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Research on mobile robot path planning based on multi-strategy improved ant colony algorithm

Published online by Cambridge University Press:  01 October 2025

Xinghua Wang Open the ORCID record for Xinghua Wang [Opens in a new window] ,
Jie Wang Open the ORCID record for Jie Wang [Opens in a new window] ,
Jiawen Cao  and
Ruijin Sun
Xinghua Wang*
Affiliation:
School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, China
Jie Wang
Affiliation:
School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, China
Jiawen Cao
Affiliation:
School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, China
Ruijin Sun
Affiliation:
School of Engineering Innovation, Shanghai Institute of Technology, Shanghai, China
*
Corresponding author: Xinghua Wang; