基于改进势场法的移动机器人路径规划

doi:10.3969/j.issn.1000-1093.2020.10.021

摘要/Abstract

摘要： 针对传统势场法存在的路径不被识别、局部极小陷阱、振荡问题，提出适用于复杂障碍环境情况下机器人路径规划、结合多行为策略与可变影响范围的势场法。通过对障碍物影响范围做可变处理，消除问题共同必要条件，提前规避路径不识别、多障碍区导致的振荡、多障碍区导致的局部极小陷阱3个问题；采取新的步进与振荡分类方式，设计多行为行动策略，并给出各行为的准确起止条件，通过预判问题的共同表现形式以及起止条件的衔接进行行为切换，提前规避单障碍区导致的局部极小陷阱和单障碍区导致的振荡2个问题；基于数学仿真软件MATLAB平台的仿真结果验证所提方法在战场复杂障碍环境下的有效性与稳定性，与传统势场法、动态窗口法、A星算法、快速随机树算法的对比结果更突出了该方法的可行性与优越性。

关键词: 机器人路径规划, 势场法, 路径不识别, 局部极小陷阱, 路径振荡, 可变影响范围, 多行为策略

Abstract: In view of the problems existing in the traditional potential field algorithm (PFA), such as unrecognized path, local minimal trap and oscillation, a potential field algorithm combining multi-behavior strategy and obstacles variable affected range, and the path planning of robots suitable for the complex obstacle environment is proposed. The obstacles affected range can be changed to eliminate the common necessary conditions of the above problems, so as to avoid unrecognized path, oscillation caused by multiple obstacles and local minimal trap caused by multiple obstacles in advance. Based on the new classification method for step-by-step and oscillation, the multi-behavior strategy is designed with the exact starting and ending conditions. The behaviors are switched by predicting the common expression of problems and the connection of starting and ending conditions, thus avoiding the local minimum trap caused by single obstacle and the oscillation caused by single obstacle in advance. The simulated results based on MATLAB verify the effectiveness and stability of the proposed method in the complex battlefield obstacle environment, and the proposed method has the feasibility for path planning compared with potential field algorithm, dynamic window approach, A-star algorithm and rapid-exploration random tree algorithm.

Key words: robotpathplanning, potentialfieldalgorithm, unrecognizedpath, localminimum, oscillation, variableaffectedrange, multi-behaviorstrategy

中图分类号:

TP242.3

孙鹏耀，黄炎焱，潘尧. 基于改进势场法的移动机器人路径规划[J]. 兵工学报, 2020, 41(10): 2106-2121.

SUN Pengyao， HUANG Yanyan， PAN Yao. Path Planning of Mobile Robots Based on Improved Potential Field Algorithm[J]. Acta Armamentarii, 2020, 41(10): 2106-2121.

