基于改进蚁群算法的机器人路径规划仿真实验

曲靖师范学院学报 ›› 2023, Vol. 42 ›› Issue (3): 39-44.

基于改进蚁群算法的机器人路径规划仿真实验

罗子灿, 何广, 宾厚, 郑湘明

湖南工业大学商学院,湖南株洲 412007

收稿日期:2023-03-24 出版日期:2023-05-26 发布日期:2023-06-30
通讯作者: 宾厚,湖南工业大学商学院教授,博士,主要从事物流与供应链管理研究.
作者简介:罗子灿,湖南工业大学商学院讲师,博士,主要从事运营管理和营销管理研究.
基金资助:
国家社会科学基金项目“快递包装回收行为机理与逆向物流网络优化研究”(21BGL025);中国包装联合会中国包装产业深度转型与可持续发展专项研究项目“中国包装产业区域布局现状与布局优化方式研究”(2021LBLY09).

Simulation Experiment of Robot Path Planning Based on Improved Ant Colony Algorithm

LUO Zican, HE Guang, BIN Hou, ZHENG Xiangming

School of Business,Hunan University of Technology,Zhuzhou Hunan 412007,China

Received:2023-03-24 Published:2023-05-26 Online:2023-06-30

摘要/Abstract

摘要： 针对传统蚁群算法在机器人路径规划中存在的收敛速度慢,转角次数多等问题,提出一种改进算法. 首先,引入转角启发函数以增加节点选择的指向性,减少转角次数;其次提出一种新的信息素更新机制,对优质蚂蚁的信息素浓度进行二次更新,加快算法收敛速度;最后,利用仿真软件对改进算法进行实验. 仿真实验结果表明,经过改进后算法的性能极大提升,在两种不同复杂度环境中,迭代次数分别下降了82.1%和87.6%,转角次数减少了36.8%,验证了改进算法的优越性.

关键词: 蚁群算法, 移动机器人, 路径规划

Abstract: An improved algorithm is proposed to address the problems of slow convergence speed and multiple turns in traditional ant colony algorithm for robot path planning. Firstly, a corner heuristic function is introduced to increase the directionality of node selection and reduce the number of corners. Secondly, a new pheromone updating mechanism is proposed to update the pheromone concentration of high-quality ants twice to speed up the convergence of the algorithm. Finally, experiments are conducted on the improved algorithm using simulation software. The simulation experimental results show that the performance of the improved algorithm has greatly improved. In two different complexity environments, the number of iterations has decreased by 82.1% and 87.6%, respectively, and the number of corners has decreased by 36.8%, verifying the superiority of the improved algorithm.

Key words: ant colony, mobile robot, path planning

中图分类号:

TP242
TP18

罗子灿, 何广, 宾厚, 郑湘明. 基于改进蚁群算法的机器人路径规划仿真实验[J]. 曲靖师范学院学报, 2023, 42(3): 39-44.

LUO Zican, HE Guang, BIN Hou, ZHENG Xiangming. Simulation Experiment of Robot Path Planning Based on Improved Ant Colony Algorithm[J]. Journal of Qujing Normal University, 2023, 42(3): 39-44.