Path planning algorithm of mobile robot based on particle swarm optimization
HAN Ming1,2, LIU Jiaomin2, WU Shuomei1, WANG Jingtao1
1. College of Computer Science and Engineering, Shijiazhuang University, Shijiazhuang Hebei 050035, China; 2. School of Information Science and Engineering, Yanshan University, Qinhuangdao Hebei 066004, China
Abstract：Concerning the slow convergence and local optimum of the traditional robot path planning algorithms in complicated enviroment, a new path planning algorithm for mobile robots based on Particle Swarm Optimization (PSO)algorithm in repulsion potential field was proposed. Firstly, the grid method was used to give a preliminary path planning of robot, which was regarded as the initial particle population. The size of grids was determined by the obstacles of different shapes and sizes and the total area of obstacles in the map, then mathematical modeling of the planning path was completed. Secondly, the particle position and speed were constantly updated through the cooperation between particles. Finally, the high-security fitness function was constructed using the repulsion potential field of obstacles to obtain an optimal path from starting point to target of robot. Simulation experiment was carried out with Matlab. The experimental results show that the proposed algorithm can implement path optimization and safely avoid obstacles in a complex environment; the contrast experimental results indicat that the proposed algorithm converges fast and can solve the local optimum problem.
韩明, 刘教民, 吴朔媚, 王敬涛. 粒子群优化的移动机器人路径规划算法[J]. 计算机应用, 2017, 37(8): 2258-2263.
HAN Ming, LIU Jiaomin, WU Shuomei, WANG Jingtao. Path planning algorithm of mobile robot based on particle swarm optimization. Journal of Computer Applications, 2017, 37(8): 2258-2263.
[1] 朱大奇,颜明重.移动机器人路径规划技术综述[J].控制与决策,2010,25(7):961-967. (ZHU D Q, YAN M Z. Survey on technology of mobile robot path planning[J]. Control and Decision, 2010, 25(7):961-967.) [2] 蔡自兴,贺汉根,陈虹.未知环境中移动机器人导航控制理论与方法[M]. 北京:科学出版社,2009:8-12. (CAI Z X, HE H G, CHEN H, Mobile robot navigation in unknown environment control theory and method[M]. Beijing:Science Press,2009:8-12.) [3] CLERC M, KENNEDY J. The particle swarm-explosion, stability and convergence in a multidimensional complex space[J]. IEEE Transactions on Evolutionary Computer, 2002, 6(1):58-73. [4] SHI P, HUA J N. Mobile Robot dynamic path planning based on artificial potential field approach[J]. Advanced Materials Research, 2012, 490-495:994-998. [5] SUD A, ANDERSEN E, CURTIS S, et al. Real-time path planning in dynamic virtual environments using multiagent navigation graphs[J]. IEEE Transactions on Visualization and Computer Graphics, 2008, 4(3):526-538. [6] TAVARES R S, MARTINS T C, TSUZUKI M S G. Simulated annealing with adaptive neighborhood:a case study in off-line robot path planning[J]. Expert Systems with Applications, 2011, 38(4):2951-2965. [7] GHATEE M, MOHADES A. Motion planning in order to optimize the length and clearance applying a Hopfield neural network[J]. Expert Systems with Applications, 2009, 36(3):4688-4695. [8] 杜宗宗,刘国栋.基于遗传模拟退火算法的移动机器人路径规划[J].计算机仿真,2009,26(12):118-121. (DU Z Z, LIU G D. Path planning of mobile robot based on genetically simulated annealing algorithm[J]. Computer Simulation, 2009, 26(12):118-121.) [9] ZENG N, ZHANG H, CHEN Y, et al. Path planning for intelligent robot based on switching local evolutionary PSO algorithm[J]. Assembly Automation, 2016, 36(2):120-126. [10] 秦元庆,孙德宝,李宁,等.基于粒子群算法的移动机器人路径规划[J].机器人,2014,26(3):222-225. (QIN Y Q, SUN D B, LI N, et al. Path planning for mobile robot based on particle swarm optimization[J]. Robot, 2014, 26(3):222-225.) [11] SHENG J W, HE G H, GUO W B et al. An improved artificial potential field algorithm for virtual human path planning[C]//Proceedings of the 2010 International Conference on Entertainment for Education Digital Techniques & Systems, LNCS 6249. Berlin:Springer-Verlag, 2010:592-601 [12] SETHANAN K, NEUNGMATCHA W. Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operations[J]. European Journal of Operational Research, 2016, 252(3):969-984. [13] TANG Z, ZHOU Y. A glowworm swarm optimization algorithm for uninhabited combat air vehicle path planning[J]. Journal of Intelligent Systems, 2014, 24(1):69-83. [14] TANG B, ZHU Z, LUO J. A convergence-guaranteed particle swarm optimization method for mobile robot global path planning[J]. Assembly Automation, 2017, 37(1):114-129.
2017, Vol. 37 
