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

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

doi:10.3969/j.iss.0372-2012-2014.03.031

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

更新时间：2025-07-16

- Global Path Planning for ALVBased on Improved Glowworm Swarm Optimization Under Uncertain Environment
- Acta Electronica Sinica Vol. 42, Issue 3, Pages: 616-624(2014)
- 作者机构：
  
  南京理工大学计算机科学与工程学院,江苏,南京,210094
- 作者简介：
- 基金信息：
- DOI：10.3969/j.iss.0372-2012-2014.03.031
  CLC： TP242
- Published：2014
- 稿件说明：
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DU Peng-zhen, TANG Zhen-min, LU Jian-feng, et al. Global Path Planning for ALVBased on Improved Glowworm Swarm Optimization Under Uncertain Environment[J]. Acta Electronica Sinica, 2014, 42(3): 616-624.
DOI：

DU Peng-zhen, TANG Zhen-min, LU Jian-feng, et al. Global Path Planning for ALVBased on Improved Glowworm Swarm Optimization Under Uncertain Environment[J]. Acta Electronica Sinica, 2014, 42(3): 616-624. DOI： 10.3969/j.iss.0372-2012-2014.03.031.

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.

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.

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