyright © 2015 John Wiley & Sons, Ltd.

An improved hierarchical A * algorithm in the optimization of parking lots

NASA Astrophysics Data System (ADS)

Wang, Yong; Wu, Junjuan; Wang, Ying

2017-08-01

In the parking lot parking path optimization, the traditional evaluation index is the shortest distance as the best index and it does not consider the actual road conditions. Now, the introduction of a more practical evaluation index can not only simplify the hardware design of the boot system but also save the software overhead. Firstly, we establish the parking lot network graph RPCDV mathematical model and all nodes in the network is divided into two layers which were constructed using different evaluation function base on the improved hierarchical A * algorithm which improves the time optimal path search efficiency and search precision of the evaluation index. The final results show that for different sections of the program attribute parameter algorithm always faster the time to find the optimal path.

Robot Path Planning in Uncertain Environments: A Language-Measure-Theoretic Approach

DTIC Science & Technology

2015-03-01

in the framework of probabilistic finite state automata (PFSA) and language measure from a control-theoretic perspective. The proposed concept has been...DOI: 10.1115/1.4027876] Keywords: path planning, language measure, probabilistic finite state automata 1 Motivation and Introduction In general

Cross-Polar Aircraft Trajectory Optimization and the Potential Climate Impact

NASA Technical Reports Server (NTRS)

Ng, Hok K.; Sridhar, Banavar; Grabbe, Shon; Chen, Neil

2011-01-01

Cross-Polar routes offer new opportunities for air travel markets. Transpolar flights reduce travel times, fuel burns, and associated environmental emissions by flying direct paths between many North American and Asian cities. This study evaluates the potential benefits of flying wind-optimal polar routes and assessed their potential impact on climate change. An optimization algorithm is developed for transpolar flights to generate wind-optimal trajectories that minimize climate impact of aircraft, in terms of global warming potentials (relative to warming by one kg of CO2) of several types of emissions, while avoiding regions of airspace that facilitate persistent contrail formation. Estimations of global warming potential are incorporated into the objective function of the optimization algorithm to assess the climate impact of aircraft emissions discharged at a given location and altitude. The regions of airspace with very low ambient temperature and areas favorable to persistent contrail formation are modeled as undesirable regions that aircraft should avoid and are formulated as soft state constraints. The fuel burn and climate impact of cross-polar air traffic flying various types of trajectory including flight plan, great circle, wind-optimal, and contrail-avoidance are computed for 15 origin-destination pairs between major international airports in the U.S. and Asia. Wind-optimal routes reduce average fuel burn of flight plan routes by 4.4% on December 4, 2010 and 8.0% on August 7, 2010, respectively. The tradeoff between persistent contrail formation and additional global warming potential of aircraft emissions is investigated with and without altitude optimization. Without altitude optimization, the reduction in contrail travel times is gradual with increase in total fuel consumption. When altitude is optimized, a one percent increase in additional global warming potential, a climate impact equivalent to that of 4070kg and 4220kg CO2 emission, reduces 135 and 105 minutes persistent contrail formation per flight during a day with medium and high contrail formation, respectively.

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †

PubMed Central

Zhang, Meiyan; Zheng, Yahong Rosa

2017-01-01

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X−Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem. PMID:28696377

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †.

PubMed

Cai, Wenyu; Zhang, Meiyan; Zheng, Yahong Rosa

2017-07-11

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X - Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

NASA Astrophysics Data System (ADS)

Fronita, Mona; Gernowo, Rahmat; Gunawan, Vincencius

2018-02-01

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

A self-optimizing scheme for energy balanced routing in Wireless Sensor Networks using SensorAnt.

PubMed

Shamsan Saleh, Ahmed M; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A; Ismail, Alyani

2012-01-01

Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy. The routing protocol should be able to provide uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSNs which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensor nodes. This method is based on the Ant Colony Optimization (ACO) metaheuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network. This method also distributes the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

Joint Chance-Constrained Dynamic Programming

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J. Bob

2012-01-01

This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step.

Inexact trajectory planning and inverse problems in the Hamilton–Pontryagin framework

PubMed Central

Burnett, Christopher L.; Holm, Darryl D.; Meier, David M.

2013-01-01

We study a trajectory-planning problem whose solution path evolves by means of a Lie group action and passes near a designated set of target positions at particular times. This is a higher-order variational problem in optimal control, motivated by potential applications in computational anatomy and quantum control. Reduction by symmetry in such problems naturally summons methods from Lie group theory and Riemannian geometry. A geometrically illuminating form of the Euler–Lagrange equations is obtained from a higher-order Hamilton–Pontryagin variational formulation. In this context, the previously known node equations are recovered with a new interpretation as Legendre–Ostrogradsky momenta possessing certain conservation properties. Three example applications are discussed as well as a numerical integration scheme that follows naturally from the Hamilton–Pontryagin principle and preserves the geometric properties of the continuous-time solution. PMID:24353467

Attention control learning in the decision space using state estimation

NASA Astrophysics Data System (ADS)

Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

2016-05-01

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

Automated flight path planning for virtual endoscopy.

PubMed

Paik, D S; Beaulieu, C F; Jeffrey, R B; Rubin, G D; Napel, S

1998-05-01

In this paper, a novel technique for rapid and automatic computation of flight paths for guiding virtual endoscopic exploration of three-dimensional medical images is described. While manually planning flight paths is a tedious and time consuming task, our algorithm is automated and fast. Our method for positioning the virtual camera is based on the medial axis transform but is much more computationally efficient. By iteratively correcting a path toward the medial axis, the necessity of evaluating simple point criteria during morphological thinning is eliminated. The virtual camera is also oriented in a stable viewing direction, avoiding sudden twists and turns. We tested our algorithm on volumetric data sets of eight colons, one aorta and one bronchial tree. The algorithm computed the flight paths in several minutes per volume on an inexpensive workstation with minimal computation time added for multiple paths through branching structures (10%-13% per extra path). The results of our algorithm are smooth, centralized paths that aid in the task of navigation in virtual endoscopic exploration of three-dimensional medical images.

Comparison of tablet-based strategies for incision planning in laser microsurgery

NASA Astrophysics Data System (ADS)

Schoob, Andreas; Lekon, Stefan; Kundrat, Dennis; Kahrs, Lüder A.; Mattos, Leonardo S.; Ortmaier, Tobias

2015-03-01

Recent research has revealed that incision planning in laser surgery deploying stylus and tablet outperforms state-of-the-art micro-manipulator-based laser control. Providing more detailed quantitation regarding that approach, a comparative study of six tablet-based strategies for laser path planning is presented. Reference strategy is defined by monoscopic visualization and continuous path drawing on a graphics tablet. Further concepts deploying stereoscopic or a synthesized laser view, point-based path definition, real-time teleoperation or a pen display are compared with the reference scenario. Volunteers were asked to redraw and ablate stamped lines on a sample. Performance is assessed by measuring planning accuracy, completion time and ease of use. Results demonstrate that significant differences exist between proposed concepts. The reference strategy provides more accurate incision planning than the stereo or laser view scenario. Real-time teleoperation performs best with respect to completion time without indicating any significant deviation in accuracy and usability. Point-based planning as well as the pen display provide most accurate planning and increased ease of use compared to the reference strategy. As a result, combining the pen display approach with point-based planning has potential to become a powerful strategy because of benefiting from improved hand-eye-coordination on the one hand and from a simple but accurate technique for path definition on the other hand. These findings as well as the overall usability scale indicating high acceptance and consistence of proposed strategies motivate further advanced tablet-based planning in laser microsurgery.

Automatic Operation For A Robot Lawn Mower

NASA Astrophysics Data System (ADS)

Huang, Y. Y.; Cao, Z. L.; Oh, S. J.; Kattan, E. U.; Hall, E. L.

1987-02-01

A domestic mobile robot, lawn mower, which performs the automatic operation mode, has been built up in the Center of Robotics Research, University of Cincinnati. The robot lawn mower automatically completes its work with the region filling operation, a new kind of path planning for mobile robots. Some strategies for region filling of path planning have been developed for a partly-known or a unknown environment. Also, an advanced omnidirectional navigation system and a multisensor-based control system are used in the automatic operation. Research on the robot lawn mower, especially on the region filling of path planning, is significant in industrial and agricultural applications.

A new optimal seam method for seamless image stitching

NASA Astrophysics Data System (ADS)

Xue, Jiale; Chen, Shengyong; Cheng, Xu; Han, Ying; Zhao, Meng

2017-07-01

A novel optimal seam method which aims to stitch those images with overlapping area more seamlessly has been propos ed. Considering the traditional gradient domain optimal seam method and fusion algorithm result in bad color difference measurement and taking a long time respectively, the input images would be converted to HSV space and a new energy function is designed to seek optimal stitching path. To smooth the optimal stitching path, a simplified pixel correction and weighted average method are utilized individually. The proposed methods exhibit performance in eliminating the stitching seam compared with the traditional gradient optimal seam and high efficiency with multi-band blending algorithm.

Multi Robot Path Planning for Budgeted Active Perception with Self-Organising Maps

DTIC Science & Technology

2016-10-04

Multi- Robot Path Planning for Budgeted Active Perception with Self-Organising Maps Graeme Best1, Jan Faigl2 and Robert Fitch1 Abstract— We propose a...optimise paths for a multi- robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting...regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes

A Power Planning Algorithm Based on RPL for AMI Wireless Sensor Networks.

PubMed

Miguel, Marcio L F; Jamhour, Edgard; Pellenz, Marcelo E; Penna, Manoel C

2017-03-25

The advanced metering infrastructure (AMI) is an architecture for two-way communication between electric, gas and water meters and city utilities. The AMI network is a wireless sensor network that provides communication for metering devices in the neighborhood area of the smart grid. Recently, the applicability of a routing protocol for low-power and lossy networks (RPL) has been considered in AMI networks. Some studies in the literature have pointed out problems with RPL, including sub-optimal path selection and instability. In this paper, we defend the viewpoint that careful planning of the transmission power in wireless RPL networks can significantly reduce the pointed problems. This paper presents a method for planning the transmission power in order to assure that, after convergence, the size of the parent set of the RPL nodes is as close as possible to a predefined size. Another important feature is that all nodes in the parent set offer connectivity through links of similar quality.

A Power Planning Algorithm Based on RPL for AMI Wireless Sensor Networks

PubMed Central

Miguel, Marcio L. F.; Jamhour, Edgard; Pellenz, Marcelo E.; Penna, Manoel C.

2017-01-01

The advanced metering infrastructure (AMI) is an architecture for two-way communication between electric, gas and water meters and city utilities. The AMI network is a wireless sensor network that provides communication for metering devices in the neighborhood area of the smart grid. Recently, the applicability of a routing protocol for low-power and lossy networks (RPL) has been considered in AMI networks. Some studies in the literature have pointed out problems with RPL, including sub-optimal path selection and instability. In this paper, we defend the viewpoint that careful planning of the transmission power in wireless RPL networks can significantly reduce the pointed problems. This paper presents a method for planning the transmission power in order to assure that, after convergence, the size of the parent set of the RPL nodes is as close as possible to a predefined size. Another important feature is that all nodes in the parent set offer connectivity through links of similar quality. PMID:28346339

Trajectory Planning by Preserving Flexibility: Metrics and Analysis

NASA Technical Reports Server (NTRS)

Idris, Husni R.; El-Wakil, Tarek; Wing, David J.

2008-01-01

In order to support traffic management functions, such as mitigating traffic complexity, ground and airborne systems may benefit from preserving or optimizing trajectory flexibility. To help support this hypothesis trajectory flexibility metrics have been defined in previous work to represent the trajectory robustness and adaptability to the risk of violating safety and traffic management constraints. In this paper these metrics are instantiated in the case of planning a trajectory with the heading degree of freedom. A metric estimation method is presented based on simplifying assumptions, namely discrete time and heading maneuvers. A case is analyzed to demonstrate the estimation method and its use in trajectory planning in a situation involving meeting a time constraint and avoiding loss of separation with nearby traffic. The case involves comparing path-stretch trajectories, in terms of adaptability and robustness along each, deduced from a map of estimated flexibility metrics over the solution space. The case demonstrated anecdotally that preserving flexibility may result in enhancing certain factors that contribute to traffic complexity, namely reducing proximity and confrontation.

Planning collision free paths for two cooperating robots using a divide-and-conquer C-space traversal heuristic

NASA Technical Reports Server (NTRS)

Weaver, Johnathan M.

1993-01-01

A method was developed to plan feasible and obstacle-avoiding paths for two spatial robots working cooperatively in a known static environment. Cooperating spatial robots as referred to herein are robots which work in 6D task space while simultaneously grasping and manipulating a common, rigid payload. The approach is configuration space (c-space) based and performs selective rather than exhaustive c-space mapping. No expensive precomputations are required. A novel, divide-and-conquer type of heuristic is used to guide the selective mapping process. The heuristic does not involve any robot, environment, or task specific assumptions. A technique was also developed which enables solution of the cooperating redundant robot path planning problem without requiring the use of inverse kinematics for a redundant robot. The path planning strategy involves first attempting to traverse along the configuration space vector from the start point towards the goal point. If an unsafe region is encountered, an intermediate via point is identified by conducting a systematic search in the hyperplane orthogonal to and bisecting the unsafe region of the vector. This process is repeatedly applied until a solution to the global path planning problem is obtained. The basic concept behind this strategy is that better local decisions at the beginning of the trouble region may be made if a possible way around the 'center' of the trouble region is known. Thus, rather than attempting paths which look promising locally (at the beginning of a trouble region) but which may not yield overall results, the heuristic attempts local strategies that appear promising for circumventing the unsafe region.

An Optimization Principle for Deriving Nonequilibrium Statistical Models of Hamiltonian Dynamics

NASA Astrophysics Data System (ADS)

Turkington, Bruce

2013-08-01

A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. Given a vector of resolved variables, selected to describe the macroscopic state of the system, a family of quasi-equilibrium probability densities on phase space corresponding to the resolved variables is employed as a statistical model, and the evolution of the mean resolved vector is estimated by optimizing over paths of these densities. Specifically, a cost function is constructed to quantify the lack-of-fit to the microscopic dynamics of any feasible path of densities from the statistical model; it is an ensemble-averaged, weighted, squared-norm of the residual that results from submitting the path of densities to the Liouville equation. The path that minimizes the time integral of the cost function determines the best-fit evolution of the mean resolved vector. The closed reduced equations satisfied by the optimal path are derived by Hamilton-Jacobi theory. When expressed in terms of the macroscopic variables, these equations have the generic structure of governing equations for nonequilibrium thermodynamics. In particular, the value function for the optimization principle coincides with the dissipation potential that defines the relation between thermodynamic forces and fluxes. The adjustable closure parameters in the best-fit reduced equations depend explicitly on the arbitrary weights that enter into the lack-of-fit cost function. Two particular model reductions are outlined to illustrate the general method. In each example the set of weights in the optimization principle contracts into a single effective closure parameter.

Modified dwell time optimization model and its applications in subaperture polishing.

PubMed

Dong, Zhichao; Cheng, Haobo; Tam, Hon-Yuen

2014-05-20

The optimization of dwell time is an important procedure in deterministic subaperture polishing. We present a modified optimization model of dwell time by iterative and numerical method, assisted by extended surface forms and tool paths for suppressing the edge effect. Compared with discrete convolution and linear equation models, the proposed model has essential compatibility with arbitrary tool paths, multiple tool influence functions (TIFs) in one optimization, and asymmetric TIFs. The emulational fabrication of a Φ200  mm workpiece by the proposed model yields a smooth, continuous, and non-negative dwell time map with a root-mean-square (RMS) convergence rate of 99.6%, and the optimization costs much less time. By the proposed model, influences of TIF size and path interval to convergence rate and polishing time are optimized, respectively, for typical low and middle spatial-frequency errors. Results show that (1) the TIF size is nonlinear inversely proportional to convergence rate and polishing time. A TIF size of ~1/7 workpiece size is preferred; (2) the polishing time is less sensitive to path interval, but increasing the interval markedly reduces the convergence rate. A path interval of ~1/8-1/10 of the TIF size is deemed to be appropriate. The proposed model is deployed on a JR-1800 and MRF-180 machine. Figuring results of Φ920  mm Zerodur paraboloid and Φ100  mm Zerodur plane by them yield RMS of 0.016λ and 0.013λ (λ=632.8  nm), respectively, and thereby validate the feasibility of proposed dwell time model used for subaperture polishing.

Design and Analysis of Optimal Ascent Trajectories for Stratospheric Airships

NASA Astrophysics Data System (ADS)

Mueller, Joseph Bernard

Stratospheric airships are lighter-than-air vehicles that have the potential to provide a long-duration airborne presence at altitudes of 18-22 km. Designed to operate on solar power in the calm portion of the lower stratosphere and above all regulated air traffic and cloud cover, these vehicles represent an emerging platform that resides between conventional aircraft and satellites. A particular challenge for airship operation is the planning of ascent trajectories, as the slow moving vehicle must traverse the high wind region of the jet stream. Due to large changes in wind speed and direction across altitude and the susceptibility of airship motion to wind, the trajectory must be carefully planned, preferably optimized, in order to ensure that the desired station be reached within acceptable performance bounds of flight time and energy consumption. This thesis develops optimal ascent trajectories for stratospheric airships, examines the structure and sensitivity of these solutions, and presents a strategy for onboard guidance. Optimal ascent trajectories are developed that utilize wind energy to achieve minimum-time and minimum-energy flights. The airship is represented by a three-dimensional point mass model, and the equations of motion include aerodynamic lift and drag, vectored thrust, added mass effects, and accelerations due to mass flow rate, wind rates, and Earth rotation. A representative wind profile is developed based on historical meteorological data and measurements. Trajectory optimization is performed by first defining an optimal control problem with both terminal and path constraints, then using direct transcription to develop an approximate nonlinear parameter optimization problem of finite dimension. Optimal ascent trajectories are determined using SNOPT for a variety of upwind, downwind, and crosswind launch locations. Results of extensive optimization solutions illustrate definitive patterns in the ascent path for minimum time flights across varying launch locations, and show that significant energy savings can be realized with minimum-energy flights, compared to minimum-time time flights, given small increases in flight time. The performance of the optimal trajectories are then studied with respect to solar energy production during ascent, as well as sensitivity of the solutions to small changes in drag coefficient and wind model parameters. Results of solar power model simulations indicate that solar energy is sufficient to power ascent flights, but that significant energy loss can occur for certain types of trajectories. Sensitivity to the drag and wind model is approximated through numerical simulations, showing that optimal solutions change gradually with respect to changing wind and drag parameters and providing deeper insight into the characteristics of optimal airship flights. Finally, alternative methods are developed to generate near-optimal ascent trajectories in a manner suitable for onboard implementation. The structures and characteristics of previously developed minimum-time and minimum-energy ascent trajectories are used to construct simplified trajectory models, which are efficiently solved in a smaller numerical optimization problem. Comparison of these alternative solutions to the original SNOPT solutions show excellent agreement, suggesting the alternate formulations are an effective means to develop near-optimal solutions in an onboard setting.

Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

PubMed Central

Jeon, Semi; Yoon, Inhye; Jang, Jinbeum; Yang, Seungji; Kim, Jisung; Paik, Joonki

2017-01-01

Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems. PMID:28208622

Optimization of carbon mitigation paths in the power sector of Shenzhen, China

NASA Astrophysics Data System (ADS)

Li, Xin; Hu, Guangxiao; Duan, Ying; Ji, Junping

2017-08-01

This paper studied the carbon mitigation paths of the power sector in Shenzhen, China from a supply-side perspective. We investigated the carbon mitigation potentials and investments of seventeen mitigation technologies in the power sector, and employed a linear programming method to optimize the mitigation paths. The results show that: 1) The total carbon mitigation potential is 5.95 MtCO2 in 2020 in which the adjustment of power supply structure, technical improvements of existing coal- and gas-fired power plant account for 87.5%,6.5% and 6.0%, respectively. 2) In the optimal path, the avoided carbon dioxide to meet the local government’s mitigation goal in power sector is 1.26 MtCO2.The adjustment of power supply structure and technical improvement of the coal-fired power plants are the driving factors of carbon mitigation, with contributions to total carbon mitigation are 72.6% and 27.4%, respectively.

