Robot Path Planning Optimization Based on Fusion of Improved Ant Colony Algorithm and A* Algorithm

Authors

Lanying Yang, School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610000, China
Chao Li, School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610000, China
Haifeng Zou, The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610000, China
Jiangtao Wan, School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610000, China
Renqiang Zhang, School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610000, China
Hui Liu, School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610000, China
Hong Lu, School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610000, China

Abstract

Abstract: To improve slow search efficiency and achieve real-time obstacle avoidance in traditional ant colony algorithms, an adaptive ant colony algorithm was proposed. A guidance direction mechanism was introduced to shorten the time of node selection. The A* algorithm's path-finding mechanism was introduced into the heuristic function to reduce the length and number of circles of the optimal path solution. The route planned by the traditional A* algorithm was used as the initial iteration data of the ant colony algorithm in global path planning, so as to solve the problem of slow initial convergence of the ant colony algorithm. The breadth first search mechanism of the traditional A* algorithm was introduced into the ant colony algorithm to address the issue of multiple iterations in the algorithm. In order to verify the superiority of this algorithm, two representative environmental models were adopted and compared with two traditional ant colony algorithms and genetic algorithms through comprehensive experiments. Comparative experiments demonstrate that the adaptive path-finding ant colony algorithm exhibits significant advantages over AS, ACS, and EAS algorithms in path planning, featuring faster convergence and superior path generation.

Recommended Citation

Yang, Lanying; Li, Chao; Zou, Haifeng; Wan, Jiangtao; Zhang, Renqiang; Liu, Hui; and Lu, Hong (2025) "Robot Path Planning Optimization Based on Fusion of Improved Ant Colony Algorithm and A* Algorithm," Journal of System Simulation: Vol. 37: Iss. 11, Article 21.
DOI: 10.16182/j.issn1004731x.joss.24-0651
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss11/21

First Page

2956

Last Page

2965

CLC

TP391.9

Recommended Citation

Yang Lanying, Li Chao, Zou Haifeng, et al. Robot Path Planning Optimization Based on Fusion of Improved Ant Colony Algorithm and A* Algorithm[J]. Journal of System Simulation, 2025, 37(11): 2956-2965.

DOI

10.16182/j.issn1004731x.joss.24-0651