Optimized multi-step ant colony algorithm for robot path planning problem solving

HU Jiabin; WANG Xiangshu; ZHANG Qi; QUAN Ruikun

doi:10.13873/J.1000-9787(2021)10-0121-04

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Optimized multi-step ant colony algorithm for robot path planning problem solving

更新时间：2025-09-11

- Optimized multi-step ant colony algorithm for robot path planning problem solving
- Issue 10, Pages: 121-124(2021)
- 作者机构：
  
  1. 重庆大学重庆大学-辛辛那提大学联合学院
  2. 重庆大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2021)10-0121-04
  CLC： TP18;TP242
- Published：2021
- 稿件说明：
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HU Jiabin, WANG Xiangshu, ZHANG Qi, et al. Optimized multi-step ant colony algorithm for robot path planning problem solving[J]. 2021, (10): 121-124.
DOI：

HU Jiabin, WANG Xiangshu, ZHANG Qi, et al. Optimized multi-step ant colony algorithm for robot path planning problem solving[J]. 2021, (10): 121-124. DOI： 10.13873/J.1000-9787(2021)10-0121-04.

摘要

针对蚁群算法收敛速度慢

规划路径存在冗余拐点不是最短路径等的一系列问题

提出优化的多步长蚁群算法。通过扩大机器人的视野域和活动域并加入简化算子

增加路径平滑度;通过差异化更新信息素并改进启发函数

促使机器人倾向终点移动

以提高收敛速度。应用MATLAB程序对改进后的算法与原蚁群算法和多步长蚁群算法进行仿真对比

通过多次仿真实验

优化的多步长蚁群算法效果显著。

Abstract

Aiming at a series of problems such as low convergence speed and roughness of paths planned that the traditional ant colony optimization（ACO） has

an improved multi-step ant colony algorithm is proposed.In order to increase the smoothness of path

the field of view and movement of robot are expanded and the simplified operator is added.By updating pheromone differentially and improving the heuristic function to make the robot inclined to direction close to the end point

the convergency speed is accordingly increased.Comparing the improved algorithm with traditional ACO and multi-step ACO by numerous simulation experiments through MATLAB software

the improved multi-step ant colony algorithm shows great superiority.

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