Path planning of decentralized multi-quadrotor based on fuzzy-cell decomposition algorithm

NASA Astrophysics Data System (ADS)

Iswanto, Wahyunggoro, Oyas; Cahyadi, Adha Imam

2017-04-01

The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms.

Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions*

PubMed Central

Janson, Lucas; Schmerling, Edward; Clark, Ashley; Pavone, Marco

2015-01-01

In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a “lazy” dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is reminiscent of the Fast Marching Method for the solution of Eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds—the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order O(n−1/d+ρ), where n is the number of sampled points, d is the dimension of the configuration space, and ρ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our the-oretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive. PMID:27003958

Predictor laws for pictorial flight displays

NASA Technical Reports Server (NTRS)

Grunwald, A. J.

1985-01-01

Two predictor laws are formulated and analyzed: (1) a circular path law based on constant accelerations perpendicular to the path and (2) a predictor law based on state transition matrix computations. It is shown that for both methods the predictor provides the essential lead zeros for the path-following task. However, in contrast to the circular path law, the state transition matrix law furnishes the system with additional zeros that entirely cancel out the higher-frequency poles of the vehicle dynamics. On the other hand, the circular path law yields a zero steady-state error in following a curved trajectory with a constant radius. A combined predictor law is suggested that utilizes the advantages of both methods. A simple analysis shows that the optimal prediction time mainly depends on the level of precision required in the path-following task, and guidelines for determining the optimal prediction time are given.

Multi-hop path tracing of mobile robot with multi-range image

NASA Astrophysics Data System (ADS)

Choudhury, Ramakanta; Samal, Chandrakanta; Choudhury, Umakanta

2010-02-01

It is well known that image processing depends heavily upon image representation technique . This paper intends to find out the optimal path of mobile robots for a specified area where obstacles are predefined as well as modified. Here the optimal path is represented by using the Quad tree method. Since there has been rising interest in the use of quad tree, we have tried to use the successive subdivision of images into quadrants from which the quad tree is developed. In the quad tree, obstacles-free area and the partial filled area are represented with different notations. After development of quad tree the algorithm is used to find the optimal path by employing neighbor finding technique, with a view to move the robot from the source to destination. The algorithm, here , permeates through the entire tree, and tries to locate the common ancestor for computation. The computation and the algorithm, aim at easing the ability of the robot to trace the optimal path with the help of adjacencies between the neighboring nodes as well as determining such adjacencies in the horizontal, vertical and diagonal directions. In this paper efforts have been made to determine the movement of the adjacent block in the quad tree and to detect the transition between the blocks equal size and finally generate the result.

The intertwining paths of the density managment and riparian buffer study and the Northwest Forest Plan

Treesearch

Kenneth J. Ruzicka; Deanna H. Olson; Klaus J. Puettmann

2013-01-01

Initiated simultaneously, the Density Management and Riparian Buff er Study of western Oregon and the Northwest Forest Plan have had intertwining paths related to federal forest management and policy changes in the Pacifi c Northwest over the last 15 to 20 years. We briefl y discuss the development of the Northwest Forest Plan and how it changed the way forest policy...

The Integrated Curriculum of "Planned Approach to Healthier Schools"

ERIC Educational Resources Information Center

Lounsbery, Monica; Gast, Julie; Smith, Nicole

2005-01-01

Planned Approach to Healthier Schools (PATHS) is a multicomponent school program that aims to establish and sustain a social norm consistent with physical activity and healthy nutrition in the school community. The PATHS components include: (1) a professional development and wellness program for faculty and staff; (2) ongoing social-marketing…

32. ISOMETRIC VIEW OF PIPING PLAN, SHOWING PATH OF CONDUIT ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

32. ISOMETRIC VIEW OF PIPING PLAN, SHOWING PATH OF CONDUIT FROM CONTROL BUNKER TO SHIELDING TANK. F.C. TORKELSON DRAWING NUMBER 842-ARVFS-701-P-1. INEL INDEX CODE NUMBER: 075 0701 60 851 151977. - Idaho National Engineering Laboratory, Advanced Reentry Vehicle Fusing System, Scoville, Butte County, ID

Planning Flight Paths of Autonomous Aerobots

NASA Technical Reports Server (NTRS)

Kulczycki, Eric; Elfes, Alberto; Sharma, Shivanjli

2009-01-01

Algorithms for planning flight paths of autonomous aerobots (robotic blimps) to be deployed in scientific exploration of remote planets are undergoing development. These algorithms are also adaptable to terrestrial applications involving robotic submarines as well as aerobots and other autonomous aircraft used to acquire scientific data or to perform surveying or monitoring functions.

EPA Critical Path Science Plan Projects 19, 20 and 21: Human and Bovine Source Detection

EPA Science Inventory

The U.S. EPA Critical Path Science Plan Projects are: Project 19: develop novel bovine and human host-specific PCR assays and complete performance evaluation with other published methods. Project 20: Evaluate human-specific assays with water samples impacted with different lev...

Optimizing Retransmission Threshold in Wireless Sensor Networks

PubMed Central

Bi, Ran; Li, Yingshu; Tan, Guozhen; Sun, Liang

2016-01-01

The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is OnΔ·max1≤i≤n{ui}, where ui is the given upper bound of the retransmission threshold of sensor node i in a given delivery path, n is the length of the delivery path and Δ is the given upper bound of the transmission delay of the delivery path. If Δ is greater than the polynomial, to reduce the time complexity, a linear programming-based (1+pmin)-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed O(1)-approximation algorithm with time complexity O(1) is proposed. Experimental results show that the proposed algorithms have better performance. PMID:27171092

Robust Accurate Non-Invasive Analyte Monitor

DOEpatents

Robinson, Mark R.

1998-11-03

An improved method and apparatus for determining noninvasively and in vivo one or more unknown values of a known characteristic, particularly the concentration of an analyte in human tissue. The method includes: (1) irradiating the tissue with infrared energy (400 nm-2400 nm) having at least several wavelengths in a given range of wavelengths so that there is differential absorption of at least some of the wavelengths by the tissue as a function of the wavelengths and the known characteristic, the differential absorption causeing intensity variations of the wavelengths incident from the tissue; (2) providing a first path through the tissue; (3) optimizing the first path for a first sub-region of the range of wavelengths to maximize the differential absorption by at least some of the wavelengths in the first sub-region; (4) providing a second path through the tissue; and (5) optimizing the second path for a second sub-region of the range, to maximize the differential absorption by at least some of the wavelengths in the second sub-region. In the preferred embodiment a third path through the tissue is provided for, which path is optimized for a third sub-region of the range. With this arrangement, spectral variations which are the result of tissue differences (e.g., melanin and temperature) can be reduced. At least one of the paths represents a partial transmission path through the tissue. This partial transmission path may pass through the nail of a finger once and, preferably, twice. Also included are apparatus for: (1) reducing the arterial pulsations within the tissue; and (2) maximizing the blood content i the tissue.

[Gender and career paths of female primary care physicians in Andalusia, Spain, at the beginning of 21st century].

PubMed

Saletti-Cuesta, Lorena; Delgado, Ana; Ortiz-Gómez, Teresa

2014-12-01

The purpose of this article was to study, from a feminist perspective, the diversity and homogeneity in the career paths of female primary care physicians from Andalusia, Spain in the early 21st century, by analyzing the meanings they give to their careers and the influence of personal, family and professional factors. We conducted a qualitative study with six discussion groups. Thirty-two female primary care physicians working in urban health centers of the public health system of Andalusia participated in the study. The discourse analysis revealed that most of the female physicians did not plan for professional goals and, when they did plan for them, the goals were intertwined with family needs. Consequently, their career paths were discontinuous. In contrast, career paths oriented towards professional development and the conscious planning of goals were more common among the female doctors acting as directors of health care centers.

Autonomous Robot Navigation in Human-Centered Environments Based on 3D Data Fusion

NASA Astrophysics Data System (ADS)

Steinhaus, Peter; Strand, Marcus; Dillmann, Rüdiger

2007-12-01

Efficient navigation of mobile platforms in dynamic human-centered environments is still an open research topic. We have already proposed an architecture (MEPHISTO) for a navigation system that is able to fulfill the main requirements of efficient navigation: fast and reliable sensor processing, extensive global world modeling, and distributed path planning. Our architecture uses a distributed system of sensor processing, world modeling, and path planning units. In this arcticle, we present implemented methods in the context of data fusion algorithms for 3D world modeling and real-time path planning. We also show results of the prototypic application of the system at the museum ZKM (center for art and media) in Karlsruhe.

Kinetic constrained optimization of the golf swing hub path.

PubMed

Nesbit, Steven M; McGinnis, Ryan S

2014-12-01

This study details an optimization of the golf swing, where the hand path and club angular trajectories are manipulated. The optimization goal was to maximize club head velocity at impact within the interaction kinetic limitations (force, torque, work, and power) of the golfer as determined through the analysis of a typical swing using a two-dimensional dynamic model. The study was applied to four subjects with diverse swing capabilities and styles. It was determined that it is possible for all subjects to increase their club head velocity at impact within their respective kinetic limitations through combined modifications to their respective hand path and club angular trajectories. The manner of the modifications, the degree of velocity improvement, the amount of kinetic reduction, and the associated kinetic limitation quantities were subject dependent. By artificially minimizing selected kinetic inputs within the optimization algorithm, it was possible to identify swing trajectory characteristics that indicated relative kinetic weaknesses of a subject. Practical implications are offered based upon the findings of the study. Key PointsThe hand path trajectory is an important characteristic of the golf swing and greatly affects club head velocity and golfer/club energy transfer.It is possible to increase the energy transfer from the golfer to the club by modifying the hand path and swing trajectories without increasing the kinetic output demands on the golfer.It is possible to identify relative kinetic output strengths and weakness of a golfer through assessment of the hand path and swing trajectories.Increasing any one of the kinetic outputs of the golfer can potentially increase the club head velocity at impact.The hand path trajectory has important influences over the club swing trajectory.

Kinetic Constrained Optimization of the Golf Swing Hub Path

PubMed Central

Nesbit, Steven M.; McGinnis, Ryan S.

2014-01-01

This study details an optimization of the golf swing, where the hand path and club angular trajectories are manipulated. The optimization goal was to maximize club head velocity at impact within the interaction kinetic limitations (force, torque, work, and power) of the golfer as determined through the analysis of a typical swing using a two-dimensional dynamic model. The study was applied to four subjects with diverse swing capabilities and styles. It was determined that it is possible for all subjects to increase their club head velocity at impact within their respective kinetic limitations through combined modifications to their respective hand path and club angular trajectories. The manner of the modifications, the degree of velocity improvement, the amount of kinetic reduction, and the associated kinetic limitation quantities were subject dependent. By artificially minimizing selected kinetic inputs within the optimization algorithm, it was possible to identify swing trajectory characteristics that indicated relative kinetic weaknesses of a subject. Practical implications are offered based upon the findings of the study. Key Points The hand path trajectory is an important characteristic of the golf swing and greatly affects club head velocity and golfer/club energy transfer. It is possible to increase the energy transfer from the golfer to the club by modifying the hand path and swing trajectories without increasing the kinetic output demands on the golfer. It is possible to identify relative kinetic output strengths and weakness of a golfer through assessment of the hand path and swing trajectories. Increasing any one of the kinetic outputs of the golfer can potentially increase the club head velocity at impact. The hand path trajectory has important influences over the club swing trajectory. PMID:25435779

A New Technique for Compensating Joint Limits in a Robot Manipulator

NASA Technical Reports Server (NTRS)

Litt, Jonathan; Hickman, Andre; Guo, Ten-Huei

1996-01-01

A new robust, optimal, adaptive technique for compensating rate and position limits in the joints of a six degree-of-freedom elbow manipulator is presented. In this new algorithm, the unmet demand as a result of actuator saturation is redistributed among the remaining unsaturated joints. The scheme is used to compensate for inadequate path planning, problems such as joint limiting, joint freezing, or even obstacle avoidance, where a desired position and orientation are not attainable due to an unrealizable joint command. Once a joint encounters a limit, supplemental commands are sent to other joints to best track, according to a selected criterion, the desired trajectory.

An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization

PubMed Central

Tsai, Rong-Guei

2018-01-01

The location information obtained using a sensor is a critical requirement in wireless sensor networks. Numerous localization schemes have been proposed, among which mobile-anchor-node-assisted localization (MANAL) can reduce costs and overcome environmental constraints. A mobile anchor node (MAN) provides its own location information to assist the localization of sensor nodes. Numerous path planning schemes have been proposed for MANAL, but most scenarios assume the absence of obstacles in the environment. However, in a realistic environment, sensor nodes cannot be located because the obstacles block the path traversed by the MAN, thereby rendering the sensor incapable of receiving sufficient three location information from the MAN. This study proposes the obstacle-tolerant path planning (OTPP) approach to solve the sensor location problem owing to obstacle blockage. OTPP can approximate the optimum beacon point number and path planning, thereby ensuring that all the unknown nodes can receive the three location information from the MAN and reduce the number of MAN broadcast packet times. Experimental results demonstrate that OTPP performs better than Z-curves because it reduces the total number of beacon points utilized and is thus more suitable in an obstacle-present environment. Compared to the Z-curve, OTPP can reduce localization error and improve localization coverage. PMID:29547582

Using the Gurobi Solvers on the Peregrine System | High-Performance

Science.gov Websites

Peregrine System Gurobi Optimizer is a suite of solvers for mathematical programming. It is licensed for ('GRB_MATLAB_PATH') >> path(path,grb) Gurobi and GAMS GAMS is a high-level modeling system for mathematical

An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.

PubMed

Lorenzo, Enery; Camacho-Caceres, Katia; Ropelewski, Alexander J; Rosas, Juan; Ortiz-Mojer, Michael; Perez-Marty, Lynn; Irizarry, Juan; Gonzalez, Valerie; Rodríguez, Jesús A; Cabrera-Rios, Mauricio; Isaza, Clara

2015-06-01

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

Optimal Patrol to Detect Attacks at Dispersed Heterogeneous Locations

DTIC Science & Technology

2013-12-01

path with one revisit SPR2 Shortest path with two revisits SPR3 Shortest path with three revisits TSP Traveling salesman problem UAV Unmanned aerial...path patrol pattern. Finding the shortest-path patrol pattern is an example of solving a traveling salesman problem , as described in Section 16.5 of...use of patrol paths based on the traveling salesman prob- lem (TSP), where patrollers follow the shortest Hamiltonian cycle in a graph in order to

Surgeon Training in Telerobotic Surgery via a Hardware-in-the-Loop Simulator

PubMed Central

Alemzadeh, Homa; Chen, Daniel; Kalbarczyk, Zbigniew; Iyer, Ravishankar K.; Kesavadas, Thenkurussi

2017-01-01

This work presents a software and hardware framework for a telerobotic surgery safety and motor skill training simulator. The aims are at providing trainees a comprehensive simulator for acquiring essential skills to perform telerobotic surgery. Existing commercial robotic surgery simulators lack features for safety training and optimal motion planning, which are critical factors in ensuring patient safety and efficiency in operation. In this work, we propose a hardware-in-the-loop simulator directly introducing these two features. The proposed simulator is built upon the Raven-II™ open source surgical robot, integrated with a physics engine and a safety hazard injection engine. Also, a Fast Marching Tree-based motion planning algorithm is used to help trainee learn the optimal instrument motion patterns. The main contributions of this work are (1) reproducing safety hazards events, related to da Vinci™ system, reported to the FDA MAUDE database, with a novel haptic feedback strategy to provide feedback to the operator when the underlying dynamics differ from the real robot's states so that the operator will be aware and can mitigate the negative impact of the safety-critical events, and (2) using motion planner to generate semioptimal path in an interactive robotic surgery training environment. PMID:29065635

Corrections to scaling for watersheds, optimal path cracks, and bridge lines

NASA Astrophysics Data System (ADS)

Fehr, E.; Schrenk, K. J.; Araújo, N. A. M.; Kadau, D.; Grassberger, P.; Andrade, J. S., Jr.; Herrmann, H. J.

2012-07-01

We study the corrections to scaling for the mass of the watershed, the bridge line, and the optimal path crack in two and three dimensions (2D and 3D). We disclose that these models have numerically equivalent fractal dimensions and leading correction-to-scaling exponents. We conjecture all three models to possess the same fractal dimension, namely, df=1.2168±0.0005 in 2D and df=2.487±0.003 in 3D, and the same exponent of the leading correction, Ω=0.9±0.1 and Ω=1.0±0.1, respectively. The close relations between watersheds, optimal path cracks in the strong disorder limit, and bridge lines are further supported by either heuristic or exact arguments.

Modeling of tool path for the CNC sheet cutting machines

NASA Astrophysics Data System (ADS)

Petunin, Aleksandr A.

2015-11-01

In the paper the problem of tool path optimization for CNC (Computer Numerical Control) cutting machines is considered. The classification of the cutting techniques is offered. We also propose a new classification of toll path problems. The tasks of cost minimization and time minimization for standard cutting technique (Continuous Cutting Problem, CCP) and for one of non-standard cutting techniques (Segment Continuous Cutting Problem, SCCP) are formalized. We show that the optimization tasks can be interpreted as discrete optimization problem (generalized travel salesman problem with additional constraints, GTSP). Formalization of some constraints for these tasks is described. For the solution GTSP we offer to use mathematical model of Prof. Chentsov based on concept of a megalopolis and dynamic programming.

Exploring chemical reaction mechanisms through harmonic Fourier beads path optimization.

PubMed

Khavrutskii, Ilja V; Smith, Jason B; Wallqvist, Anders

2013-10-28

Here, we apply the harmonic Fourier beads (HFB) path optimization method to study chemical reactions involving covalent bond breaking and forming on quantum mechanical (QM) and hybrid QM∕molecular mechanical (QM∕MM) potential energy surfaces. To improve efficiency of the path optimization on such computationally demanding potentials, we combined HFB with conjugate gradient (CG) optimization. The combined CG-HFB method was used to study two biologically relevant reactions, namely, L- to D-alanine amino acid inversion and alcohol acylation by amides. The optimized paths revealed several unexpected reaction steps in the gas phase. For example, on the B3LYP∕6-31G(d,p) potential, we found that alanine inversion proceeded via previously unknown intermediates, 2-iminopropane-1,1-diol and 3-amino-3-methyloxiran-2-ol. The CG-HFB method accurately located transition states, aiding in the interpretation of complex reaction mechanisms. Thus, on the B3LYP∕6-31G(d,p) potential, the gas phase activation barriers for the inversion and acylation reactions were 50.5 and 39.9 kcal∕mol, respectively. These barriers determine the spontaneous loss of amino acid chirality and cleavage of peptide bonds in proteins. We conclude that the combined CG-HFB method further advances QM and QM∕MM studies of reaction mechanisms.

Optimal rotation sequences for active perception

NASA Astrophysics Data System (ADS)

Nakath, David; Rachuy, Carsten; Clemens, Joachim; Schill, Kerstin

2016-05-01

One major objective of autonomous systems navigating in dynamic environments is gathering information needed for self localization, decision making, and path planning. To account for this, such systems are usually equipped with multiple types of sensors. As these sensors often have a limited field of view and a fixed orientation, the task of active perception breaks down to the problem of calculating alignment sequences which maximize the information gain regarding expected measurements. Action sequences that rotate the system according to the calculated optimal patterns then have to be generated. In this paper we present an approach for calculating these sequences for an autonomous system equipped with multiple sensors. We use a particle filter for multi- sensor fusion and state estimation. The planning task is modeled as a Markov decision process (MDP), where the system decides in each step, what actions to perform next. The optimal control policy, which provides the best action depending on the current estimated state, maximizes the expected cumulative reward. The latter is computed from the expected information gain of all sensors over time using value iteration. The algorithm is applied to a manifold representation of the joint space of rotation and time. We show the performance of the approach in a spacecraft navigation scenario where the information gain is changing over time, caused by the dynamic environment and the continuous movement of the spacecraft

Multistructural microiteration technique for geometry optimization and reaction path calculation in large systems.

