Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (8): 2258-2263.DOI: 10.11772/j.issn.1001-9081.2017.08.2258

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Path planning algorithm of mobile robot based on particle swarm optimization

HAN Ming1,2, LIU Jiaomin2, WU Shuomei1, WANG Jingtao1   

  1. 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
  • Received:2017-01-17 Revised:2017-03-05 Online:2017-08-10 Published:2017-08-12
  • Supported by:
    This work is partially supported by the Science and Technology Plan Projects of Hebei Province (15220327,16222101D-2),the Youth Topnotch Talent Program of Hebei Universities and Colleges (BJ2017105).


韩明1,2, 刘教民2, 吴朔媚1, 王敬涛1   

  1. 1. 石家庄学院 计算机科学与工程学院, 石家庄 050035;
    2. 燕山大学 信息科学与工程学院, 河北 秦皇岛 066004
  • 通讯作者: 韩明
  • 作者简介:韩明(1984-),男,河北行唐人,讲师,博士,CCF会员,主要研究方向:智能机器人、模式识别与控制;刘教民(1958-),男,河南西峡人,教授,博士生导师,博士,CCF会员,主要研究方向:智能控制、模式识别;吴朔媚(1977-),女,河北邢台人,讲师,硕士,主要研究方向:模式识别、机器视觉;王敬涛(1984-),女,河北邯郸人,助教,硕士,主要研究方向:智能计算、模式识别。
  • 基金资助:

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.

Key words: grid method, Particle Swarm Optimization (PSO), path planning, progress factor, fitness function

摘要: 针对移动机器人在复杂环境下采用传统方法路径规划收敛速度慢和局部最优问题,提出了斥力场下粒子群优化(PSO)的移动机器人路径规划算法。首先采用栅格法对机器人的移动路径进行初步规划,并将栅格法得到的初步路径作为粒子的初始种群,根据障碍物的不同形状和尺寸以及障碍物所占的地图总面积确定栅格粒度的大小,进而对规划路径进行数学建模;然后根据粒子之间的相互协作实现对粒子位置和速度的不断更新;最后采用障碍物斥力势场构造高安全性适应度函数,从而得到一条机器人从初始位置到目标的最优路径。利用Matlab平台对所提算法进行仿真,结果表明,该算法可以实现复杂环境下路径寻优和安全避障;同时还通过对比实验验证了算法收敛速度快,能解决局部最优问题。

关键词: 栅格法, 粒子群优化, 路径规划, 步进因子, 适应度函数

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