[1]蒲兴成,李俊杰,吴慧超,等.基于改进粒子群算法的移动机器人多目标点路径规划[J].智能系统学报,2017,12(03):301-309.[doi:10.11992/tis.201606046]
 PU Xingcheng,LI Junjie,WU Huichao,et al.Mobile robot multi-goal path planning using improved particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2017,12(03):301-309.[doi:10.11992/tis.201606046]
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基于改进粒子群算法的移动机器人多目标点路径规划(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年03期
页码:
301-309
栏目:
学术论文—智能系统
出版日期:
2017-06-25

文章信息/Info

Title:
Mobile robot multi-goal path planning using improved particle swarm optimization
作者:
蒲兴成1 李俊杰2 吴慧超2 张毅3
1. 重庆邮电大学 数理学院, 重庆 400065;
2. 重庆邮电大学 智能系统及机器人研究所, 重庆 400065;
3. 重庆邮电大学 先进制造学院, 重庆 400065
Author(s):
PU Xingcheng1 LI Junjie2 WU Huichao2 ZHANG Yi3
1. School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. Research Center of Intelligent System and Robot, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
3. Advanced Manufacturing Engineering School, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
关键词:
移动机器人多目标点路径规划蚁群算法改进粒子群算法反向学习策略惯性权重学习因子
Keywords:
mobile robotmulti-goal path planningACOimproved PSOopposition-based learninginertia weightlearning factors
分类号:
TP242.6
DOI:
10.11992/tis.201606046
摘要:
针对移动机器人遍历多个目标点的路径规划问题,提出了一种基于改进粒子群算法和蚁群算法相结合的路径规划新方法。该方法将目标点的选择转化为旅行商问题,并利用蚁群算法进行优化,定义了每两个目标点之间的路径规划目标函数,利用粒子群算法对其进行优化。针对粒子群算法存在的早熟现象,将反向学习策略引入粒子群算法,并对粒子群算法的惯性权重和学习因子进行改进。性能测试结果表明,改进的粒子群算法能有效避免粒子早熟现象,提高粒子群算法的寻优能力及稳定性。仿真实验结果验证了新方法能有效地实现机器人的多目标点无碰撞路径规划。真实环境下的实验结果证明了新方法在机器人多目标点路径规划的实际应用中也具有有效性。
Abstract:
To solve the problem of multi-goal path planning for mobile robots that pass multiple goals, a new path planning method, based on improved particle swarm optimization (PSO) and ant colony optimization (ACO), is proposed. In this new method, the first step is to use an improved PSO, which has high convergence, to optimize the path between two goals among a sequence of goals. The second step is to use the ACO to obtain the shortest path for all target points. The performance experimental result demonstrates that the improved PSO algorithm can effectively avoid premature convergence and enhances search ability and stability. Simulation results show that the improved PSO algorithm can make a mobile robot realize collision-free multi-goal path planning effectively . Experiments in a real environment demonstrate that this algorithm has practical application for multi-goal path planning.

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备注/Memo

备注/Memo:
收稿日期:2016-06-30。
基金项目:国家自然科学基金(51604056),重庆市科学技术委员会项目(cstc2015jcyBx0066);重庆市教委项目(KJ1400432).
作者简介:蒲兴成,男,1973年生,副教授,博士,主要研究方向为非线性系统、随机系统和现代智能算法。主持重庆邮电大学校级科研项目3项,参与国际合作项目1项,参与省部级项目6项。发表学术论文30余篇,出版著作1部;李俊杰,男,1990年生,硕士研究生,主要研究方向为移动机器人自主导航;吴慧超,女,1990年生,硕士研究生,主要研究方向为智能服务机器人。
通讯作者:李俊杰.E-mail:lijunjie166@126.com.
更新日期/Last Update: 2017-06-25