PubMed

Suzuki, Kimichi; Morokuma, Keiji; Maeda, Satoshi

2017-10-05

We propose a multistructural microiteration (MSM) method for geometry optimization and reaction path calculation in large systems. MSM is a simple extension of the geometrical microiteration technique. In conventional microiteration, the structure of the non-reaction-center (surrounding) part is optimized by fixing atoms in the reaction-center part before displacements of the reaction-center atoms. In this method, the surrounding part is described as the weighted sum of multiple surrounding structures that are independently optimized. Then, geometric displacements of the reaction-center atoms are performed in the mean field generated by the weighted sum of the surrounding parts. MSM was combined with the QM/MM-ONIOM method and applied to chemical reactions in aqueous solution or enzyme. In all three cases, MSM gave lower reaction energy profiles than the QM/MM-ONIOM-microiteration method over the entire reaction paths with comparable computational costs. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

TU-G-BRB-01: Continuous Path Optimization for Non-Coplanar Variant SAD IMRT Delivery Using C-Arm Machines.

PubMed

Ruan, D; Dong, P; Low, D; Sheng, K

2012-06-01

To develop and investigate a continuous path optimization methodology to traverse prescribed non-coplanar IMRT beams with variant SADs, by orchestrating the couch and gantry movement with zero-collision, minimal patient motion consequence and machine travel time. We convert the given collision zone definition and the prescribed beam location/angles to a tumor-centric coordinate, and represent the traversing path as a continuous open curve. We proceed to optimize a composite objective function consisting of (1) a strong attraction energy to ensure all prescribed beams are en-route, (2) a penalty for patient-motion inducing couch motion, and (3) a penalty for travel-time inducing overall path-length. Feasibility manifold is defined as complement to collision zone and the optimization is performed with a level set representation evolved with variational flows. The proposed method has been implemented and tested on clinically derived data. In the absence of any existing solutions for the same problem, we validate by: (1) visual inspecting the generated path rendered in the 3D tumor-centric coordinates, and (2) comparing with a traveling-salesman (TSP) solution obtained from relaxing the variant SADs and continuous collision-avoidance requirement. The proposed method has generated delivery paths that are smooth and intuitively appealing. Under relaxed settings, our results outperform the generic TSP solutions and agree with specially tuned versions. We have proposed a novel systematic approach that automatically determines the continuous path to cover non-coplanar, varying SAD IMRT beams. The proposed approach accommodates patient-specific collision zone definition and ensures its avoidance continuously. The differential penalty to couch and gantry motions allows customizable tradeoff between patient geometry stability and delivery efficiency. This development paves the path to achieve safe, accurate and efficient non-coplanar IMRT delivery with the advanced robotic controls in new-generation C-arm systems, enabling practical harvesting of the dose benefit offered by non-coplanar, variant SAD IMRT treatment. © 2012 American Association of Physicists in Medicine.

Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning.

PubMed

Trinh, Lan Anh; Ekström, Mikael; Cürüklü, Baran

2018-01-01

Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e., safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta * algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta * algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.

Search Problems in Mission Planning and Navigation of Autonomous Aircraft. M.S. Thesis

NASA Technical Reports Server (NTRS)

Krozel, James A.

1988-01-01

An architecture for the control of an autonomous aircraft is presented. The architecture is a hierarchical system representing an anthropomorphic breakdown of the control problem into planner, navigator, and pilot systems. The planner system determines high level global plans from overall mission objectives. This abstract mission planning is investigated by focusing on the Traveling Salesman Problem with variations on local and global constraints. Tree search techniques are applied including the breadth first, depth first, and best first algorithms. The minimum-column and row entries for the Traveling Salesman Problem cost matrix provides a powerful heuristic to guide these search techniques. Mission planning subgoals are directed from the planner to the navigator for planning routes in mountainous terrain with threats. Terrain/threat information is abstracted into a graph of possible paths for which graph searches are performed. It is shown that paths can be well represented by a search graph based on the Voronoi diagram of points representing the vertices of mountain boundaries. A comparison of Dijkstra's dynamic programming algorithm and the A* graph search algorithm from artificial intelligence/operations research is performed for several navigation path planning examples. These examples illustrate paths that minimize a combination of distance and exposure to threats. Finally, the pilot system synthesizes the flight trajectory by creating the control commands to fly the aircraft.

Optimal exploration systems

NASA Astrophysics Data System (ADS)

Klesh, Andrew T.

This dissertation studies optimal exploration, defined as the collection of information about given objects of interest by a mobile agent (the explorer) using imperfect sensors. The key aspects of exploration are kinematics (which determine how the explorer moves in response to steering commands), energetics (which determine how much energy is consumed by motion and maneuvers), informatics (which determine the rate at which information is collected) and estimation (which determines the states of the objects). These aspects are coupled by the steering decisions of the explorer. We seek to improve exploration by finding trade-offs amongst these couplings and the components of exploration: the Mission, the Path and the Agent. A comprehensive model of exploration is presented that, on one hand, accounts for these couplings and on the other hand is simple enough to allow analysis. This model is utilized to pose and solve several exploration problems where an objective function is to be minimized. Specific functions to be considered are the mission duration and the total energy. These exploration problems are formulated as optimal control problems and necessary conditions for optimality are obtained in the form of two-point boundary value problems. An analysis of these problems reveals characteristics of optimal exploration paths. Several regimes are identified for the optimal paths including the Watchtower, Solar and Drag regime, and several non-dimensional parameters are derived that determine the appropriate regime of travel. The so-called Power Ratio is shown to predict the qualitative features of the optimal paths, provide a metric to evaluate an aircrafts design and determine an aircrafts capability for flying perpetually. Optimal exploration system drivers are identified that provide perspective as to the importance of these various regimes of flight. A bank-to-turn solar-powered aircraft flying at constant altitude on Mars is used as a specific platform for analysis using the coupled model. Flight-paths found with this platform are presented that display the optimal exploration problem characteristics. These characteristics are used to form heuristics, such as a Generalized Traveling Salesman Problem solver, to simplify the exploration problem. These heuristics are used to empirically show the successful completion of an exploration mission by a physical explorer.

Task path planning, scheduling and learning for free-ranging robot systems

NASA Technical Reports Server (NTRS)

Wakefield, G. Steve

1987-01-01

The development of robotics applications for space operations is often restricted by the limited movement available to guided robots. Free ranging robots can offer greater flexibility than physically guided robots in these applications. Presented here is an object oriented approach to path planning and task scheduling for free-ranging robots that allows the dynamic determination of paths based on the current environment. The system also provides task learning for repetitive jobs. This approach provides a basis for the design of free-ranging robot systems which are adaptable to various environments and tasks.

Path Planning Based on Ply Orientation Information for Automatic Fiber Placement on Mesh Surface

NASA Astrophysics Data System (ADS)

Pei, Jiazhi; Wang, Xiaoping; Pei, Jingyu; Yang, Yang

2018-03-01

This article introduces an investigation of path planning with ply orientation information for automatic fiber placement (AFP) on open-contoured mesh surface. The new method makes use of the ply orientation information generated by loading characteristics on surface, divides the surface into several zones according to the ply orientation information and then designs different fiber paths in different zones. This article also gives new idea of up-layer design in order to make up for defects between parts and improve product's strength.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty.

PubMed

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty

PubMed Central

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method. PMID:26417946

Robust mobility in human-populated environments

NASA Astrophysics Data System (ADS)

Gonzalez, Juan Pablo; Phillips, Mike; Neuman, Brad; Likhachev, Max

2012-06-01

Creating robots that can help humans in a variety of tasks requires robust mobility and the ability to safely navigate among moving obstacles. This paper presents an overview of recent research in the Robotics Collaborative Technology Alliance (RCTA) that addresses many of the core requirements for robust mobility in human-populated environments. Safe Interval Path Planning (SIPP) allows for very fast planning in dynamic environments when planning timeminimal trajectories. Generalized Safe Interval Path Planning extends this concept to trajectories that minimize arbitrary cost functions. Finally, generalized PPCP algorithm is used to generate plans that reason about the uncertainty in the predicted trajectories of moving obstacles and try to actively disambiguate the intentions of humans whenever necessary. We show how these approaches consider moving obstacles and temporal constraints and produce high-fidelity paths. Experiments in simulated environments show the performance of the algorithms under different controlled conditions, and experiments on physical mobile robots interacting with humans show how the algorithms perform under the uncertainties of the real world.

The flight planning - flight management connection

NASA Technical Reports Server (NTRS)

Sorensen, J. A.

1984-01-01

Airborne flight management systems are currently being implemented to minimize direct operating costs when flying over a fixed route between a given city pair. Inherent in the design of these systems is that the horizontal flight path and wind and temperature models be defined and input into the airborne computer before flight. The wind/temperature model and horizontal path are products of the flight planning process. Flight planning consists of generating 3-D reference trajectories through a forecast wind field subject to certain ATC and transport operator constraints. The interrelationships between flight management and flight planning are reviewed, and the steps taken during the flight planning process are summarized.

Path Planning Algorithms for the Adaptive Sensor Fleet

NASA Technical Reports Server (NTRS)

Stoneking, Eric; Hosler, Jeff

2005-01-01

The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

Comparison of some evolutionary algorithms for optimization of the path synthesis problem

NASA Astrophysics Data System (ADS)

Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna

2018-01-01

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.

Real-time path planning in dynamic virtual environments using multiagent navigation graphs.

PubMed

Sud, Avneesh; Andersen, Erik; Curtis, Sean; Lin, Ming C; Manocha, Dinesh

2008-01-01

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.

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

Reister, D.B.; Pin, F.G.

This paper addresses the problem of time-optional motions for a mobile platform in a planar environment. The platform has two non-steerable independently driven wheels. The overall mission of the robot is expressed in terms of a sequence of via points at which the platform must be at rest in a given configuration (position and orientation). The objective is to plan time-optimal trajectories between these configurations assuming an unobstructed environment. Using Pontryagin's maximum principle (PMP), we formally demonstrate that all time optimal motions of the platform for this problem occur for bang-bang controls on the wheels (at each instant, the accelerationmore » on each wheel is either at its upper or lower limit). The PMP, however, only provides necessary conditions for time optimality. To find the time optimal robot trajectories, we first parameterize the bang-bang trajectories using the switch times on the wheels (the times at which the wheel accelerations change sign). With this parameterization, we can fully search the robot trajectory space and find the switch times that will produce particular paths to a desired final configuration of the platform. We show numerically that robot trajectories with three switch times (two on one wheel, one on the other) can reach any position, while trajectories with four switch times can reach any configuration. By numerical comparison with other trajectories involving similar or greater numbers of switch times, we then identify the sets of time-optimal trajectories. These are uniquely defined using ranges of the parameters, and consist of subsets of trajectories with three switch times for the problem when the final orientation of the robot is not specified, and four switch times when a full final configuration is specified. We conclude with a description of the use of the method for trajectory planning for one of our robots.« less

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

Reister, D.B.; Pin, F.G.

This paper addresses the problem of time-optional motions for a mobile platform in a planar environment. The platform has two non-steerable independently driven wheels. The overall mission of the robot is expressed in terms of a sequence of via points at which the platform must be at rest in a given configuration (position and orientation). The objective is to plan time-optimal trajectories between these configurations assuming an unobstructed environment. Using Pontryagin`s maximum principle (PMP), we formally demonstrate that all time optimal motions of the platform for this problem occur for bang-bang controls on the wheels (at each instant, the accelerationmore » on each wheel is either at its upper or lower limit). The PMP, however, only provides necessary conditions for time optimality. To find the time optimal robot trajectories, we first parameterize the bang-bang trajectories using the switch times on the wheels (the times at which the wheel accelerations change sign). With this parameterization, we can fully search the robot trajectory space and find the switch times that will produce particular paths to a desired final configuration of the platform. We show numerically that robot trajectories with three switch times (two on one wheel, one on the other) can reach any position, while trajectories with four switch times can reach any configuration. By numerical comparison with other trajectories involving similar or greater numbers of switch times, we then identify the sets of time-optimal trajectories. These are uniquely defined using ranges of the parameters, and consist of subsets of trajectories with three switch times for the problem when the final orientation of the robot is not specified, and four switch times when a full final configuration is specified. We conclude with a description of the use of the method for trajectory planning for one of our robots.« less

Trajectory optimization for dynamic couch rotation during volumetric modulated arc radiotherapy

NASA Astrophysics Data System (ADS)

Smyth, Gregory; Bamber, Jeffrey C.; Evans, Philip M.; Bedford, James L.

2013-11-01

Non-coplanar radiation beams are often used in three-dimensional conformal and intensity modulated radiotherapy to reduce dose to organs at risk (OAR) by geometric avoidance. In volumetric modulated arc radiotherapy (VMAT) non-coplanar geometries are generally achieved by applying patient couch rotations to single or multiple full or partial arcs. This paper presents a trajectory optimization method for a non-coplanar technique, dynamic couch rotation during VMAT (DCR-VMAT), which combines ray tracing with a graph search algorithm. Four clinical test cases (partial breast, brain, prostate only, and prostate and pelvic nodes) were used to evaluate the potential OAR sparing for trajectory-optimized DCR-VMAT plans, compared with standard coplanar VMAT. In each case, ray tracing was performed and a cost map reflecting the number of OAR voxels intersected for each potential source position was generated. The least-cost path through the cost map, corresponding to an optimal DCR-VMAT trajectory, was determined using Dijkstra’s algorithm. Results show that trajectory optimization can reduce dose to specified OARs for plans otherwise comparable to conventional coplanar VMAT techniques. For the partial breast case, the mean heart dose was reduced by 53%. In the brain case, the maximum lens doses were reduced by 61% (left) and 77% (right) and the globes by 37% (left) and 40% (right). Bowel mean dose was reduced by 15% in the prostate only case. For the prostate and pelvic nodes case, the bowel V50 Gy and V60 Gy were reduced by 9% and 45% respectively. Future work will involve further development of the algorithm and assessment of its performance over a larger number of cases in site-specific cohorts.

a Walking Disturbance Index Suggestions for Optimized Path Search for the People with Reduced Mobility

NASA Astrophysics Data System (ADS)

Moon, M.; Bang, Y.; Yu, K.; Kim, J.

2015-10-01

Recently, due to the increased penetration of smart devices and the development of geographic information system (GIS) technology, various route guidance services for pedestrians have been developed. However, until now, pedestrian navigation services for the people with reduced mobility (people who experience discomfort in transportation) including wheelchair users, the elderly, and pregnant women have not been provided. In this study, we present a walking disturbance index methodology for searching an optimized path for the people with reduced mobility by defining the factors that affect the walking of the people with reduced mobility and deriving the weights of these factors. In future research, we expect to be able to provide a navigation system that gives an optimized path for the people with reduced mobility using this method.

System and method for optimal load and source scheduling in context aware homes

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

Shetty, Pradeep; Foslien Graber, Wendy; Mangsuli, Purnaprajna R.

A controller for controlling energy consumption in a home includes a constraints engine to define variables for multiple appliances in the home corresponding to various home modes and persona of an occupant of the home. A modeling engine models multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context. An optimal scheduler receives the multiple paths of energy utilization and generates a schedule as a function of the multiple paths and a selected persona to place the home in a desired state.

An Application of Multi-Criteria Shortest Path to a Customizable Hex-Map Environment

DTIC Science & Technology

2015-03-26

forces which could act as intermediate destinations or obstacles to movement through the network. This is similar to a traveling salesman problem ...118 Abstract The shortest path problem of finding the optimal path through a complex network is well-studied in the field of operations research. This...research presents an applica- tion of the shortest path problem to a customizable map with terrain features and enemy engagement risk. The PathFinder

Sensors and Algorithms for an Unmanned Surf-Zone Robot

DTIC Science & Technology

2015-12-01

71 3. Data Fusion and Filtering................................................ 74 C. VIRTUAL POTENTIAL FIELD (VPF) PATH PLANNING ...iron effects are clearly seen: Soft iron de - calibration (sphere distortion) was caused by proximity of circuit boards. Offset of the center of the...information to perform global tasks such as path- planning , sensors and actuators commands, external communications, etc. Python3 is used as the primary

Liouville action as path-integral complexity: from continuous tensor networks to AdS/CFT

NASA Astrophysics Data System (ADS)

Caputa, Pawel; Kundu, Nilay; Miyaji, Masamichi; Takayanagi, Tadashi; Watanabe, Kento

2017-11-01

We propose an optimization procedure for Euclidean path-integrals that evaluate CFT wave functionals in arbitrary dimensions. The optimization is performed by minimizing certain functional, which can be interpreted as a measure of computational complexity, with respect to background metrics for the path-integrals. In two dimensional CFTs, this functional is given by the Liouville action. We also formulate the optimization for higher dimensional CFTs and, in various examples, find that the optimized hyperbolic metrics coincide with the time slices of expected gravity duals. Moreover, if we optimize a reduced density matrix, the geometry becomes two copies of the entanglement wedge and reproduces the holographic entanglement entropy. Our approach resembles a continuous tensor network renormalization and provides a concrete realization of the proposed interpretation of AdS/CFT as tensor networks. The present paper is an extended version of our earlier report arXiv:1703.00456 and includes many new results such as evaluations of complexity functionals, energy stress tensor, higher dimensional extensions and time evolutions of thermofield double states.

Feedback laws for fuel minimization for transport aircraft

NASA Technical Reports Server (NTRS)

Price, D. B.; Gracey, C.

1984-01-01

The Theoretical Mechanics Branch has as one of its long-range goals to work toward solving real-time trajectory optimization problems on board an aircraft. This is a generic problem that has application to all aspects of aviation from general aviation through commercial to military. Overall interest is in the generic problem, but specific problems to achieve concrete results are examined. The problem is to develop control laws that generate approximately optimal trajectories with respect to some criteria such as minimum time, minimum fuel, or some combination of the two. These laws must be simple enough to be implemented on a computer that is flown on board an aircraft, which implies a major simplification from the two point boundary value problem generated by a standard trajectory optimization problem. In addition, the control laws allow for changes in end conditions during the flight, and changes in weather along a planned flight path. Therefore, a feedback control law that generates commands based on the current state rather than a precomputed open-loop control law is desired. This requirement, along with the need for order reduction, argues for the application of singular perturbation techniques.

I-FORCAST: Rapid Flight Planning Tool

NASA Technical Reports Server (NTRS)

Oaida, Bogdan; Khan, Mohammed; Mercury, Michael B.

2012-01-01

I-FORCAST (Instrument - Field of Regard Coverage Analysis and Simulation Tool) is a flight planning tool specifically designed for quickly verifying the feasibility and estimating the cost of airborne remote sensing campaigns (see figure). Flights are simulated by being broken into three predefined routing algorithms as necessary: mapping in a snaking pattern, mapping the area around a point target (like a volcano) with a star pattern, and mapping the area between a list of points. The tool has been used to plan missions for radar, lidar, and in-situ atmospheric measuring instruments for a variety of aircraft. It has also been used for global and regional scale campaigns and automatically includes landings when refueling is required. The software has been compared to the flight times of known commercial aircraft route travel times, as well as a UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) campaign, and was within 15% of the actual flight time. Most of the discrepancy is due to non-optimal flight paths taken by actual aircraft to avoid restricted airspace and used to follow landing and take-off corridors.

