Robot path planning algorithm based on improved ACO algorithm

LIU Xuefang; ZENG Guohui; LIU Jin

doi:10.13873/j.1000-9787(2019)10-0129-03

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Robot path planning algorithm based on improved ACO algorithm

更新时间：2025-09-11

- Robot path planning algorithm based on improved ACO algorithm
- Issue 10, Pages: 129-131+138(2019)
- 作者机构：
  
  上海工程技术大学电子电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2019)10-0129-03
  CLC： TP18;TP242
- Published：2019
- 稿件说明：
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LIU Xuefang, ZENG Guohui, LIU Jin. Robot path planning algorithm based on improved ACO algorithm[J]. 2019, (10): 129-131+138.
DOI：

LIU Xuefang, ZENG Guohui, LIU Jin. Robot path planning algorithm based on improved ACO algorithm[J]. 2019, (10): 129-131+138. DOI： 10.13873/j.1000-9787(2019)10-0129-03.

摘要

针对机器人路径规划中

传统蚁群算法收敛速度慢、易陷入局部最优解等问题

提出了一种移动机器人路径规划的改进蚁群优化(ACO)算法。用栅格法建立环境模型

并基于人工势场建立启发信息素矩阵

降低了蚂蚁在初始阶段搜索的盲目性;引入激励函数

降低搜索过程中的死锁现象;改进信息素的更新机制

增强了优秀蚂蚁对全局路径规划的影响。仿真结果表明:改进后蚁群算法的机器人路径规划算法加快了收敛速度

具有较强的鲁棒性和全局寻优能力。

Abstract

Aiming at the problems that the traditional ant colony optimization（ACO） algorithm has a slow convergence speed and easy to fall into local optimal solution in path planning of mobile robot

an improved ACO algrithm for path planning of robot is proposed.Environment model is established by the grid method

and the heuristic pheromone matrix is set up based on the artificial potential field

which reduce the blindness of the ants in the initial phase of the search.Excitation function is introduced.Dead-lock phenomenon in search process is reduced.Update mechanism of pheromone is improved

and the impact of excellent ants on global path planning is enhanced. The simulation results show that the proposed improved ant colony algorithm robot path planning algorithm accelerates the convergence speed and has strong robustness and global optimization ability.

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