Research on Path Planning of Mobile Robot Based on Improved Immune-Ant Colony Algorithm

Guanyi Liu (), Xuewei Li, Yumeng Mao, Jingxiao Sun, Dehan Jiao and Xuemei Li
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Guanyi Liu: Beijing Jiaotong University
Xuewei Li: Beijing Jiaotong University
Yumeng Mao: Beijing Jiaotong University
Jingxiao Sun: Beijing Jiaotong University
Dehan Jiao: Beijing Jiaotong University
Xuemei Li: Beijing Jiaotong University

A chapter in IEIS 2023, 2024, pp 185-197 from Springer

Abstract: Abstract In order to solve the problems of low search efficiency and easy to fall into local optimal solution when using traditional ant colony algorithm for path planning of mobile robots, an improved immune ant colony hybrid algorithm is proposed. Firstly, the optimal solution is obtained by using the fast global convergence of the immune algorithm, which is used as the initial pheromone distribution of the ant colony algorithm. On this basis, the improved ant colony algorithm is used for global path planning, which effectively solves the problem that the search efficiency is low due to the lack of pheromone in the early stage. By comparing the experimental results of the two algorithms, the advantages of hybrid algorithm are illustrated. The experimental results show that the improved Immune Ant Colony Algorithm can better solve the path planning problem of mobile robots in complex environments.

Keywords: path planning; immune algorithm; ant colony algorithm; mobile robot (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-97-4137-3_15

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DOI: 10.1007/978-981-97-4137-3_15

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