An efficient dynamic system for real-time robot-path planning

被引:90
作者
Willms, Allan R. [1 ]
Yang, Simon X.
机构
[1] Univ Guelph, Dept Math & Stat, Guelph, ON N1G 2W1, Canada
[2] Univ Guelph, Sch Engn, Adv Robot & Intelligent Syst Grp, Guelph, ON N1G 2W1, Canada
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2006年 / 36卷 / 04期
基金
加拿大自然科学与工程研究理事会;
关键词
collision avoidance; dynamic environment; dynamic programming (DP); dynamic system; mobile robot; path planning; real-time navigation;
D O I
10.1109/TSMCB.2005.862724
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations.
引用
收藏
页码:755 / 766
页数:12
相关论文
共 36 条
[1]   ROBOT MOTION PLANNING - A DISTRIBUTED REPRESENTATION APPROACH [J].
BARRAQUAND, J ;
LATOMBE, JC .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 1991, 10 (06) :628-649
[2]   Self-organizing feature maps for modeling and control of robotic manipulators [J].
Barreto, Guilherme De A. ;
Araújo, Aluizio F. R. ;
Ritter, Helge J. .
Journal of Intelligent and Robotic Systems: Theory and Applications, 2003, 36 (04) :407-450
[3]   An analytically tractable potential field model of free space and its application in obstacle avoidance [J].
Chuang, JH ;
Ahuja, N .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1998, 28 (05) :729-736
[4]  
Denardo E.V, 1982, Dynamic Programming, Models and Applications, V1st
[5]  
Fujii T, 1998, IEEE INT CONF ROBOT, P2186, DOI 10.1109/ROBOT.1998.680648
[6]   An unsupervised neural network for low-level control of a wheeled mobile robot: Noise resistance, stability, and hardware implementation [J].
Gaudiano, P ;
Zalama, E ;
Coronado, JL .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (03) :485-496
[7]   NEURAL-NETWORK DYNAMICS FOR PATH PLANNING AND OBSTACLE AVOIDANCE [J].
GLASIUS, R ;
KOMODA, A ;
GIELEN, SCAM .
NEURAL NETWORKS, 1995, 8 (01) :125-133
[8]   POPULATION CODING IN A NEURAL-NET FOR TRAJECTORY FORMATION [J].
GLASIUS, R ;
KOMODA, A ;
GIELEN, S .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 1994, 5 (04) :549-563
[9]  
Glasius R, 1996, BIOL CYBERN, V74, P511, DOI 10.1007/BF00209422
[10]   NONLINEAR NEURAL NETWORKS - PRINCIPLES, MECHANISMS, AND ARCHITECTURES [J].
GROSSBERG, S .
NEURAL NETWORKS, 1988, 1 (01) :17-61