Assessment of Masonry Buildings Subjected to Landslide-Induced Settlements: From Load Path Method to Evolutionary Optimization Method

NASA Astrophysics Data System (ADS)

Palmisano, Fabrizio; Elia, Angelo

2017-10-01

One of the main difficulties, when dealing with landslide structural vulnerability, is the diagnosis of the causes of crack patterns. This is also due to the excessive complexity of models based on classical structural mechanics that makes them inappropriate especially when there is the necessity to perform a rapid vulnerability assessment at the territorial scale. This is why, a new approach, based on a ‘simple model’ (i.e. the Load Path Method, LPM), has been proposed by Palmisano and Elia for the interpretation of the behaviour of masonry buildings subjected to landslide-induced settlements. However, the LPM is very useful for rapidly finding the 'most plausible solution' instead of the exact solution. To find the solution, optimization algorithms are necessary. In this scenario, this article aims to show how the Bidirectional Evolutionary Structural Optimization method by Huang and Xie, can be very useful to optimize the strut-and-tie models obtained by using the Load Path Method.

Optimization of MLS receivers for multipath environments

NASA Technical Reports Server (NTRS)

Mcalpine, G. A.; Irwin, S. H.; NELSON; Roleyni, G.

1977-01-01

Optimal design studies of MLS angle-receivers and a theoretical design-study of MLS DME-receivers are reported. The angle-receiver results include an integration of the scan data processor and tracking filter components of the optimal receiver into a unified structure. An extensive simulation study comparing the performance of the optimal and threshold receivers in a wide variety of representative dynamical interference environments was made. The optimal receiver was generally superior. A simulation of the performance of the threshold and delay-and-compare receivers in various signal environments was performed. An analysis of combined errors due to lateral reflections from vertical structures with small differential path delays, specular ground reflections with neglible differential path delays, and thermal noise in the receivers is provided.

Multi-objective four-dimensional vehicle motion planning in large dynamic environments.

PubMed

Wu, Paul P-Y; Campbell, Duncan; Merz, Torsten

2011-06-01

This paper presents Multi-Step A∗ (MSA∗), a search algorithm based on A∗ for multi-objective 4-D vehicle motion planning (three spatial and one time dimensions). The research is principally motivated by the need for offline and online motion planning for autonomous unmanned aerial vehicles (UAVs). For UAVs operating in large dynamic uncertain 4-D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and a grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles, and the rules of the air. It is shown that MSA∗ finds a cost optimal solution using variable length, angle, and velocity trajectory segments. These segments are approximated with a grid-based cell sequence that provides an inherent tolerance to uncertainty. The computational efficiency is achieved by using variable successor operators to create a multiresolution memory-efficient lattice sampling structure. The simulation studies on the UAV flight planning problem show that MSA∗ meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of a vector neighborhood-based A∗.

Impulsive noise suppression in color images based on the geodesic digital paths

NASA Astrophysics Data System (ADS)

Smolka, Bogdan; Cyganek, Boguslaw

2015-02-01

In the paper a novel filtering design based on the concept of exploration of the pixel neighborhood by digital paths is presented. The paths start from the boundary of a filtering window and reach its center. The cost of transitions between adjacent pixels is defined in the hybrid spatial-color space. Then, an optimal path of minimum total cost, leading from pixels of the window's boundary to its center is determined. The cost of an optimal path serves as a degree of similarity of the central pixel to the samples from the local processing window. If a pixel is an outlier, then all the paths starting from the window's boundary will have high costs and the minimum one will also be high. The filter output is calculated as a weighted mean of the central pixel and an estimate constructed using the information on the minimum cost assigned to each image pixel. So, first the costs of optimal paths are used to build a smoothed image and in the second step the minimum cost of the central pixel is utilized for construction of the weights of a soft-switching scheme. The experiments performed on a set of standard color images, revealed that the efficiency of the proposed algorithm is superior to the state-of-the-art filtering techniques in terms of the objective restoration quality measures, especially for high noise contamination ratios. The proposed filter, due to its low computational complexity, can be applied for real time image denoising and also for the enhancement of video streams.

Effect of reflected and refracted signals on coherent underwater acoustic communication: results from the Kauai experiment (KauaiEx 2003).

PubMed

Rouseff, Daniel; Badiey, Mohsen; Song, Aijun

2009-11-01

The performance of a communications equalizer is quantified in terms of the number of acoustic paths that are treated as usable signal. The analysis uses acoustical and oceanographic data collected off the Hawaiian Island of Kauai. Communication signals were measured on an eight-element vertical array at two different ranges, 1 and 2 km, and processed using an equalizer based on passive time-reversal signal processing. By estimating the Rayleigh parameter, it is shown that all paths reflected by the sea surface at both ranges undergo incoherent scattering. It is demonstrated that some of these incoherently scattered paths are still useful for coherent communications. At range of 1 km, optimal communications performance is achieved when six acoustic paths are retained and all paths with more than one reflection off the sea surface are rejected. Consistent with a model that ignores loss from near-surface bubbles, the performance improves by approximately 1.8 dB when increasing the number of retained paths from four to six. The four-path results though are more stable and require less frequent channel estimation. At range of 2 km, ray refraction is observed and communications performance is optimal when some paths with two sea-surface reflections are retained.

Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

NASA Astrophysics Data System (ADS)

SONG, Pan; GAO, Bolin; XIE, Shugang; FANG, Rui

2017-05-01

The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation (CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control (MC) problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control (NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control (TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane-change (DLC) test results show that by using the optimal predictive controller, the root-mean-square (RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration (OPA) driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive-by-wire vehicle with better lane-keeping and collision-avoidance capabilities during autonomous driving.

Defining Advancement Career Paths and Succession Plans: Critical Human Capital Retention Strategies for High-Performing Advancement Divisions

ERIC Educational Resources Information Center

Croteau, Jon Derek; Wolk, Holly Gordon

2010-01-01

There are many factors that can influence whether a highly talented staff member will build a career within an institution or use it as a stepping stone. This article defines and explores the notions of developing career paths and succession planning and why they are critical human capital investment strategies in retaining the highest performers…

Is addiction a failure of rationality?

PubMed

Ferguson, Brian

2012-01-01

This note argues that addiction itself can be seen as a form of double failure, specifically the failure to stick to a predetermined optimal lifetime consumption path for an addictive commodity, which failure might be the result of bad luck, and the failure to adjust the level of consumption of the addictive commodity once the consumer is off their optimal path, which might be seen as a failure of judgment.

Springback effects during single point incremental forming: Optimization of the tool path

NASA Astrophysics Data System (ADS)

Giraud-Moreau, Laurence; Belchior, Jérémy; Lafon, Pascal; Lotoing, Lionel; Cherouat, Abel; Courtielle, Eric; Guines, Dominique; Maurine, Patrick

2018-05-01

Incremental sheet forming is an emerging process to manufacture sheet metal parts. This process is more flexible than conventional one and well suited for small batch production or prototyping. During the process, the sheet metal blank is clamped by a blank-holder and a small-size smooth-end hemispherical tool moves along a user-specified path to deform the sheet incrementally. Classical three-axis CNC milling machines, dedicated structure or serial robots can be used to perform the forming operation. Whatever the considered machine, large deviations between the theoretical shape and the real shape can be observed after the part unclamping. These deviations are due to both the lack of stiffness of the machine and residual stresses in the part at the end of the forming stage. In this paper, an optimization strategy of the tool path is proposed in order to minimize the elastic springback induced by residual stresses after unclamping. A finite element model of the SPIF process allowing the shape prediction of the formed part with a good accuracy is defined. This model, based on appropriated assumptions, leads to calculation times which remain compatible with an optimization procedure. The proposed optimization method is based on an iterative correction of the tool path. The efficiency of the method is shown by an improvement of the final shape.

Quadcopter Path Following Control Design Using Output Feedback with Command Generator Tracker LOS Based At Square Path

NASA Astrophysics Data System (ADS)

Nugraha, A. T.; Agustinah, T.

2018-01-01

Quadcopter an unstable system, underactuated and nonlinear in quadcopter control research developments become an important focus of attention. In this study, following the path control method for position on the X and Y axis, used structure-Generator Tracker Command (CGT) is tested. Attitude control and position feedback quadcopter is compared using the optimal output. The addition of the H∞ performance optimal output feedback control is used to maintain the stability and robustness of quadcopter. Iterative numerical techniques Linear Matrix Inequality (LMI) is used to find the gain controller. The following path control problems is solved using the method of LQ regulators with output feedback. Simulations show that the control system can follow the paths that have been defined in the form of a reference signal square shape. The result of the simulation suggest that the method which used can bring the yaw angle at the expected value algorithm. Quadcopter can do automatically following path with cross track error mean X=0.5 m and Y=0.2 m.

Using GIS and Google Earth for the creation of the Going-to-the-Sun Road Avalanche Atlas, Glacier National Park, Montana, USA

USGS Publications Warehouse

Peitzsch, Erich H.; Fagre, Daniel B.; Dundas, Mark

2010-01-01

Snow avalanche paths are key geomorphologic features in Glacier National Park, Montana, and an important component of mountain ecosystems: they are isolated within a larger ecosystem, they are continuously disturbed, and they contain unique physical characteristics (Malanson and Butler, 1984). Avalanches impact subalpine forest structure and function, as well as overall biodiversity (Bebi et al., 2009). Because avalanches are dynamic phenomena, avalanche path geometry and spatial extent depend upon climatic regimes. The USGS/GNP Avalanche Program formally began in 2003 as an avalanche forecasting program for the spring opening of the ever-popular Going-to-the-Sun Road (GTSR), which crosses through 37 identified avalanche paths. Avalanche safety and forecasting is a necessary part of the GTSR spring opening procedures. An avalanche atlas detailing topographic parameters and oblique photographs was completed for the GTSR corridor in response to a request from GNP personnel for planning and resource management. Using ArcMap 9.2 GIS software, polygons were created for every avalanche path affecting the GTSR using aerial imagery, field-based observations, and GPS measurements of sub-meter accuracy. Spatial attributes for each path were derived within the GIS. Resulting products include an avalanche atlas book for operational use, a geoPDF of the atlas, and a Google Earth flyover illustrating each path and associated photographs. The avalanche atlas aids park management in worker safety, infrastructure planning, and natural resource protection by identifying avalanche path patterns and location. The atlas was created for operational and planning purposes and is also used as a foundation for research such as avalanche ecology projects and avalanche path runout modeling.

Nanoscale imaging of clinical specimens using pathology-optimized expansion microscopy

PubMed Central

Zhao, Yongxin; Bucur, Octavian; Irshad, Humayun; Chen, Fei; Weins, Astrid; Stancu, Andreea L.; Oh, Eun-Young; DiStasio, Marcello; Torous, Vanda; Glass, Benjamin; Stillman, Isaac E.; Schnitt, Stuart J.; Beck, Andrew H.; Boyden, Edward S.

2017-01-01

Expansion microscopy (ExM), a method for improving the resolution of light microscopy by physically expanding the specimen, has not been applied to clinical tissue samples. Here we report a clinically optimized form of ExM that supports nanoscale imaging of human tissue specimens that have been fixed with formalin, embedded in paraffin, stained with hematoxylin and eosin (H&E), and/or fresh frozen. The method, which we call expansion pathology (ExPath), converts clinical samples into an ExM-compatible state, then applies an ExM protocol with protein anchoring and mechanical homogenization steps optimized for clinical samples. ExPath enables ~70 nm resolution imaging of diverse biomolecules in intact tissues using conventional diffraction-limited microscopes, and standard antibody and fluorescent DNA in situ hybridization reagents. We use ExPath for optical diagnosis of kidney minimal-change disease, which previously required electron microscopy (EM), and demonstrate high-fidelity computational discrimination between early breast neoplastic lesions that to date have challenged human judgment. ExPath may enable the routine use of nanoscale imaging in pathology and clinical research. PMID:28714966

A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

PubMed

Quan, Wei; Fang, Jiancheng

2010-01-01

A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.

Nanoscale imaging of clinical specimens using pathology-optimized expansion microscopy.

PubMed

Zhao, Yongxin; Bucur, Octavian; Irshad, Humayun; Chen, Fei; Weins, Astrid; Stancu, Andreea L; Oh, Eun-Young; DiStasio, Marcello; Torous, Vanda; Glass, Benjamin; Stillman, Isaac E; Schnitt, Stuart J; Beck, Andrew H; Boyden, Edward S

2017-08-01

Expansion microscopy (ExM), a method for improving the resolution of light microscopy by physically expanding a specimen, has not been applied to clinical tissue samples. Here we report a clinically optimized form of ExM that supports nanoscale imaging of human tissue specimens that have been fixed with formalin, embedded in paraffin, stained with hematoxylin and eosin, and/or fresh frozen. The method, which we call expansion pathology (ExPath), converts clinical samples into an ExM-compatible state, then applies an ExM protocol with protein anchoring and mechanical homogenization steps optimized for clinical samples. ExPath enables ∼70-nm-resolution imaging of diverse biomolecules in intact tissues using conventional diffraction-limited microscopes and standard antibody and fluorescent DNA in situ hybridization reagents. We use ExPath for optical diagnosis of kidney minimal-change disease, a process that previously required electron microscopy, and we demonstrate high-fidelity computational discrimination between early breast neoplastic lesions for which pathologists often disagree in classification. ExPath may enable the routine use of nanoscale imaging in pathology and clinical research.

Efficient evaluation of atom tunneling combined with electronic structure calculations.

PubMed

Ásgeirsson, Vilhjálmur; Arnaldsson, Andri; Jónsson, Hannes

2018-03-14

Methodology for finding optimal tunneling paths and evaluating tunneling rates for atomic rearrangements is described. First, an optimal JWKB tunneling path for a system with fixed energy is obtained using a line integral extension of the nudged elastic band method. Then, a calculation of the dynamics along the path is used to determine the temperature at which it corresponds to an optimal Feynman path for thermally activated tunneling (instanton) and a harmonic approximation is used to estimate the transition rate. The method is illustrated with calculations for a modified two-dimensional Müller-Brown surface but is efficient enough to be used in combination with electronic structure calculations of the energy and atomic forces in systems containing many atoms. An example is presented where tunneling is the dominant mechanism well above room temperature as an H 3 BNH 3 molecule dissociates to form H 2 . Also, a solid-state example is presented where density functional theory calculations of H atom tunneling in a Ta crystal give close agreement with experimental measurements on hydrogen diffusion over a wide range in temperature.

Traffic-engineering-aware shortest-path routing and its application in IP-over-WDM networks [Invited

NASA Astrophysics Data System (ADS)

Lee, Youngseok; Mukherjee, Biswanath

2004-03-01

Single shortest-path routing is known to perform poorly for Internet traffic engineering (TE) where the typical optimization objective is to minimize the maximum link load. Splitting traffic uniformly over equal-cost multiple shortest paths in open shortest path first and intermediate system-intermediate system protocols does not always minimize the maximum link load when multiple paths are not carefully selected for the global traffic demand matrix. However, a TE-aware shortest path among all the equal-cost multiple shortest paths between each ingress-egress pair can be selected such that the maximum link load is significantly reduced. IP routers can use the globally optimal TE-aware shortest path without any change to existing routing protocols and without any serious configuration overhead. While calculating TE-aware shortest paths, the destination-based forwarding constraint at a node should be satisfied, because an IP router will forward a packet to the next hop toward the destination by looking up the destination prefix. We present a mathematical problem formulation for finding a set of TE-aware shortest paths for the given network as an integer linear program, and we propose a simple heuristic for solving large instances of the problem. Then we explore the usage of our proposed algorithm for the integrated TE method in IP-over-WDM networks. The proposed algorithm is evaluated through simulations in IP networks as well as in IP-over-WDM networks.

Nb3Sn SRF Cavities for Nuclear Physics Applications

NASA Astrophysics Data System (ADS)

Eremeev, Grigory

2017-01-01

Nuclear physics experiments rely increasingly on accelerators, which employ superconducting RF (SRF) technology. CEBAF, SNS, FRIB, ESS, among others exploit the low surface resistance of SRF cavities to efficiently accelerate particle beams towards experimental targets. Niobium is the cavity material of choice for all current or planned SRF accelerators, but it has been long recognized that other superconductors with high superconducting transition temperatures have the potential to surpass niobium for SRF applications. Among the alternatives, Nb3Sn coated cavities are the most advanced on the path to practical applications: Nb3Sn coatings on R&D cavities have Tc consistently close the optimal 18 K, very low RF surface resistances, and very recently were shown to reach above Hc1 without anomalous RF surface resistance increase. In my talk I will discuss the prospects of Nb3Sn SRF cavities, the research efforts to realize Nb3Sn coatings on practical multi-cell accelerating structures, and the path toward possible inclusion in CEBAF. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics.

Intensity Modulated Radiation Therapy Dose Painting for Localized Prostate Cancer Using {sup 11}C-choline Positron Emission Tomography Scans

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

Chang, Joe H.; University of Melbourne, Victoria; Lim Joon, Daryl

Purpose: To demonstrate the technical feasibility of intensity modulated radiation therapy (IMRT) dose painting using {sup 11}C-choline positron emission tomography PET scans in patients with localized prostate cancer. Methods and Materials: This was an RT planning study of 8 patients with prostate cancer who had {sup 11}C-choline PET scans prior to radical prostatectomy. Two contours were semiautomatically generated on the basis of the PET scans for each patient: 60% and 70% of the maximum standardized uptake values (SUV{sub 60%} and SUV{sub 70%}). Three IMRT plans were generated for each patient: PLAN{sub 78}, which consisted of whole-prostate radiation therapy to 78more » Gy; PLAN{sub 78-90}, which consisted of whole-prostate RT to 78 Gy, a boost to the SUV{sub 60%} to 84 Gy, and a further boost to the SUV{sub 70%} to 90 Gy; and PLAN{sub 72-90}, which consisted of whole-prostate RT to 72 Gy, a boost to the SUV{sub 60%} to 84 Gy, and a further boost to the SUV{sub 70%} to 90 Gy. The feasibility of these plans was judged by their ability to reach prescription doses while adhering to published dose constraints. Tumor control probabilities based on PET scan-defined volumes (TCP{sub PET}) and on prostatectomy-defined volumes (TCP{sub path}), and rectal normal tissue complication probabilities (NTCP) were compared between the plans. Results: All plans for all patients reached prescription doses while adhering to dose constraints. TCP{sub PET} values for PLAN{sub 78}, PLAN{sub 78-90}, and PLAN{sub 72-90} were 65%, 97%, and 96%, respectively. TCP{sub path} values were 71%, 97%, and 89%, respectively. Both PLAN{sub 78-90} and PLAN{sub 72-90} had significantly higher TCP{sub PET} (P=.002 and .001) and TCP{sub path} (P<.001 and .014) values than PLAN{sub 78}. PLAN{sub 78-90} and PLAN{sub 72-90} were not significantly different in terms of TCP{sub PET} or TCP{sub path}. There were no significant differences in rectal NTCPs between the 3 plans. Conclusions: IMRT dose painting for localized prostate cancer using {sup 11}C-choline PET scans is technically feasible. Dose painting results in higher TCPs without higher NTCPs.« less

The application of Markov decision process with penalty function in restaurant delivery robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Hu, Zhen; Wang, Ying

2017-05-01

As the restaurant delivery robot is often in a dynamic and complex environment, including the chairs inadvertently moved to the channel and customers coming and going. The traditional Markov decision process path planning algorithm is not save, the robot is very close to the table and chairs. To solve this problem, this paper proposes the Markov Decision Process with a penalty term called MDPPT path planning algorithm according to the traditional Markov decision process (MDP). For MDP, if the restaurant delivery robot bumps into an obstacle, the reward it receives is part of the current status reward. For the MDPPT, the reward it receives not only the part of the current status but also a negative constant term. Simulation results show that the MDPPT algorithm can plan a more secure path.

Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

NASA Technical Reports Server (NTRS)

Murphy, Michael G.

1991-01-01

Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system.

Tracking trade transactions in water resource systems: A node-arc optimization formulation

NASA Astrophysics Data System (ADS)

Erfani, Tohid; Huskova, Ivana; Harou, Julien J.

