电子学报 ›› 2014, Vol. 42 ›› Issue (3): 616-624.DOI: 10.3969/j.iss.0372-2012-2014.03.031

• 科研通信 • 上一篇    

不确定环境下基于改进萤火虫算法的地面自主车辆全局路径规划方法

杜鹏桢, 唐振民, 陆建峰, 孙研   

  1. 南京理工大学计算机科学与工程学院, 江苏南京 210094
  • 收稿日期:2013-08-14 修回日期:2013-10-15 出版日期:2014-03-25
    • 作者简介:
    • 杜鹏桢 男,1982年11月出生.2005年本科毕业于南京理工大学电子工程与光电技术学院,现为南京理工大学计算机科学与工程学院硕博连读生,从事机器人及智能计算相关研究.E-mail:h.k@foxmail.com
    • 基金资助:
    • 国家自然科学基金 (No.91220301,No.61371040); 高等学校学科创新引智计划资助课题 (No.B13022)

Global Path Planning for ALVBased on Improved Glowworm Swarm Optimization Under Uncertain Environment

DU Peng-zhen, TANG Zhen-min, LU Jian-feng, SUN Yan   

  1. College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
  • Received:2013-08-14 Revised:2013-10-15 Online:2014-03-25 Published:2014-03-25
    • Supported by:
    • National Natural Science Foundation of China (No.91220301, No.61371040); Supported by Overseas Expertise Introduction Project for Discipline Innovation  (“111Project”) (No.B13022)

摘要: 针对地面自主车辆的特点,提出了一种基于改进萤火虫算法(Glowworm Swarm Optimization,GSO)的路径规划方法.首先利用GSO覆盖多个局部最优解的能力,一次生成多条规划路径;然后提出两种路径切换算法,分别用于调优和脱困.在通过路径交叉点时,调优切换算法对交叉路径进行重新评估并切换到较优路径,最终达到实际行驶路径的最优化.在遇到环境发生改变时,脱困切换算法通过启发式搜索快速切换到适当路径,重用了原搜索结果,避免了二次规划.通过大量的仿真实验及实际试用,证明了所提方法的可行性和有效性.

关键词: 路径规划, 地面自主车辆, 人工萤火虫算法, 二次规划, 路径切换

Abstract: According to the characteristics of autonomous land vehicle,a global path planning method based on improved glowworm swarm optimization(GSO)is proposed.Firstly,more than one path is generated with GSO which covers multiple local optima.Then two path switching algorithms are proposed,of which one aims at optimization and the other aims at rescue.When the cross point is passed through,the optimization switching algorithm revaluates the paths,switches to the optimum path,and ultimately attains optimal actual travel route.When the environment changes,the rescue switching algorithm switches to the appropriate path by heuristic search,which reuses the original search results,avoiding the secondary planning.Many simulation experiments and actual trial show that the proposed method is feasible and effective.

Key words: path planning, autonomous land vehicle(ALV), glowworm swarm optimization(GSO), secondary planning, path switching

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