参考文献

［1］ GE S S, CUI Y J. New potential functions for mobile robot path planning［J］. IEEE Transactions on Robotics and Automation, 2000, 16(5): 615-620.
［2］陈天德, 黄炎焱, 沈炜. 基于虚拟障碍物法的无震荡航路规划［J］. 兵工学报, 2019, 40(3): 651-658.
CHEN T D, HUANG Y Y, SHEN W. Non-oscillation path planning based on virtual obstacle method［J］. Acta Armamentarii, 2019, 40(3): 651-658. (in Chinese)
［3］李东方, 王超, 邓宏彬, 等. 基于人工势场和RRT算法的机器蛇水下三维避障算法［J］. 兵工学报, 2017, 38(增刊1): 205-214.
LI D F, WANG C, DENG H B, et al. 3D intelligent ob-stacle avoidance algorithm based on artificial potential field method and rapidly-exploring random tree［J］. Acta Armamentarii, 2017, 38(S1): 205-214. (in Chinese)
［4］ QIAN Q W, WU J F, WANG Z. Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimiza-tion algorithm［J］. Personal and Ubiquitous Computing, 2019 ,23(3): 393-403.
［5］张献, 任耀峰, 王润芃. 基于自适应遗传算法的连续时空最优搜索路径规划研究［J］. 兵工学报, 2015, 36(12): 2386-2395.
ZHANG X, REN Y F, WANG R P. Research on optimal search path programming in continuous time and space based on an adaptive genetic algorithm［J］. Acta Armamentarii, 2015, 36(12): 2386-2395. (in Chinese)
［6］ CHEN Y J, XIANG S S, CHEN F X. Research on a task planning method for multi-ship cooperative driving［J］. Journal of Shanghai Jiaotong University(Science), 2019, 24(2): 99-108.
［7］薛裕颖, 张祥银, 张国梁, 等. 基于量子行为烟花算法的移动机器人路径规划及平滑［J］. 控制理论与应用, 2019, 36(9): 1398-1408.
XUE Y Y, ZHANG X Y, ZHANG G L, et al. Path planning and smoothing based on quantum-behaved fireworks algorithm for mobile robot［J］. Control Theory & Applications, 2019, 36(9): 1398-1408. (in Chinese)
［8］ MABROUK M H, MCLNNES C R. Solving the potential field local minimum problem using internal agent states［J］. Robotics and Autonomous Systems, 2008, 56(12): 1050-1060.
［9］ MABROUK M H, MCLNNES C R. An emergent wall following behaviour to escape local minima for swarms of agents［J］. IAENG International Journal of Computer Science, 2008, 35(4): 463-476.
［10］ RIMON E, KODITSCHEK D E. Exact robot navigation using artificial potential functions［J］. IEEE Transac-tions on Robotics and Automation, 1992, 8(5): 501-518.
［11］ CONNOLLY C I, GRUPEN R A. The applications of harmonic functions to robotics［J］. Journal of Robotic Systems, 1993, 10(7): 931-946.
［12］ VOLPE R, KHOSLA P. Manipulator control with superquadric artificial potential functions: theory and experiments［J］. IEEE Transactions on Systems, Man and Cybernetics, 1990, 20(6): 1423-1436.
［13］ BARRAQUAND J, LANGLOIS B, LATOMBE J C. Numerical potential field techniques for robot path planning［J］. IEEE Transactions on Systems, Man and Cybernetics, 1992, 22(2): 224-241.
［14］ CHANG H. A new technique to handle local minimum for imperfect potential field based motion planning［C］∥Proceedings of IEEE International Conference on Robotics and Automation. Minneapolis, MN, US: IEEE, 1996. 108-112.
［15］叶炜垚, 王春香, 杨明, 等. 基于虚拟障碍物的移动机器人路径规划方法［J］. 机器人, 2011, 33(3): 273-278, 286.
YE W Y, WANG C X, YANG M, et al. Virtual obstacles based path planning for mobile robots［J］. Robot, 2011, 33(3): 273-278, 286. (in Chinese)
［16］ YUN X, TAN K C. A wall-following method for escaping local minima in potential field based motion planning［C］∥Procee-
dings of International Conference on Advanced Robotics. Monterey, CA, US: IEEE, 1997: 421-426.
［17］翟红生, 王佳欣. 基于人工势场的机器人动态路径规划新方法［J］. 重庆邮电大学学报(自然科学版), 2015, 27(6): 814-818.
ZHAI H S, WANG J X. Dynamic path planning research for mobile robot based on artificial potential field［J］. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2015, 27(6):814-818. (in Chinese)
［18］黄辰, 费继友, 刘洋, 等. 基于动态反馈A^*蚁群算法的平滑路径规划方法［J］. 农业机械学报, 2017, 48(4): 34-40, 102.
HUANG C, FEI J Y, LIU Y, et al. Smooth path planning method based on dynamic feedback A^* ant colony algorithm［J］.
Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(4): 34-40, 102. (in Chinese)
［19］叶彬强, 王一. 基于人工势场法的机器人避障算法［J］. 重庆理工大学学报(自然科学版), 2012, 26(9): 82-85.
YE B Q, WANG Y, Research of obstacle avoidance algorithm for robot based on artificial potential field［J］. Journal of Chongqing University of Technology(Natural Science), 2012, 26(9): 82-85. (in Chinese)
［20］石鸿雁, 孙昌志. 一种基于混沌优化算法的机器人路径规划方法［J］. 机器人, 2005, 27(2): 152-157.
SHI H Y, SUN C Z. Path planning method for robot based on chaotic optimization algorithm［J］. Robot, 2005, 27(2): 152-157. (in Chinese)
［21］吴镜开, 黄远灿, 王世兴. 基于势场法的移动机器人避障路径规划［J］. 微计算机信息, 2007,23 (5): 228-230.
WU J K, HUANG Y C, WANG S X. Anti-collision path planning for mobile robot based on potential field method［J］. Microcomputer Information, 2007,23(5): 228-230. (in Chinese)
［22］ RECEVEUR J B, VICTOR S, MELCHIOR P. New interpretation of fractional potential fields for robust path planning［J］. Fractional Calculus and Applied Analysis, 2019, 22(1): 113-127.
［23］ WANG H, HUANG Y J, KHAJEPOUR A, et al. Crash mitigation in motion planning for autonomous vehicles［J］. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3313-3323.