2013-05-01

We formulate and apply a multicommodity network flow node-arc optimization model capable of tracking trade transactions in complex water resource systems. The model uses a simple node to node network connectivity matrix and does not require preprocessing of all possible flow paths in the network. We compare the proposed node-arc formulation with an existing arc-path (flow path) formulation and explain the advantages and difficulties of both approaches. We verify the proposed formulation model on a hypothetical water distribution network. Results indicate the arc-path model solves the problem with fewer constraints, but the proposed formulation allows using a simple network connectivity matrix which simplifies modeling large or complex networks. The proposed algorithm allows converting existing node-arc hydroeconomic models that broadly represent water trading to ones that also track individual supplier-receiver relationships (trade transactions).

Optimal investment and location decisions of a firm in a flood risk area using Impulse Control Theory

NASA Astrophysics Data System (ADS)

Grames, Johanna; Grass, Dieter; Kort, Peter; Prskawetz, Alexia

2017-04-01

Flooding events can affect businesses close to rivers, lakes or coasts. This paper provides a partial equilibrium model which helps to understand the optimal location choice for a firm in flood risk areas and its investment strategies. How often, when and how much are firms willing to invest in flood risk protection measures? We apply Impulse Control Theory to solve the model analytically and develop a continuation algorithm to solve the model numerically. Firms always invest in flood defense. The investment increases the higher the flood risk and the more firms also value the future, i.e. the more sustainable they plan. Investments in production capital follow a similar path. Hence, planning in a sustainable way leads to economic growth. Sociohydrological feedbacks are crucial for the location choice of the firm, whereas different economic situations have an impact on investment strategies. If flood defense is already present, e.g. built up by the government, firms move closer to the water and invest less in flood defense, which allows firms to accrue higher expected profits. Firms with a large initial production capital surprisingly try not to keep their market advantage, but rather reduce flood risk by reducing exposed production capital.

Comparison Of Reaction Barriers In Energy And Free Energy For Enzyme Catalysis

NASA Astrophysics Data System (ADS)

Andrés Cisneros, G.; Yang, Weitao

Reaction paths on potential energy surfaces obtained from QM/MM calculations of enzymatic or solution reactions depend on the starting structure employed for the path calculations. The free energies associated with these paths should be more reliable for studying reaction mechanisms, because statistical averages are used. To investigate this, the role of enzyme environment fluctuations on reaction paths has been studied with an ab initio QM/MM method for the first step of the reaction catalyzed by 4-oxalocrotonate tautomerase (4OT). Four minimum energy paths (MEPs) are compared, which have been determined with two different methods. The first path (path A) has been determined with a procedure that combines the nudged elastic band (NEB) method and a second order parallel path optimizer recently developed in our group. The second path (path B) has also been determined by the combined procedure, however, the enzyme environment has been relaxed by molecular dynamics (MD) simulations. The third path (path C) has been determined with the coordinate driving (CD) method, using the enzyme environment from path B. We compare these three paths to a previously determined path (path D) determined with the CD method. In all four cases the QM/MM-FE method (Y. Zhang et al., JCP, 112, 3483) was employed to obtain the free energy barriers for all four paths. In the case of the combined procedure, the reaction path is approximated by a small number of images which are optimized to the MEP in parallel, which results in a reduced computational cost. However, this does not allow the FEP calculation on the MEP. In order to perform FEP calculations on these paths, we introduce a modification to the NEB method that enables the addition of as many extra images to the path as needed for the FEP calculations. The calculated potential energy barriers show differences in the activation barrier between the calculated paths of as much as 5.17 kcal/mol. However, the largest free energy barrier difference is 1.58 kcal/mol. These results show the importance of the inclusion of the environment fluctuation in the calculation of enzymatic activation barriers

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

PubMed

Liu, Zhong; Gao, Xiaoguang; Fu, Xiaowei

2018-05-08

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

A global approach to kinematic path planning to robots with holonomic and nonholonomic constraints

NASA Technical Reports Server (NTRS)

Divelbiss, Adam; Seereeram, Sanjeev; Wen, John T.

1993-01-01

Robots in applications may be subject to holonomic or nonholonomic constraints. Examples of holonomic constraints include a manipulator constrained through the contact with the environment, e.g., inserting a part, turning a crank, etc., and multiple manipulators constrained through a common payload. Examples of nonholonomic constraints include no-slip constraints on mobile robot wheels, local normal rotation constraints for soft finger and rolling contacts in grasping, and conservation of angular momentum of in-orbit space robots. The above examples all involve equality constraints; in applications, there are usually additional inequality constraints such as robot joint limits, self collision and environment collision avoidance constraints, steering angle constraints in mobile robots, etc. The problem of finding a kinematically feasible path that satisfies a given set of holonomic and nonholonomic constraints, of both equality and inequality types is addressed. The path planning problem is first posed as a finite time nonlinear control problem. This problem is subsequently transformed to a static root finding problem in an augmented space which can then be iteratively solved. The algorithm has shown promising results in planning feasible paths for redundant arms satisfying Cartesian path following and goal endpoint specifications, and mobile vehicles with multiple trailers. In contrast to local approaches, this algorithm is less prone to problems such as singularities and local minima.

Minimal entropy probability paths between genome families.

PubMed

Ahlbrandt, Calvin; Benson, Gary; Casey, William

2004-05-01

We develop a metric for probability distributions with applications to biological sequence analysis. Our distance metric is obtained by minimizing a functional defined on the class of paths over probability measures on N categories. The underlying mathematical theory is connected to a constrained problem in the calculus of variations. The solution presented is a numerical solution, which approximates the true solution in a set of cases called rich paths where none of the components of the path is zero. The functional to be minimized is motivated by entropy considerations, reflecting the idea that nature might efficiently carry out mutations of genome sequences in such a way that the increase in entropy involved in transformation is as small as possible. We characterize sequences by frequency profiles or probability vectors, in the case of DNA where N is 4 and the components of the probability vector are the frequency of occurrence of each of the bases A, C, G and T. Given two probability vectors a and b, we define a distance function based as the infimum of path integrals of the entropy function H( p) over all admissible paths p(t), 0 < or = t< or =1, with p(t) a probability vector such that p(0)=a and p(1)=b. If the probability paths p(t) are parameterized as y(s) in terms of arc length s and the optimal path is smooth with arc length L, then smooth and "rich" optimal probability paths may be numerically estimated by a hybrid method of iterating Newton's method on solutions of a two point boundary value problem, with unknown distance L between the abscissas, for the Euler-Lagrange equations resulting from a multiplier rule for the constrained optimization problem together with linear regression to improve the arc length estimate L. Matlab code for these numerical methods is provided which works only for "rich" optimal probability vectors. These methods motivate a definition of an elementary distance function which is easier and faster to calculate, works on non-rich vectors, does not involve variational theory and does not involve differential equations, but is a better approximation of the minimal entropy path distance than the distance //b-a//(2). We compute minimal entropy distance matrices for examples of DNA myostatin genes and amino-acid sequences across several species. Output tree dendograms for our minimal entropy metric are compared with dendograms based on BLAST and BLAST identity scores.

The Optimal Capital Stock and Consumption Evolution for Non Zero Consumers Growth Rate in the Framework of Ramsey Model on Finite Horizon

NASA Astrophysics Data System (ADS)

Bonchiş, N.; Balint, Şt.

2010-09-01

In this paper the Ramsey optimal growth of the capital stock and consumption on finite horizon is analyzed when the growth rate of consumers is strictly positive. The main purpose is to establish the dependence of the optimal capital stock and consumption evolution on the growth rate of consumers. The analysis reveals: for any initial value k0≥0 there exists a unique optimal evolution path of length N+1 for the capital stock; if k0 is strictly positive then all the elements of the optimal capital stock evolution path are strictly positives except the last one which is zero; the optimal capital stock evolution of length N+1 starting from k0≥0 satisfies the Euler equation; the value function VN is strictly increasing, strictly concave and continuous on R+. The family of functions {VN-T}T = 0…N-1 satisfies the Bellman equation and it is the unique solution of this equation which is both continuous and satisfies the transversality condition. The Mangasarian Lemma is also satisfied. For N tending to infinity the optimal evolution path of length N of the capital stock tends to those on the infinite time horizon. For any k0>0 the value function in k0 decreases when the consumers growth rate increases.

Processor Would Find Best Paths On Map

NASA Technical Reports Server (NTRS)

Eberhardt, Silvio P.

1990-01-01

Proposed very-large-scale integrated (VLSI) circuit image-data processor finds path of least cost from specified origin to any destination on map. Cost of traversal assigned to each picture element of map. Path of least cost from originating picture element to every other picture element computed as path that preserves as much as possible of signal transmitted by originating picture element. Dedicated microprocessor at each picture element stores cost of traversal and performs its share of computations of paths of least cost. Least-cost-path problem occurs in research, military maneuvers, and in planning routes of vehicles.

Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

PubMed Central

Ravankar, Abhijeet; Ravankar, Ankit A.; Kobayashi, Yukinori; Emaru, Takanori

2017-01-01

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments. PMID:28678193

Potential dosimetric benefit of dose-warping based 4D planning compared to conventional 3D planning in liver stereotactic body radiotherapy (SBRT)

NASA Astrophysics Data System (ADS)

Yeo, U. J.; Taylor, M. L.; Kron, T.; Pham, D.; Siva, S.; Franich, R. D.

2013-06-01

Respiratory motion induces dosimetric uncertainties for thoracic and abdominal cancer radiotherapy (RT) due to deforming and moving anatomy. This study investigates the extent of dosimetric differences between conventional 3D treatment planning and path-integrated 4D treatment planning in liver stereotactic body radiotherapy (SBRT). Respiratory-correlated 4DCT image sets with 10 phases were acquired for patients with liver tumours. Path-integrated 4D dose accumulation was performed using dose-warping techniques based on deformable image registration. Dose-volume histogram analysis demonstrated that the 3D planning approach overestimated doses to targets by up to 24% and underestimated dose to normal liver by ~4.5%, compared to the 4D planning methodology. Therefore, 4D planning has the potential to quantify such issues of under- and/or over-dosage and improve treatment accuracy.

PRP Comments for ICF Q1/Q2 FY17 Experiments 3/10/16

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

Kauffman, R.

2016-04-14

The PRP generally endorsed the Program plan during the short time for discussions. We agree that the strategy to develop a hohlraum that is symmetric and has low laser-plasma instabilities and to develop an alternative method for supporting the capsule is the best path forward for making progress in understanding ignition performance. The Program is oriented toward a milestone in 2020 for “determining the efficacy of NIF for ignition and credible physics-scaling to multi-megajoule yields for all ICF approaches.” We are concerned that the time and resources are not sufficient to vet all of the various approaches that are beingmore » pursued to make an informed decision by this date. For NIF to meet this goal, a process will be needed to to select the most promising paths forward. We recommend that the Program develop this process for selecting the path forward to optimize resources. We were glad to see that the direct drive program took our comments under consideration. We think that the proposed experiments have the program headed in a better direction. The PRP had only a short time to discuss the detailed experimental proposals. The following are comments on the detailed proposals. We did not have time to discuss them as a group. They represent individual opinions and provided to you as feedback to your proposals.« less

Echolocating bats use future-target information for optimal foraging.

PubMed

Fujioka, Emyo; Aihara, Ikkyu; Sumiya, Miwa; Aihara, Kazuyuki; Hiryu, Shizuko

2016-04-26

When seeing or listening to an object, we aim our attention toward it. While capturing prey, many animal species focus their visual or acoustic attention toward the prey. However, for multiple prey items, the direction and timing of attention for effective foraging remain unknown. In this study, we adopted both experimental and mathematical methodology with microphone-array measurements and mathematical modeling analysis to quantify the attention of echolocating bats that were repeatedly capturing airborne insects in the field. Here we show that bats select rational flight paths to consecutively capture multiple prey items. Microphone-array measurements showed that bats direct their sonar attention not only to the immediate prey but also to the next prey. In addition, we found that a bat's attention in terms of its flight also aims toward the next prey even when approaching the immediate prey. Numerical simulations revealed a possibility that bats shift their flight attention to control suitable flight paths for consecutive capture. When a bat only aims its flight attention toward its immediate prey, it rarely succeeds in capturing the next prey. These findings indicate that bats gain increased benefit by distributing their attention among multiple targets and planning the future flight path based on additional information of the next prey. These experimental and mathematical studies allowed us to observe the process of decision making by bats during their natural flight dynamics.

Detumbling control for kinematically redundant space manipulator post-grasping a rotational satellite

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich

2017-12-01

The objective of this paper is to establish a detumbling strategy and a coordination control scheme for a kinematically redundant space manipulator post-grasping a rotational satellite. First, the dynamics of the kinematically redundant space robot after grasping the target is presented, which lays the foundation for the coordination controller design. Subsequently, optimal detumbling and motion planning strategy for the post-capture phase is proposed based on the quartic Bézier curves and adaptive differential evolution (DE) algorithm subject to the specific constraints. Both detumbling time and control torques are taken into account for the generation of the optimal detumbling strategy. Furthermore, a coordination control scheme is presented to track the designed reference path while regulating the attitude of the chaser to a desired value, which successfully dumps the initial angular velocity of the rotational satellite and controls the base attitude synchronously. Simulation results are presented for detumbling a target with rotational motion using a 7 degree-of-freedom (DOF) redundant space manipulator, which demonstrates the effectiveness of the proposed method.

Autonomous navigation and control of a Mars rover

NASA Technical Reports Server (NTRS)

Miller, D. P.; Atkinson, D. J.; Wilcox, B. H.; Mishkin, A. H.

1990-01-01

A Mars rover will need to be able to navigate autonomously kilometers at a time. This paper outlines the sensing, perception, planning, and execution monitoring systems that are currently being designed for the rover. The sensing is based around stereo vision. The interpretation of the images use a registration of the depth map with a global height map provided by an orbiting spacecraft. Safe, low energy paths are then planned through the map, and expectations of what the rover's articulation sensors should sense are generated. These expectations are then used to ensure that the planned path is correctly being executed.

GOS hook type wells, directional planning, techniques applied and problems encountered

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

A /Azim, M.; Fahmy, H.; Salem, A.

1995-10-01

This paper addresses the various aspects of hook type wells introduced and drilled within GUPCO operations during he last two years. The first well of this category was October-G10, drilled in October 1992 from October ``G`` platform to a target point in the Nubia formation. Several wells of the same type have been drilled through 1993 and 1994. This group includes October-H1, Ramadan 3-57, July 62-69 and SB 374-3. Drilling hook type well profiles has resulted in increased production and more reserve recovery. The driving force behind using this profile was the reservoir requirements where it was required to hitmore » a target within few meters at a certain angle and direction. Torque and drag models have been used to optimize well path planning, resulting in lower torque and drag values. Daily pot appraisal of the drilling operations to monitor hole cleaning effectiveness. Combination of advanced steerable systems and PDC bits enabled GUPCO to drill these wells cost effectively.« less

Path Analysis and Residual Plotting as Methods of Environmental Scanning in Higher Education: An Illustration with Applications and Enrollments.

ERIC Educational Resources Information Center

Morcol, Goktug; McLaughlin, Gerald W.

1990-01-01

The study proposes using path analysis and residual plotting as methods supporting environmental scanning in strategic planning for higher education institutions. Path models of three levels of independent variables are developed. Dependent variables measuring applications and enrollments at Virginia Polytechnic Institute and State University are…

A Path Planning and Obstacle Avoidance Hybrid System Using a Connectionist Network

DTIC Science & Technology

1990-06-01

Department lele7 Prfessor of Aerospace Sciences and Mathematical Sciences Houston, Texas June, 1990 Abstract A PATH PLANNING AND OBSTACLE AVOIDANCE HYBRID...See Weiland (1989), Wu (1989), Norwood (1989), Cheatham (1987 & 1989), Adnan (1990), and Regalbuto (1988 & 1990).] Possible applications of this...neuron model’s output can be described mathematically as: Yj(t+ At) =sgn ijXi(t)-O Other non-linearity functions, such as and the sigmoid/ logistics

Robot Path Planning in Uncertain Environments: A Language Measure-theoretic Approach

DTIC Science & Technology

2014-01-01

Paper DS-14-1028 to appear in the Special Issue on Stochastic Models, Control and Algorithms in Robotics, ASME Journal of Dynamic Systems...Measurement and Control Robot Path Planning in Uncertain Environments: A Language Measure-theoretic Approach⋆ Devesh K. Jha† Yue Li† Thomas A. Wettergren‡† Asok...algorithm, called ν⋆, that was formulated in the framework of probabilistic finite state automata (PFSA) and language measure from a control -theoretic

Evaluation of a New Backtrack Free Path Planning Algorithm for Manipulators

NASA Astrophysics Data System (ADS)

Islam, Md. Nazrul; Tamura, Shinsuke; Murata, Tomonari; Yanase, Tatsuro

This paper evaluates a newly proposed backtrack free path planning algorithm (BFA) for manipulators. BFA is an exact algorithm, i.e. it is resolution complete. Different from existing resolution complete algorithms, its computation time and memory space are proportional to the number of arms. Therefore paths can be calculated within practical and predetermined time even for manipulators with many arms, and it becomes possible to plan complicated motions of multi-arm manipulators in fully automated environments. The performance of BFA is evaluated for 2-dimensional environments while changing the number of arms and obstacle placements. Its performance under locus and attitude constraints is also evaluated. Evaluation results show that the computation volume of the algorithm is almost the same as the theoretical one, i.e. it increases linearly with the number of arms even in complicated environments. Moreover BFA achieves the constant performance independent of environments.

Adaptive Gait Control for a Quadruped Robot on 3D Path Planning

NASA Astrophysics Data System (ADS)

Igarashi, Hiroshi; Kakikura, Masayoshi

A legged walking robot is able to not only move on irregular terrain but also change its posture. For example, the robot can pass under overhead obstacles by crouching. The purpose of our research is to realize efficient path planning with a quadruped robot. Therefore, the path planning is expected to extended in three dimensions because of the mobility. However, some issues of the quadruped robot, which are instability, workspace limitation, deadlock and slippage, complicate realizing such application. In order to improve these issues and reinforce the mobility, a new static gait pattern for a quadruped robot, called TFG: Trajectory Following Gait, is proposed. The TFG intends to obtain high controllability like a wheel robot. Additionally, the TFG allows to change it posture during the walk. In this paper, some experimental results show that the TFG improves the issues and it is available for efficient locomotion in three dimensional environment.

Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States

DTIC Science & Technology

2016-04-28

Contract # N00014-14-C-0004 Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States Progress Report...Aviation (ONR BAA12-SN-0028). This project addresses the Sea Based Aviation (SBA) initiative in Advanced Handling Qualities for Rotorcraft. Landing a...and a degraded visual environment, workload during the landing task begins to approach the limits of a human pilot’s capability. It is a similarly

A statistical-based scheduling algorithm in automated data path synthesis

NASA Technical Reports Server (NTRS)

Jeon, Byung Wook; Lursinsap, Chidchanok

1992-01-01

In this paper, we propose a new heuristic scheduling algorithm based on the statistical analysis of the cumulative frequency distribution of operations among control steps. It has a tendency of escaping from local minima and therefore reaching a globally optimal solution. The presented algorithm considers the real world constraints such as chained operations, multicycle operations, and pipelined data paths. The result of the experiment shows that it gives optimal solutions, even though it is greedy in nature.

Light extraction efficiency of GaN-based LED with pyramid texture by using ray path analysis.

PubMed

Pan, Jui-Wen; Wang, Chia-Shen

2012-09-10

We study three different gallium-nitride (GaN) based light emitting diode (LED) cases based on the different locations of the pyramid textures. In case 1, the pyramid texture is located on the sapphire top surface, in case 2, the pyramid texture is locate on the P-GaN top surface, while in case 3, the pyramid texture is located on both the sapphire and P-GaN top surfaces. We study the relationship between the light extraction efficiency (LEE) and angle of slant of the pyramid texture. The optimization of total LEE was highest for case 3 among the three cases. Moreover, the seven escape paths along which most of the escaped photon flux propagated were selected in a simulation of the LEDs. The seven escape paths were used to estimate the slant angle for the optimization of LEE and to precisely analyze the photon escape path.

Inverse-optimized 3D conformal planning: Minimizing complexity while achieving equivalence with beamlet IMRT in multiple clinical sites

PubMed Central

Fraass, Benedick A.; Steers, Jennifer M.; Matuszak, Martha M.; McShan, Daniel L.

2012-01-01

Purpose: Inverse planned intensity modulated radiation therapy (IMRT) has helped many centers implement highly conformal treatment planning with beamlet-based techniques. The many comparisons between IMRT and 3D conformal (3DCRT) plans, however, have been limited because most 3DCRT plans are forward-planned while IMRT plans utilize inverse planning, meaning both optimization and delivery techniques are different. This work avoids that problem by comparing 3D plans generated with a unique inverse planning method for 3DCRT called inverse-optimized 3D (IO-3D) conformal planning. Since IO-3D and the beamlet IMRT to which it is compared use the same optimization techniques, cost functions, and plan evaluation tools, direct comparisons between IMRT and simple, optimized IO-3D plans are possible. Though IO-3D has some similarity to direct aperture optimization (DAO), since it directly optimizes the apertures used, IO-3D is specifically designed for 3DCRT fields (i.e., 1–2 apertures per beam) rather than starting with IMRT-like modulation and then optimizing aperture shapes. The two algorithms are very different in design, implementation, and use. The goals of this work include using IO-3D to evaluate how close simple but optimized IO-3D plans come to nonconstrained beamlet IMRT, showing that optimization, rather than modulation, may be the most important aspect of IMRT (for some sites). Methods: The IO-3D dose calculation and optimization functionality is integrated in the in-house 3D planning/optimization system. New features include random point dose calculation distributions, costlet and cost function capabilities, fast dose volume histogram (DVH) and plan evaluation tools, optimization search strategies designed for IO-3D, and an improved, reimplemented edge/octree calculation algorithm. The IO-3D optimization, in distinction to DAO, is designed to optimize 3D conformal plans (one to two segments per beam) and optimizes MLC segment shapes and weights with various user-controllable search strategies which optimize plans without beamlet or pencil beam approximations. IO-3D allows comparisons of beamlet, multisegment, and conformal plans optimized using the same cost functions, dose points, and plan evaluation metrics, so quantitative comparisons are straightforward. Here, comparisons of IO-3D and beamlet IMRT techniques are presented for breast, brain, liver, and lung plans. Results: IO-3D achieves high quality results comparable to beamlet IMRT, for many situations. Though the IO-3D plans have many fewer degrees of freedom for the optimization, this work finds that IO-3D plans with only one to two segments per beam are dosimetrically equivalent (or nearly so) to the beamlet IMRT plans, for several sites. IO-3D also reduces plan complexity significantly. Here, monitor units per fraction (MU/Fx) for IO-3D plans were 22%–68% less than that for the 1 cm × 1 cm beamlet IMRT plans and 72%–84% than the 0.5 cm × 0.5 cm beamlet IMRT plans. Conclusions: The unique IO-3D algorithm illustrates that inverse planning can achieve high quality 3D conformal plans equivalent (or nearly so) to unconstrained beamlet IMRT plans, for many sites. IO-3D thus provides the potential to optimize flat or few-segment 3DCRT plans, creating less complex optimized plans which are efficient and simple to deliver. The less complex IO-3D plans have operational advantages for scenarios including adaptive replanning, cases with interfraction and intrafraction motion, and pediatric patients. PMID:22755717

The path of placement of a removable partial denture: a microscope based approach to survey and design

PubMed Central

2015-01-01

This article reviews the topic of how to identify and develop a removable partial denture (RPD) path of placement, and provides a literature review of the concept of the RPD path of placement, also known as the path of insertion. An optimal RPD path of placement, guided by mutually parallel guide planes, ensures that the RPD flanges fit intimately over edentulous ridge structures and that the framework fits intimately with guide plane surfaces, which prevents food collecting empty spaces between the intaglio surface of the framework and intraoral surfaces, and ensures that RPD clasps engage adequate numbers of tooth undercuts to ensure RPD retention. The article covers topics such as the causes of obstructions to RPD intra-oral seating, the causes of food collecting empty spaces that may exist around an RPD, and how to identify if a guide plane is parallel with the projected RPD path of placement. The article presents a method of using a surgical operating microscope, or high magnification (6-8x or greater) binocular surgical loupes telescopes, combined with co-axial illumination, to identify a preliminary path of placement for an arch. This preliminary path of placement concept may help to guide a dentist or a dental laboratory technician when surveying a master cast of the arch to develop an RPD path of placement, or in verifying that intra-oral contouring has aligned teeth surfaces optimally with the RPD path of placement. In dentistry, a well-fitting RPD reduces long-term periodontal or structural damage to abutment teeth. PMID:25722842

Motion planning for autonomous vehicle based on radial basis function neural network in unstructured environment.

PubMed

Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao

2014-09-18

The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.

Motion Planning for Autonomous Vehicle Based on Radial Basis Function Neural Network in Unstructured Environment

PubMed Central

Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao

2014-01-01

The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality. PMID:25237902

Robust path planning for flexible needle insertion using Markov decision processes.

PubMed

Tan, Xiaoyu; Yu, Pengqian; Lim, Kah-Bin; Chui, Chee-Kong

2018-05-11

Flexible needle has the potential to accurately navigate to a treatment region in the least invasive manner. We propose a new planning method using Markov decision processes (MDPs) for flexible needle navigation that can perform robust path planning and steering under the circumstance of complex tissue-needle interactions. This method enhances the robustness of flexible needle steering from three different perspectives. First, the method considers the problem caused by soft tissue deformation. The method then resolves the common needle penetration failure caused by patterns of targets, while the last solution addresses the uncertainty issues in flexible needle motion due to complex and unpredictable tissue-needle interaction. Computer simulation and phantom experimental results show that the proposed method can perform robust planning and generate a secure control policy for flexible needle steering. Compared with a traditional method using MDPs, the proposed method achieves higher accuracy and probability of success in avoiding obstacles under complicated and uncertain tissue-needle interactions. Future work will involve experiment with biological tissue in vivo. The proposed robust path planning method can securely steer flexible needle within soft phantom tissues and achieve high adaptability in computer simulation.

Operation costs and pollutant emissions reduction by definition of new collection scheduling and optimization of MSW collection routes using GIS. The case study of Barreiro, Portugal.

PubMed

Zsigraiova, Zdena; Semiao, Viriato; Beijoco, Filipa

2013-04-01

This work proposes an innovative methodology for the reduction of the operation costs and pollutant emissions involved in the waste collection and transportation. Its innovative feature lies in combining vehicle route optimization with that of waste collection scheduling. The latter uses historical data of the filling rate of each container individually to establish the daily circuits of collection points to be visited, which is more realistic than the usual assumption of a single average fill-up rate common to all the system containers. Moreover, this allows for the ahead planning of the collection scheduling, which permits a better system management. The optimization process of the routes to be travelled makes recourse to Geographical Information Systems (GISs) and uses interchangeably two optimization criteria: total spent time and travelled distance. Furthermore, rather than using average values, the relevant parameters influencing fuel consumption and pollutant emissions, such as vehicle speed in different roads and loading weight, are taken into consideration. The established methodology is applied to the glass-waste collection and transportation system of Amarsul S.A., in Barreiro. Moreover, to isolate the influence of the dynamic load on fuel consumption and pollutant emissions a sensitivity analysis of the vehicle loading process is performed. For that, two hypothetical scenarios are tested: one with the collected volume increasing exponentially along the collection path; the other assuming that the collected volume decreases exponentially along the same path. The results evidence unquestionable beneficial impacts of the optimization on both the operation costs (labor and vehicles maintenance and fuel consumption) and pollutant emissions, regardless the optimization criterion used. Nonetheless, such impact is particularly relevant when optimizing for time yielding substantial improvements to the existing system: potential reductions of 62% for the total spent time, 43% for the fuel consumption and 40% for the emitted pollutants. This results in total cost savings of 57%, labor being the greatest contributor, representing over €11,000 per year for the two vehicles collecting glass-waste. Moreover, it is shown herein that the dynamic loading process of the collection vehicle impacts on both the fuel consumption and on pollutant emissions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Bio-Inspired Genetic Algorithms with Formalized Crossover Operators for Robotic Applications.

PubMed

Zhang, Jie; Kang, Man; Li, Xiaojuan; Liu, Geng-Yang

2017-01-01

Genetic algorithms are widely adopted to solve optimization problems in robotic applications. In such safety-critical systems, it is vitally important to formally prove the correctness when genetic algorithms are applied. This paper focuses on formal modeling of crossover operations that are one of most important operations in genetic algorithms. Specially, we for the first time formalize crossover operations with higher-order logic based on HOL4 that is easy to be deployed with its user-friendly programing environment. With correctness-guaranteed formalized crossover operations, we can safely apply them in robotic applications. We implement our technique to solve a path planning problem using a genetic algorithm with our formalized crossover operations, and the results show the effectiveness of our technique.

Decentralized Bayesian search using approximate dynamic programming methods.

PubMed

Zhao, Yijia; Patek, Stephen D; Beling, Peter A

2008-08-01

We consider decentralized Bayesian search problems that involve a team of multiple autonomous agents searching for targets on a network of search points operating under the following constraints: 1) interagent communication is limited; 2) the agents do not have the opportunity to agree in advance on how to resolve equivalent but incompatible strategies; and 3) each agent lacks the ability to control or predict with certainty the actions of the other agents. We formulate the multiagent search-path-planning problem as a decentralized optimal control problem and introduce approximate dynamic heuristics that can be implemented in a decentralized fashion. After establishing some analytical properties of the heuristics, we present computational results for a search problem involving two agents on a 5 x 5 grid.

The Edge-Disjoint Path Problem on Random Graphs by Message-Passing.

PubMed

Altarelli, Fabrizio; Braunstein, Alfredo; Dall'Asta, Luca; De Bacco, Caterina; Franz, Silvio

2015-01-01

We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length.

The Edge-Disjoint Path Problem on Random Graphs by Message-Passing

PubMed Central

2015-01-01

We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length. PMID:26710102

IT Workforce: Key Practices Help Ensure Strong Integrated Program Teams; Selected Departments Need to Assess Skill Gaps

DTIC Science & Technology

2016-11-01

personnel, career paths for program managers, plans to strengthen program management, and use of special hiring authorities) Monitor and report...agencies with direct hiring authority for program managers and directed OPM to create a specialized career path. OMB also tasked agencies with...guidance for developing career paths for IT program managers.14 OPM’s career path guide was to build upon its IT Program Management Competency Model

Dijkstra Methode for Optimalize Recommendation System of Garbage Transportation Time in Surakarta City

NASA Astrophysics Data System (ADS)

Hartatik; Purbayu, A.; Triyono, L.

2018-03-01

Major problem that often occurs in waste transportation in each region is the route of garbage transportation. Determination of this route should become a major concern because it affects fuel consumption and also the working time from the employee. Therefore, in this research we will develop an application to optimize with pigeonhole and dijsktra algorithm. Pigeonhole algorithm is used to determine which garbage trucks should be taken in a particular TPS. Time optimization is done by determining the shortest path that can be skipped for each garbage truck. Data generated from Pigeonhole then used to determine the shortest path by using Dijkstra algorithm.

Robot path planning using expert systems and machine vision

NASA Astrophysics Data System (ADS)

Malone, Denis E.; Friedrich, Werner E.

1992-02-01

This paper describes a system developed for the robotic processing of naturally variable products. In order to plan the robot motion path it was necessary to use a sensor system, in this case a machine vision system, to observe the variations occurring in workpieces and interpret this with a knowledge based expert system. The knowledge base was acquired by carrying out an in-depth study of the product using examination procedures not available in the robotic workplace and relates the nature of the required path to the information obtainable from the machine vision system. The practical application of this system to the processing of fish fillets is described and used to illustrate the techniques.

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

Dorum, O.H.; Hoover, A.; Jones, J.P.

This paper addresses some issues in the development of sensor-based systems for mobile robot navigation which use range imaging sensors as the primary source for geometric information about the environment. In particular, we describe a model of scanning laser range cameras which takes into account the properties of the mechanical system responsible for image formation and a calibration procedure which yields improved accuracy over previous models. In addition, we describe an algorithm which takes the limitations of these sensors into account in path planning and path execution. In particular, range imaging sensors are characterized by a limited field of viewmore » and a standoff distance -- a minimum distance nearer than which surfaces cannot be sensed. These limitations can be addressed by enriching the concept of configuration space to include information about what can be sensed from a given configuration, and using this information to guide path planning and path following.« less

Neurosurgical robotic arm drilling navigation system.

PubMed

Lin, Chung-Chih; Lin, Hsin-Cheng; Lee, Wen-Yo; Lee, Shih-Tseng; Wu, Chieh-Tsai

2017-09-01

The aim of this work was to develop a neurosurgical robotic arm drilling navigation system that provides assistance throughout the complete bone drilling process. The system comprised neurosurgical robotic arm navigation combining robotic and surgical navigation, 3D medical imaging based surgical planning that could identify lesion location and plan the surgical path on 3D images, and automatic bone drilling control that would stop drilling when the bone was to be drilled-through. Three kinds of experiment were designed. The average positioning error deduced from 3D images of the robotic arm was 0.502 ± 0.069 mm. The correlation between automatically and manually planned paths was 0.975. The average distance error between automatically planned paths and risky zones was 0.279 ± 0.401 mm. The drilling auto-stopping algorithm had 0.00% unstopped cases (26.32% in control group 1) and 70.53% non-drilled-through cases (8.42% and 4.21% in control groups 1 and 2). The system may be useful for neurosurgical robotic arm drilling navigation. Copyright © 2016 John Wiley & Sons, Ltd.

Vector-model-supported approach in prostate plan optimization

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

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100more » previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.« less

The use of 3-D sensing techniques for on-line collision-free path planning

NASA Technical Reports Server (NTRS)

Hayward, V.; Aubry, S.; Jasiukajc, Z.

1987-01-01

The state of the art in collision prevention for manipulators with revolute joints, showing that it is a particularly computationally hard problem, is discussed. Based on the analogy with other hard or undecidable problems such as theorem proving, an extensible multi-resolution architecture for path planning, based on a collection of weak methods is proposed. Finally, the role that sensors can play for an on-line use of sensor data is examined.

Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States

DTIC Science & Technology

2016-08-12

Performing Organization: The Pennsylvania State University Department of Aerospace Engineering 231C Hammond Building University Park, PA 16802 Attn...Plant Models Used in the Study The H-60 class model was developed and distributed by ART to both NAVAIR and Penn State research teams. The model...To) 07 109 I 201 4 tD 07 I 08 12016 ’t TITLE AND SUBTITLE Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States

Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States

DTIC Science & Technology

2017-04-15

50 0 50 Singular Values Frequency (rad/s) S in g u la r V a lu e s ( d B ) controller . The non -output variables can be estimated by reliable linear...Contract # N00014-14-C-0004 Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States Progress Report...recovery of a VTOL UAV. There is a clear need for additional levels of stability and control augmentation and, ultimately, fully autonomous landing

Maximal Sensitive Dependence and the Optimal Path to Epidemic Extinction

DTIC Science & Technology

2010-01-01

1996; Elgart and Kamenev, 2004). Instead, in this article, we will employ an eikonal approximation to recast the problem in terms of an effective...a control strategy on the extinction rate can be determined by its effect on the optimal path (Dykman et al., 2008). Through the use of the eikonal ...the solution of Eqs. (6a)–(6b) in the eikonal form (Elgart and Kamenev, 2004; Doering et al., 2005; Kubo et al., 1973; Wentzell, 1976; Gang, 1987

Fuzzy logic path planning system for collision avoidance by an autonomous rover vehicle

NASA Technical Reports Server (NTRS)

Murphy, Michael G.

1993-01-01

The Space Exploration Initiative of the United States will make great demands upon NASA and its limited resources. One aspect of great importance will be providing for autonomous (unmanned) operation of vehicles and/or subsystems in space flight and surface exploration. An additional, complicating factor is that much of the need for autonomy of operation will take place under conditions of great uncertainty or ambiguity. Issues in developing an autonomous collision avoidance subsystem within a path planning system for application in a remote, hostile environment that does not lend itself well to remote manipulation by Earth-based telecommunications is addressed. A good focus is unmanned surface exploration of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. Four major issues addressed are (1) avoidance of a fuzzy moving obstacle; (2) backoff from a deadend in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system. Examples of the need for collision avoidance by an autonomous rover vehicle on the surface of Mars with a moving obstacle would be wind-blown debris, surface flow or anomalies due to subsurface disturbances, another vehicle, etc. The other issues of backoff, sensor fusion, and adaptive learning are important in the overall path planning system.

Multiresolution motion planning for autonomous agents via wavelet-based cell decompositions.

PubMed

Cowlagi, Raghvendra V; Tsiotras, Panagiotis

2012-10-01

We present a path- and motion-planning scheme that is "multiresolution" both in the sense of representing the environment with high accuracy only locally and in the sense of addressing the vehicle kinematic and dynamic constraints only locally. The proposed scheme uses rectangular multiresolution cell decompositions, efficiently generated using the wavelet transform. The wavelet transform is widely used in signal and image processing, with emerging applications in autonomous sensing and perception systems. The proposed motion planner enables the simultaneous use of the wavelet transform in both the perception and in the motion-planning layers of vehicle autonomy, thus potentially reducing online computations. We rigorously prove the completeness of the proposed path-planning scheme, and we provide numerical simulation results to illustrate its efficacy.

Epidemic extinction paths in complex networks

NASA Astrophysics Data System (ADS)

Hindes, Jason; Schwartz, Ira B.

2017-05-01

We study the extinction of long-lived epidemics on finite complex networks induced by intrinsic noise. Applying analytical techniques to the stochastic susceptible-infected-susceptible model, we predict the distribution of large fluctuations, the most probable or optimal path through a network that leads to a disease-free state from an endemic state, and the average extinction time in general configurations. Our predictions agree with Monte Carlo simulations on several networks, including synthetic weighted and degree-distributed networks with degree correlations, and an empirical high school contact network. In addition, our approach quantifies characteristic scaling patterns for the optimal path and distribution of large fluctuations, both near and away from the epidemic threshold, in networks with heterogeneous eigenvector centrality and degree distributions.

Epidemic extinction paths in complex networks.

PubMed

Hindes, Jason; Schwartz, Ira B

2017-05-01

We study the extinction of long-lived epidemics on finite complex networks induced by intrinsic noise. Applying analytical techniques to the stochastic susceptible-infected-susceptible model, we predict the distribution of large fluctuations, the most probable or optimal path through a network that leads to a disease-free state from an endemic state, and the average extinction time in general configurations. Our predictions agree with Monte Carlo simulations on several networks, including synthetic weighted and degree-distributed networks with degree correlations, and an empirical high school contact network. In addition, our approach quantifies characteristic scaling patterns for the optimal path and distribution of large fluctuations, both near and away from the epidemic threshold, in networks with heterogeneous eigenvector centrality and degree distributions.

Incorporating target registration error into robotic bone milling

NASA Astrophysics Data System (ADS)

Siebold, Michael A.; Dillon, Neal P.; Webster, Robert J.; Fitzpatrick, J. Michael

2015-03-01

Robots have been shown to be useful in assisting surgeons in a variety of bone drilling and milling procedures. Examples include commercial systems for joint repair or replacement surgeries, with in vitro feasibility recently shown for mastoidectomy. Typically, the robot is guided along a path planned on a CT image that has been registered to the physical anatomy in the operating room, which is in turn registered to the robot. The registrations often take advantage of the high accuracy of fiducial registration, but, because no real-world registration is perfect, the drill guided by the robot will inevitably deviate from its planned path. The extent of the deviation can vary from point to point along the path because of the spatial variation of target registration error. The allowable deviation can also vary spatially based on the necessary safety margin between the drill tip and various nearby anatomical structures along the path. Knowledge of the expected spatial distribution of registration error can be obtained from theoretical models or experimental measurements and used to modify the planned path. The objective of such modifications is to achieve desired probabilities for sparing specified structures. This approach has previously been studied for drilling straight holes but has not yet been generalized to milling procedures, such as mastoidectomy, in which cavities of more general shapes must be created. In this work, we present a general method for altering any path to achieve specified probabilities for any spatial arrangement of structures to be protected. We validate the method via numerical simulations in the context of mastoidectomy.

