Mobile Robot Path Planning Method Based on Particle Swarm Optimization in Dynamic Environment

lilamei; tangxianlun; zhangli; chenlong

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Mobile Robot Path Planning Method Based on Particle Swarm Optimization in Dynamic Environment

更新时间：2023-11-14

- Mobile Robot Path Planning Method Based on Particle Swarm Optimization in Dynamic Environment
- Advanced Engineering Sciences Vol. 47, Issue Z2, Pages: 149-154(2015)
- 作者机构：
  
  1. 重庆邮电大学
  2. 重庆邮电大学自动化学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2015，
  
  Published Online：21 November 2014，
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李腊梅,唐贤伦,张莉,陈龙.基于多智能体粒子群优化的移动机器人动态路径规划[J].工程科学与技术,2015,47(Z2):149-154.

lilamei, tangxianlun, zhangli, et al. Mobile Robot Path Planning Method Based on Particle Swarm Optimization in Dynamic Environment. [J]. Advanced Engineering Sciences 47(Z2):149-154(2015)
李腊梅,唐贤伦,张莉,陈龙.基于多智能体粒子群优化的移动机器人动态路径规划[J].工程科学与技术,2015,47(Z2):149-154. DOI：

lilamei, tangxianlun, zhangli, et al. Mobile Robot Path Planning Method Based on Particle Swarm Optimization in Dynamic Environment. [J]. Advanced Engineering Sciences 47(Z2):149-154(2015) DOI：

摘要

中文摘要: 为了确保机器人在动态环境下无碰撞地到达目标位置，将多Agent粒子群优化算法（multi-agent particle swarm optimization

MAPSO）引入到粒子滤波（particle filter

PF）中，提出一种基于多Agent粒子群优化粒子滤波算法（multi-agent particle swarm optimized particle filter

MAPSOPF）的路径规划方法。通过多Agent系统的竞争、协作机制，调整MAPSOPF中粒子的提议分布，更新预估粒子的位置。与PSOPF算法相比较，该算法的迭代步数减少了50%~60%，计算时间复杂度降低了5%~50%。与改进的遗传算法相比较，MAPSOPF算法的计算时间复杂度降低了95%。三种算法中，MAPSOPF可近似得到最短的路径。实验结果表明该算法可有效应用于移动机器人动态路径规划。

Abstract

Abstract:To guarantee that the robot reaches the target position without the collisions with the moving obstacles in a dynamic environment

a path planning method based on multi-agent particle swarm optimized particle filter (multi-agent particle swarm optimized particle filter

MAPSOPF) is presented by introducing multi-agent particle swarm optimization (multi-agent particle swarm optimization

MAPSO) to the particle filter (particle filter

PF). Through the communication and competition among particles in multi-agent system

the MAPSOPF algorithm can adjust the particles’ proposal distribution and update the prediction of particles. Compared with PSOPF algorithm

MAPSOPF algorithm can cut down iteration times by 50%~60% and reduce time consumption by 5%~50%. It can decrease time consumption by 95% in comparison with genetic algorithm. Among the three algorithms

the proposed algorithm can obtain the shortest path. The simulation experiment results verify that the optimized algorithm can solve path planning problem in dynamic environment effectively.

关键词

路径规划动态多Agent粒子群优化算法

Keywords

path planningdynamic environmentmulti-agent particle swarm optimization algorithm

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