The eye of the storm: Balancing my storm of family, career and self

NASA Astrophysics Data System (ADS)

Horton, K. Renee

2008-03-01

In knowing that the path I travel is not the usual path traveled by most; this has turned out to be the best path for me and my family. It is very important to prioritize what is important to you and then define the best path for you versus choosing a path and the path chooses your prioritizes. Coming from a loving and supportive middle class upbringing created a deep sense of family and the importance of family. Early in my life I was determined to have children and a career. Over the last ten years there have been several obstacles to overcome in my storm, but with careful planning, due diligence, and a support system to help maintain calm at the center of my storm I have been able to achieve my goals of pursuing my Doctorate. A complete research plan was put into place into choosing the institution that I would further my academic endeavors in the same manner in which my dissertation research topic has been defined. Just as any successful business, all persons involved in my future success were consulted with equal input into the new endeavor with the full understanding of what this new plan entailed. We decided on the University of Alabama for several reasons: location, weather, flexibility, policies, research and my ability to make a change in the face of science. According to my advisor, I will do that in about two and half years at my graduation ceremony when I become the first African American to receive a PhD in Material Science from the University of Alabama.

Incorporating Target Registration Error Into Robotic Bone Milling

PubMed Central

Siebold, Michael A.; Dillon, Neal P.; Webster, Robert J.; Fitzpatrick, J. Michael

2015-01-01

Robots have been shown to be useful in assisting surgeons in a variety of bone drilling and milling procedures. Examples include commercial systems for joint repair or replacement surgeries, with in vitro feasibility recently shown for mastoidectomy. Typically, the robot is guided along a path planned on a CT image that has been registered to the physical anatomy in the operating room, which is in turn registered to the robot. The registrations often take advantage of the high accuracy of fiducial registration, but, because no real-world registration is perfect, the drill guided by the robot will inevitably deviate from its planned path. The extent of the deviation can vary from point to point along the path because of the spatial variation of target registration error. The allowable deviation can also vary spatially based on the necessary safety margin between the drill tip and various nearby anatomical structures along the path. Knowledge of the expected spatial distribution of registration error can be obtained from theoretical models or experimental measurements and used to modify the planned path. The objective of such modifications is to achieve desired probabilities for sparing specified structures. This approach has previously been studied for drilling straight holes but has not yet been generalized to milling procedures, such as mastoidectomy, in which cavities of more general shapes must be created. In this work, we present a general method for altering any path to achieve specified probabilities for any spatial arrangement of structures to be protected. We validate the method via numerical simulations in the context of mastoidectomy. PMID:26692630

A PLM-based automated inspection planning system for coordinate measuring machine

NASA Astrophysics Data System (ADS)

Zhao, Haibin; Wang, Junying; Wang, Boxiong; Wang, Jianmei; Chen, Huacheng

2006-11-01

With rapid progress of Product Lifecycle Management (PLM) in manufacturing industry, automatic generation of inspection planning of product and the integration with other activities in product lifecycle play important roles in quality control. But the techniques for these purposes are laggard comparing with techniques of CAD/CAM. Therefore, an automatic inspection planning system for Coordinate Measuring Machine (CMM) was developed to improve the automatization of measuring based on the integration of inspection system in PLM. Feature information representation is achieved based on a PLM canter database; measuring strategy is optimized through the integration of multi-sensors; reasonable number and distribution of inspection points are calculated and designed with the guidance of statistic theory and a synthesis distribution algorithm; a collision avoidance method is proposed to generate non-collision inspection path with high efficiency. Information mapping is performed between Neutral Interchange Files (NIFs), such as STEP, DML, DMIS, XML, etc., to realize information integration with other activities in the product lifecycle like design, manufacturing and inspection execution, etc. Simulation was carried out to demonstrate the feasibility of the proposed system. As a result, the inspection process is becoming simpler and good result can be got based on the integration in PLM.

Route planning in a four-dimensional environment

NASA Technical Reports Server (NTRS)

Slack, M. G.; Miller, D. P.

1987-01-01

Robots must be able to function in the real world. The real world involves processes and agents that move independently of the actions of the robot, sometimes in an unpredictable manner. A real-time integrated route planning and spatial representation system for planning routes through dynamic domains is presented. The system will find the safest most efficient route through space-time as described by a set of user defined evaluation functions. Because the route planning algorthims is highly parallel and can run on an SIMD machine in O(p) time (p is the length of a path), the system will find real-time paths through unpredictable domains when used in an incremental mode. Spatial representation, an SIMD algorithm for route planning in a dynamic domain, and results from an implementation on a traditional computer architecture are discussed.

Optimizing the Compressive Strength of Strain-Hardenable Stretch-Formed Microtruss Architectures

NASA Astrophysics Data System (ADS)

Yu, Bosco; Abu Samk, Khaled; Hibbard, Glenn D.

2015-05-01

The mechanical performance of stretch-formed microtrusses is determined by both the internal strut architecture and the accumulated plastic strain during fabrication. The current study addresses the question of optimization, by taking into consideration the interdependency between fabrication path, material properties and architecture. Low carbon steel (AISI1006) and aluminum (AA3003) material systems were investigated experimentally, with good agreement between measured values and the analytical model. The compressive performance of the microtrusses was then optimized on a minimum weight basis under design constraints such as fixed starting sheet thickness and final microtruss height by satisfying the Karush-Kuhn-Tucker condition. The optimization results were summarized as carpet plots in order to meaningfully visualize the interdependency between architecture, microstructural state, and mechanical performance, enabling material and processing path selection.

Optimal short-range trajectories for helicopters

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

Slater, G.L.; Erzberger, H.

1982-12-01

An optimal flight path algorithm using a simplified altitude state model and a priori climb cruise descent flight profile was developed and applied to determine minimum fuel and minimum cost trajectories for a helicopter flying a fixed range trajectory. In addition, a method was developed for obtaining a performance model in simplified form which is based on standard flight manual data and which is applicable to the computation of optimal trajectories. The entire performance optimization algorithm is simple enough that on line trajectory optimization is feasible with a relatively small computer. The helicopter model used is the Silorsky S-61N. Themore » results show that for this vehicle the optimal flight path and optimal cruise altitude can represent a 10% fuel saving on a minimum fuel trajectory. The optimal trajectories show considerable variability because of helicopter weight, ambient winds, and the relative cost trade off between time and fuel. In general, reasonable variations from the optimal velocities and cruise altitudes do not significantly degrade the optimal cost. For fuel optimal trajectories, the optimum cruise altitude varies from the maximum (12,000 ft) to the minimum (0 ft) depending on helicopter weight.« less

An industrial robot singular trajectories planning based on graphs and neural networks

NASA Astrophysics Data System (ADS)

Łęgowski, Adrian; Niezabitowski, Michał

2016-06-01

Singular trajectories are rarely used because of issues during realization. A method of planning trajectories for given set of points in task space with use of graphs and neural networks is presented. In every desired point the inverse kinematics problem is solved in order to derive all possible solutions. A graph of solutions is made. The shortest path is determined to define required nodes in joint space. Neural networks are used to define the path between these nodes.

Planning Minimum-Energy Paths in an Off-Road Environment with Anisotropic Traversal Costs and Motion Constraints

DTIC Science & Technology

1989-06-01

problems, and (3) weighted-region problems. Since the minimum-energy path-planning problem addressed in this dissertation is a hybrid between the two...contains components that are strictly vehicle dependent, components that are strictly terrain dependent, and components representing a hybrid of...Single Segment Braking/Multiple Segment Hybrid Using Eq. (3.46), the traversal cost U 1,.-1 can be rewritten as Uop- 1 = mgD Itan01 , (4.12a) and the

Vervet monkey (Chlorocebus pygerythrus) behavior in a multi-destination route: Evidence for planning ahead when heuristics fail.

PubMed

Teichroeb, Julie Annette; Smeltzer, Eve Ann

2018-01-01

Animal paths are analogous to intractable mathematical problems like the Traveling Salesman Problem (TSP) and the shortest path problem (SPP). Both the TSP and SPP require an individual to find the shortest path through multiple targets but the TSP demands a return to the start, while the SPP does not. Vervet monkeys are very efficient in solving TSPs but this species is a multiple central place forager that does not always return to the same sleeping site and thus theoretically should be selected to find solutions to SPPs rather than TSPs. We examined path choice by wild vervets in an SPP experimental array where the shortest paths usually differed from those consistent with common heuristic strategies, the nearest-neighbor rule (NNR-go to the closest resource that has not been visited), and the convex hull (put a mental loop around sites, adding inner targets in order of distance from the edge)-an efficient strategy for TSPs but not SPPs. In addition, humans solving SPPs use an initial segment strategy (ISS-choose the straightest path at the beginning, only turning when necessary) and we looked at vervet paths consistent with this strategy. In 615 trials by single foragers, paths usually conformed to the NNR and rarely the slightly more efficient convex hull, supporting that vervets may be selected to solve SPPs. Further, like humans solving SPPs, vervets showed a tendency to use the ISS. Paths consistent with heuristics dropped off sharply, and use of the shortest path increased, when heuristics led to longer paths showing trade-offs in efficiency versus cognitive load. Two individuals out of 17, found the shortest path most often, showing inter-individual variation in path planning. Given support for the NNR and the ISS, we propose a new rule-of-thumb termed the "region heuristic" that vervets may apply in multi-destination routes.

Vervet monkey (Chlorocebus pygerythrus) behavior in a multi-destination route: Evidence for planning ahead when heuristics fail

PubMed Central

Smeltzer, Eve Ann

2018-01-01

Animal paths are analogous to intractable mathematical problems like the Traveling Salesman Problem (TSP) and the shortest path problem (SPP). Both the TSP and SPP require an individual to find the shortest path through multiple targets but the TSP demands a return to the start, while the SPP does not. Vervet monkeys are very efficient in solving TSPs but this species is a multiple central place forager that does not always return to the same sleeping site and thus theoretically should be selected to find solutions to SPPs rather than TSPs. We examined path choice by wild vervets in an SPP experimental array where the shortest paths usually differed from those consistent with common heuristic strategies, the nearest-neighbor rule (NNR–go to the closest resource that has not been visited), and the convex hull (put a mental loop around sites, adding inner targets in order of distance from the edge)–an efficient strategy for TSPs but not SPPs. In addition, humans solving SPPs use an initial segment strategy (ISS–choose the straightest path at the beginning, only turning when necessary) and we looked at vervet paths consistent with this strategy. In 615 trials by single foragers, paths usually conformed to the NNR and rarely the slightly more efficient convex hull, supporting that vervets may be selected to solve SPPs. Further, like humans solving SPPs, vervets showed a tendency to use the ISS. Paths consistent with heuristics dropped off sharply, and use of the shortest path increased, when heuristics led to longer paths showing trade-offs in efficiency versus cognitive load. Two individuals out of 17, found the shortest path most often, showing inter-individual variation in path planning. Given support for the NNR and the ISS, we propose a new rule-of-thumb termed the “region heuristic” that vervets may apply in multi-destination routes. PMID:29813105

Upconverting nanoparticles for optimizing scintillator based detection systems

DOEpatents

Kross, Brian; McKisson, John E; McKisson, John; Weisenberger, Andrew; Xi, Wenze; Zom, Carl

2013-09-17

An upconverting device for a scintillation detection system is provided. The detection system comprises a scintillator material, a sensor, a light transmission path between the scintillator material and the sensor, and a plurality of upconverting nanoparticles particles positioned in the light transmission path.

Do Workplace Literacy Programs Promote High Skills or Low Wages? Suggestions for Future Evaluations of Workplace Literacy Programs.

ERIC Educational Resources Information Center

Sarmiento, Tony

Workplace literacy programs can support the path toward either low wages or high skills. Instead of the "high skill" path, most U.S. companies follow the "low wage" path. Depending on who is involved, which program goals are selected, and what planning process is followed, a workplace literacy program can maintain outdated workplaces or foster…

Patient-specific dosimetric endpoints based treatment plan quality control in radiotherapy.

PubMed

Song, Ting; Staub, David; Chen, Mingli; Lu, Weiguo; Tian, Zhen; Jia, Xun; Li, Yongbao; Zhou, Linghong; Jiang, Steve B; Gu, Xuejun

2015-11-07

In intensity modulated radiotherapy (IMRT), the optimal plan for each patient is specific due to unique patient anatomy. To achieve such a plan, patient-specific dosimetric goals reflecting each patient's unique anatomy should be defined and adopted in the treatment planning procedure for plan quality control. This study is to develop such a personalized treatment plan quality control tool by predicting patient-specific dosimetric endpoints (DEs). The incorporation of patient specific DEs is realized by a multi-OAR geometry-dosimetry model, capable of predicting optimal DEs based on the individual patient's geometry. The overall quality of a treatment plan is then judged with a numerical treatment plan quality indicator and characterized as optimal or suboptimal. Taking advantage of clinically available prostate volumetric modulated arc therapy (VMAT) treatment plans, we built and evaluated our proposed plan quality control tool. Using our developed tool, six of twenty evaluated plans were identified as sub-optimal plans. After plan re-optimization, these suboptimal plans achieved better OAR dose sparing without sacrificing the PTV coverage, and the dosimetric endpoints of the re-optimized plans agreed well with the model predicted values, which validate the predictability of the proposed tool. In conclusion, the developed tool is able to accurately predict optimally achievable DEs of multiple OARs, identify suboptimal plans, and guide plan optimization. It is a useful tool for achieving patient-specific treatment plan quality control.

Study on the measuring distance for blood glucose infrared spectral measuring by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Li, Xiang

2016-10-01

Blood glucose monitoring is of great importance for controlling diabetes procedure and preventing the complications. At present, the clinical blood glucose concentration measurement is invasive and could be replaced by noninvasive spectroscopy analytical techniques. Among various parameters of optical fiber probe used in spectrum measuring, the measurement distance is the key one. The Monte Carlo technique is a flexible method for simulating light propagation in tissue. The simulation is based on the random walks that photons make as they travel through tissue, which are chosen by statistically sampling the probability distributions for step size and angular deflection per scattering event. The traditional method for determine the optimal distance between transmitting fiber and detector is using Monte Carlo simulation to find out the point where most photons come out. But there is a problem. In the epidermal layer there is no artery, vein or capillary vessel. Thus, when photons propagate and interactive with tissue in epidermal layer, no information is given to the photons. A new criterion is proposed to determine the optimal distance, which is named effective path length in this paper. The path length of each photons travelling in dermis is recorded when running Monte-Carlo simulation, which is the effective path length defined above. The sum of effective path length of every photon at each point is calculated. The detector should be place on the point which has most effective path length. Then the optimal measuring distance between transmitting fiber and detector is determined.

Shortest multiple disconnected path for the analysis of entanglements in two- and three-dimensional polymeric systems

NASA Astrophysics Data System (ADS)

Kröger, Martin

2005-06-01

We present an algorithm which returns a shortest path and related number of entanglements for a given configuration of a polymeric system in 2 or 3 dimensions. Rubinstein and Helfand, and later Everaers et al. introduced a concept to extract primitive paths for dense polymeric melts made of linear chains (a multiple disconnected multibead 'path'), where each primitive path is defined as a path connecting the (space-fixed) ends of a polymer under the constraint of non-interpenetration (excluded volume) between primitive paths of different chains, such that the multiple disconnected path fulfills a minimization criterion. The present algorithm uses geometrical operations and provides a—model independent—efficient approximate solution to this challenging problem. Primitive paths are treated as 'infinitely' thin (we further allow for finite thickness to model excluded volume), and tensionless lines rather than multibead chains, excluded volume is taken into account without a force law. The present implementation allows to construct a shortest multiple disconnected path (SP) for 2D systems (polymeric chain within spherical obstacles) and an optimal SP for 3D systems (collection of polymeric chains). The number of entanglements is then simply obtained from the SP as either the number of interior kinks, or from the average length of a line segment. Further, information about structure and potentially also the dynamics of entanglements is immediately available from the SP. We apply the method to study the 'concentration' dependence of the degree of entanglement in phantom chain systems. Program summaryTitle of program:Z Catalogue number:ADVG Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADVG Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: Silicon Graphics (Irix), Sun (Solaris), PC (Linux) Operating systems or monitors under which the program has been tested: UNIX, Linux Program language used: USANSI Fortran 77 and Fortran 90 Memory required to execute with typical data: 1 MByte No. of lines in distributed program, including test data, etc.: 10 660 No. of bytes in distributed program, including test data, etc.: 119 551 Distribution formet:tar.gz Nature of physical problem: The problem is to obtain primitive paths substantiating a shortest multiple disconnected path (SP) for a given polymer configuration (chains of particles, with or without additional single particles as obstacles for the 2D case). Primitive paths are here defined as in [M. Rubinstein, E. Helfand, J. Chem. Phys. 82 (1985) 2477; R. Everaers, S.K. Sukumaran, G.S. Grest, C. Svaneborg, A. Sivasubramanian, K. Kremer, Science 303 (2004) 823] as the shortest line (path) respecting 'topological' constraints (from neighboring polymers or point obstacles) between ends of polymers. There is a unique solution for the 2D case. For the 3D case it is unique if we construct a primitive path of a single chain embedded within fixed line obstacles [J.S.B. Mitchell, Geometric shortest paths and network optimization, in: J.-R. Sack, J. Urrutia (Eds.), Handbook of Computational Geometry, Elsevier, Amsterdam, 2000, pp. 633-701]. For a large 3D configuration made of several chains, short is meant to be the Euclidean shortest multiple disconnected path (SP) where primitive paths are constructed for all chains simultaneously. While the latter problem, in general, does not possess a unique solution, the algorithm must return a locally optimal solution, robust against minor displacements of the disconnected path and chain re-labeling. The problem is solved if the number of kinks (or entanglements Z), explicitly deduced from the SP, is quite insensitive to the exact conformation of the SP which allows to estimate Z with a small error. Efficient method of solution: Primitive paths are constructed from the given polymer configuration (a non-shortest multiple disconnected path, including obstacles, if present) by first replacing each polymer contour by a line with a number of 'kinks' (beads, nodes) and 'segments' (edges). To obtain primitive paths, defined to be uncrossable by any other objects (neighboring primitive paths, line or point obstacles), the algorithm minimizes the length of all primitive paths consecutively, until a final minimum Euclidean length of the SP is reached. Fast geometric operations rather than dynamical methods are used to minimize the contour lengths of the primitive paths. Neighbor lists are used to keep track of potentially intersecting segments of other chains. Periodic boundary conditions are employed. A finite small line thickness is used in order to make sure that entanglements are not 'lost' due to finite precision of representation of numbers. Restrictions on the complexity of the problem: For a single chain embedded within fixed line or point obstacles, the algorithm returns the exact SP. For more complex problems, the algorithm returns a locally optimal SP. Except for exotic, probably rare, configurations it turns out that different locally optimal SPs possess quite an identical number of nodes. In general, the problem constructing the SP is known to be NP-hard [J.S.B. Mitchell, Geometric shortest paths and network optimization, in: J.-R. Sack, J. Urrutia (Eds.), Handbook of Computational Geometry, Elsevier, Amsterdam, 2000, pp. 633-701], and we offer a solution which should suffice to analyze physical problems, and gives an estimate about the precision and uniqueness of the result (from a standard deviation by varying the parameter: cyclicswitch). The program is NOT restricted to handle systems for which segment lengths of the SP exceed half the box size. Typical running time: Typical running times are approximately two orders of magnitude shorter compared with the ones needed for a corresponding molecular dynamics approach, and scale mostly linearly with system size. We provide a benchmark table.

Optimization of Supersonic Transport Trajectories

NASA Technical Reports Server (NTRS)

Ardema, Mark D.; Windhorst, Robert; Phillips, James

1998-01-01

This paper develops a near-optimal guidance law for generating minimum fuel, time, or cost fixed-range trajectories for supersonic transport aircraft. The approach uses a choice of new state variables along with singular perturbation techniques to time-scale decouple the dynamic equations into multiple equations of single order (second order for the fast dynamics). Application of the maximum principle to each of the decoupled equations, as opposed to application to the original coupled equations, avoids the two point boundary value problem and transforms the problem from one of a functional optimization to one of multiple function optimizations. It is shown that such an approach produces well known aircraft performance results such as minimizing the Brequet factor for minimum fuel consumption and the energy climb path. Furthermore, the new state variables produce a consistent calculation of flight path angle along the trajectory, eliminating one of the deficiencies in the traditional energy state approximation. In addition, jumps in the energy climb path are smoothed out by integration of the original dynamic equations at constant load factor. Numerical results performed for a supersonic transport design show that a pushover dive followed by a pullout at nominal load factors are sufficient maneuvers to smooth the jump.

Social Milieu Oriented Routing: A New Dimension to Enhance Network Security in WSNs.

PubMed

Liu, Lianggui; Chen, Li; Jia, Huiling

2016-02-19

In large-scale wireless sensor networks (WSNs), in order to enhance network security, it is crucial for a trustor node to perform social milieu oriented routing to a target a trustee node to carry out trust evaluation. This challenging social milieu oriented routing with more than one end-to-end Quality of Trust (QoT) constraint has proved to be NP-complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this challenging problem. However, existing solutions cannot guarantee the efficiency of searching; that is, they can hardly avoid obtaining partial optimal solutions during a searching process. Quantum annealing (QA) uses delocalization and tunneling to avoid falling into local minima without sacrificing execution time. This has been proven a promising way to many optimization problems in recently published literatures. In this paper, for the first time, with the help of a novel approach, that is, configuration path-integral Monte Carlo (CPIMC) simulations, a QA-based optimal social trust path (QA_OSTP) selection algorithm is applied to the extraction of the optimal social trust path in large-scale WSNs. Extensive experiments have been conducted, and the experiment results demonstrate that QA_OSTP outperforms its heuristic opponents.

Optimal symmetric flight with an intermediate vehicle model

NASA Technical Reports Server (NTRS)

Menon, P. K. A.; Kelley, H. J.; Cliff, E. M.

1983-01-01

Optimal flight in the vertical plane with a vehicle model intermediate in complexity between the point-mass and energy models is studied. Flight-path angle takes on the role of a control variable. Range-open problems feature subarcs of vertical flight and singular subarcs. The class of altitude-speed-range-time optimization problems with fuel expenditure unspecified is investigated and some interesting phenomena uncovered. The maximum-lift-to-drag glide appears as part of the family, final-time-open, with appropriate initial and terminal transient exceeding level-flight drag, some members exhibiting oscillations. Oscillatory paths generally fail the Jacobi test for durations exceeding a period and furnish a minimum only for short-duration problems.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

PubMed

Bennett, Casey C; Hauser, Kris

2013-01-01

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. Copyright © 2012 Elsevier B.V. All rights reserved.

Solving fuzzy shortest path problem by genetic algorithm

NASA Astrophysics Data System (ADS)

Syarif, A.; Muludi, K.; Adrian, R.; Gen, M.

2018-03-01

Shortest Path Problem (SPP) is known as one of well-studied fields in the area Operations Research and Mathematical Optimization. It has been applied for many engineering and management designs. The objective is usually to determine path(s) in the network with minimum total cost or traveling time. In the past, the cost value for each arc was usually assigned or estimated as a deteministic value. For some specific real world applications, however, it is often difficult to determine the cost value properly. One way of handling such uncertainty in decision making is by introducing fuzzy approach. With this situation, it will become difficult to solve the problem optimally. This paper presents the investigations on the application of Genetic Algorithm (GA) to a new SPP model in which the cost values are represented as Triangular Fuzzy Number (TFN). We adopts the concept of ranking fuzzy numbers to determine how good the solutions. Here, by giving his/her degree value, the decision maker can determine the range of objective value. This would be very valuable for decision support system in the real world applications.Simulation experiments were carried out by modifying several test problems with 10-25 nodes. It is noted that the proposed approach is capable attaining a good solution with different degree of optimism for the tested problems.

Interpolating between random walks and optimal transportation routes: Flow with multiple sources and targets

NASA Astrophysics Data System (ADS)

Guex, Guillaume

2016-05-01

In recent articles about graphs, different models proposed a formalism to find a type of path between two nodes, the source and the target, at crossroads between the shortest-path and the random-walk path. These models include a freely adjustable parameter, allowing to tune the behavior of the path toward randomized movements or direct routes. This article presents a natural generalization of these models, namely a model with multiple sources and targets. In this context, source nodes can be viewed as locations with a supply of a certain good (e.g. people, money, information) and target nodes as locations with a demand of the same good. An algorithm is constructed to display the flow of goods in the network between sources and targets. With again a freely adjustable parameter, this flow can be tuned to follow routes of minimum cost, thus displaying the flow in the context of the optimal transportation problem or, by contrast, a random flow, known to be similar to the electrical current flow if the random-walk is reversible. Moreover, a source-targetcoupling can be retrieved from this flow, offering an optimal assignment to the transportation problem. This algorithm is described in the first part of this article and then illustrated with case studies.

Confidence level estimation in multi-target classification problems

NASA Astrophysics Data System (ADS)

Chang, Shi; Isaacs, Jason; Fu, Bo; Shin, Jaejeong; Zhu, Pingping; Ferrari, Silvia

2018-04-01

This paper presents an approach for estimating the confidence level in automatic multi-target classification performed by an imaging sensor on an unmanned vehicle. An automatic target recognition algorithm comprised of a deep convolutional neural network in series with a support vector machine classifier detects and classifies targets based on the image matrix. The joint posterior probability mass function of target class, features, and classification estimates is learned from labeled data, and recursively updated as additional images become available. Based on the learned joint probability mass function, the approach presented in this paper predicts the expected confidence level of future target classifications, prior to obtaining new images. The proposed approach is tested with a set of simulated sonar image data. The numerical results show that the estimated confidence level provides a close approximation to the actual confidence level value determined a posteriori, i.e. after the new image is obtained by the on-board sensor. Therefore, the expected confidence level function presented in this paper can be used to adaptively plan the path of the unmanned vehicle so as to optimize the expected confidence levels and ensure that all targets are classified with satisfactory confidence after the path is executed.

Incorporating deliverable monitor unit constraints into spot intensity optimization in intensity modulated proton therapy treatment planning

PubMed Central

Cao, Wenhua; Lim, Gino; Li, Xiaoqiang; Li, Yupeng; Zhu, X. Ronald; Zhang, Xiaodong

2014-01-01

The purpose of this study is to investigate the feasibility and impact of incorporating deliverable monitor unit (MU) constraints into spot intensity optimization in intensity modulated proton therapy (IMPT) treatment planning. The current treatment planning system (TPS) for IMPT disregards deliverable MU constraints in the spot intensity optimization (SIO) routine. It performs a post-processing procedure on an optimized plan to enforce deliverable MU values that are required by the spot scanning proton delivery system. This procedure can create a significant dose distribution deviation between the optimized and post-processed deliverable plans, especially when small spot spacings are used. In this study, we introduce a two-stage linear programming (LP) approach to optimize spot intensities and constrain deliverable MU values simultaneously, i.e., a deliverable spot intensity optimization (DSIO) model. Thus, the post-processing procedure is eliminated and the associated optimized plan deterioration can be avoided. Four prostate cancer cases at our institution were selected for study and two parallel opposed beam angles were planned for all cases. A quadratic programming (QP) based model without MU constraints, i.e., a conventional spot intensity optimization (CSIO) model, was also implemented to emulate the commercial TPS. Plans optimized by both the DSIO and CSIO models were evaluated for five different settings of spot spacing from 3 mm to 7 mm. For all spot spacings, the DSIO-optimized plans yielded better uniformity for the target dose coverage and critical structure sparing than did the CSIO-optimized plans. With reduced spot spacings, more significant improvements in target dose uniformity and critical structure sparing were observed in the DSIO- than in the CSIO-optimized plans. Additionally, better sparing of the rectum and bladder was achieved when reduced spacings were used for the DSIO-optimized plans. The proposed DSIO approach ensures the deliverability of optimized IMPT plans that take into account MU constraints. This eliminates the post-processing procedure required by the TPS as well as the resultant deteriorating effect on ultimate dose distributions. This approach therefore allows IMPT plans to adopt all possible spot spacings optimally. Moreover, dosimetric benefits can be achieved using smaller spot spacings. PMID:23835656

Homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints.

PubMed

López-Nicolás, Gonzalo; Gans, Nicholas R; Bhattacharya, Sourabh; Sagüés, Carlos; Guerrero, Josechu J; Hutchinson, Seth

2010-08-01

In this paper, we present a visual servo controller that effects optimal paths for a nonholonomic differential drive robot with field-of-view constraints imposed by the vision system. The control scheme relies on the computation of homographies between current and goal images, but unlike previous homography-based methods, it does not use the homography to compute estimates of pose parameters. Instead, the control laws are directly expressed in terms of individual entries in the homography matrix. In particular, we develop individual control laws for the three path classes that define the language of optimal paths: rotations, straight-line segments, and logarithmic spirals. These control laws, as well as the switching conditions that define how to sequence path segments, are defined in terms of the entries of homography matrices. The selection of the corresponding control law requires the homography decomposition before starting the navigation. We provide a controllability and stability analysis for our system and give experimental results.

Effective use of congestion in complex networks

NASA Astrophysics Data System (ADS)

Echagüe, Juan; Cholvi, Vicent; Kowalski, Dariusz R.

2018-03-01

In this paper, we introduce a congestion-aware routing protocol that selects the paths according to the congestion of nodes in the network. The aim is twofold: on one hand, and in order to prevent the networks from collapsing, it provides a good tolerance to nodes' overloads; on the other hand, and in order to guarantee efficient communication, it also incentivize the routes to follow short paths. We analyze the performance of our proposed routing strategy by means of a series of experiments carried out by using simulations. We show that it provides a tolerance to collapse close to the optimal value. Furthermore, the average length of the paths behaves optimally up to the certain value of packet generation rate ρ and it grows in a linear fashion with the increase of ρ.

An optimization model for metabolic pathways.

PubMed

Planes, F J; Beasley, J E

2009-10-15

Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.

Tool path strategy and cutting process monitoring in intelligent machining

NASA Astrophysics Data System (ADS)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

Simulation in production of open rotor propellers: from optimal surface geometry to automated control of mechanical treatment

NASA Astrophysics Data System (ADS)

Grinyok, A.; Boychuk, I.; Perelygin, D.; Dantsevich, I.

2018-03-01

A complex method of the simulation and production design of open rotor propellers was studied. An end-to-end diagram was proposed for the evaluating, designing and experimental testing the optimal geometry of the propeller surface, for the machine control path generation as well as for simulating the cutting zone force condition and its relationship with the treatment accuracy which was defined by the propeller elastic deformation. The simulation data provided the realization of the combined automated path control of the cutting tool.

Pedestrian flow-path modeling to support tsunami-evacuation planning

NASA Astrophysics Data System (ADS)

Wood, N. J.; Jones, J. M.; Schmidtlein, M.

2015-12-01

Near-field tsunami hazards are credible threats to many coastal communities throughout the world. Along the U.S. Pacific Northwest coast, low-lying areas could be inundated by a series of catastrophic tsunamis potentially arriving in a matter of minutes following a Cascadia subduction zone (CSZ) earthquake. We developed a geospatial-modeling method for characterizing pedestrian-evacuation flow paths and evacuation basins to support evacuation and relief planning efforts for coastal communities in this region. We demonstrate this approach using the coastal communities of Aberdeen, Hoquiam, and Cosmopolis in southwestern Grays Harbor County, Washington (USA), where previous research suggests approximately 20,500 people (99% of the residents in tsunami-hazard zones) will likely have enough time to evacuate before tsunami-wave arrival. Geospatial, anisotropic, path distance models were developed to map the most efficient pedestrian paths to higher ground from locations within the tsunami-hazard zone. This information was then used to identify evacuation basins, outlining neighborhoods sharing a common evacuation pathway to safety. We then estimated the number of people traveling along designated evacuation pathways and arriving at pre-determined safe assembly areas, helping determine shelter demand and relief support (e.g., for elderly individuals or tourists). Finally, we assessed which paths may become inaccessible due to earthquake-induced ground failures, a factor which may impact an individual's success in reaching safe ground. The presentation will include a discussion of the implications of our analysis for developing more comprehensive coastal community tsunami-evacuation planning strategies worldwide.

On-Line Path Generation and Tracking for High-Speed Wheeled Autonomous Vehicles

DTIC Science & Technology

2006-02-17

On-Line Path Generation and Tracking for High-Speed Wheeled Autonomous Vehicles Report Title ABSTRACT In this work we proposed two semi-analytic...298-102 Enclosure 1 On-Line Path Generation and Tracking for High-Speed Wheeled Autonomous Vehicles by...Specifically, the following problems will be addressed during this project: 2.1 Challenges The problem of trajectory planning for high-speed autonomous vehicles is

Navigating the Path to a Biomedical Science Career

NASA Astrophysics Data System (ADS)

Zimmerman, Andrea McNeely

The number of biomedical PhD scientists being trained and graduated far exceeds the number of academic faculty positions and academic research jobs. If this trend is compelling biomedical PhD scientists to increasingly seek career paths outside of academia, then more should be known about their intentions, desires, training experiences, and career path navigation. Therefore, the purpose of this study was to understand the process through which biomedical PhD scientists are trained and supported for navigating future career paths. In addition, the study sought to determine whether career development support efforts and opportunities should be redesigned to account for the proportion of PhD scientists following non-academic career pathways. Guided by the social cognitive career theory (SCCT) framework this study sought to answer the following central research question: How does a southeastern tier 1 research university train and support its biomedical PhD scientists for navigating their career paths? Key findings are: Many factors influence PhD scientists' career sector preference and job search process, but the most influential were relationships with faculty, particularly the mentor advisor; Planned activities are a significant aspect of the training process and provide skills for career success; and Planned activities provided skills necessary for a career, but influential factors directed the career path navigated. Implications for practice and future research are discussed.

Field calculations, single-particle tracking, and beam dynamics with space charge in the electron lens for the Fermilab Integrable Optics Test Accelerator

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

Noll, Daniel; Stancari, Giulio

2015-11-17

An electron lens is planned for the Fermilab Integrable Optics Test Accelerator as a nonlinear element for integrable dynamics, as an electron cooler, and as an electron trap to study space-charge compensation in rings. We present the main design principles and constraints for nonlinear integrable optics. A magnetic configuration of the solenoids and of the toroidal section is laid out. Singleparticle tracking is used to optimize the electron path. Electron beam dynamics at high intensity is calculated with a particle-in-cell code to estimate current limits, profile distortions, and the effects on the circulating beam. In the conclusions, we summarize themore » main findings and list directions for further work.« less

Software for Project-Based Learning of Robot Motion Planning

ERIC Educational Resources Information Center

Moll, Mark; Bordeaux, Janice; Kavraki, Lydia E.

2013-01-01

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can…

ELASTIC NET FOR COX'S PROPORTIONAL HAZARDS MODEL WITH A SOLUTION PATH ALGORITHM.

PubMed

Wu, Yichao

2012-01-01

For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox's proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox's proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems.

Integrating obstacle avoidance, global path planning, visual cue detection, and landmark triangulation in a mobile robot

NASA Astrophysics Data System (ADS)

Kortenkamp, David; Huber, Marcus J.; Congdon, Clare B.; Huffman, Scott B.; Bidlack, Clint R.; Cohen, Charles J.; Koss, Frank V.; Raschke, Ulrich; Weymouth, Terry E.

1993-05-01

This paper describes the design and implementation of an integrated system for combining obstacle avoidance, path planning, landmark detection and position triangulation. Such an integrated system allows the robot to move from place to place in an environment, avoiding obstacles and planning its way out of traps, while maintaining its position and orientation using distinctive landmarks. The task the robot performs is to search a 22 m X 22 m arena for 10 distinctive objects, visiting each object in turn. This same task was recently performed by a dozen different robots at a competition in which the robot described in this paper finished first.

Design and Evaluation of a Dynamic Programming Flight Routing Algorithm Using the Convective Weather Avoidance Model

NASA Technical Reports Server (NTRS)

Ng, Hok K.; Grabbe, Shon; Mukherjee, Avijit

2010-01-01

The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.

Financial Planning for Retirement: An Imperative for Baby Boomer Women.

ERIC Educational Resources Information Center

Glass, J. Conrad, Jr.; Kilpatrick, Beverly B.

1998-01-01

Many women fail to plan for retirement due to economic constraints, interrupted career paths, lower earnings, gender bias, gender-role socialization, self-esteem, role definition, locus of control, or risk tolerance. Retirement education must address women's specific issues regarding financial planning. (SK)

Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain

PubMed Central

Fernando, Chrisantha; Vasas, Vera; Szathmáry, Eörs; Husbands, Phil

2011-01-01

We propose a previously unrecognized kind of informational entity in the brain that is capable of acting as the basis for unlimited hereditary variation in neuronal networks. This unit is a path of activity through a network of neurons, analogous to a path taken through a hidden Markov model. To prove in principle the capabilities of this new kind of informational substrate, we show how a population of paths can be used as the hereditary material for a neuronally implemented genetic algorithm, (the swiss-army knife of black-box optimization techniques) which we have proposed elsewhere could operate at somatic timescales in the brain. We compare this to the same genetic algorithm that uses a standard ‘genetic’ informational substrate, i.e. non-overlapping discrete genotypes, on a range of optimization problems. A path evolution algorithm (PEA) is defined as any algorithm that implements natural selection of paths in a network substrate. A PEA is a previously unrecognized type of natural selection that is well suited for implementation by biological neuronal networks with structural plasticity. The important similarities and differences between a standard genetic algorithm and a PEA are considered. Whilst most experiments are conducted on an abstract network model, at the conclusion of the paper a slightly more realistic neuronal implementation of a PEA is outlined based on Izhikevich spiking neurons. Finally, experimental predictions are made for the identification of such informational paths in the brain. PMID:21887266

Sensory feedback in a bump attractor model of path integration.

PubMed

Poll, Daniel B; Nguyen, Khanh; Kilpatrick, Zachary P

2016-04-01

Mammalian spatial navigation systems utilize several different sensory information channels. This information is converted into a neural code that represents the animal's current position in space by engaging place cell, grid cell, and head direction cell networks. In particular, sensory landmark (allothetic) cues can be utilized in concert with an animal's knowledge of its own velocity (idiothetic) cues to generate a more accurate representation of position than path integration provides on its own (Battaglia et al. The Journal of Neuroscience 24(19):4541-4550 (2004)). We develop a computational model that merges path integration with feedback from external sensory cues that provide a reliable representation of spatial position along an annular track. Starting with a continuous bump attractor model, we explore the impact of synaptic spatial asymmetry and heterogeneity, which disrupt the position code of the path integration process. We use asymptotic analysis to reduce the bump attractor model to a single scalar equation whose potential represents the impact of asymmetry and heterogeneity. Such imperfections cause errors to build up when the network performs path integration, but these errors can be corrected by an external control signal representing the effects of sensory cues. We demonstrate that there is an optimal strength and decay rate of the control signal when cues appear either periodically or randomly. A similar analysis is performed when errors in path integration arise from dynamic noise fluctuations. Again, there is an optimal strength and decay of discrete control that minimizes the path integration error.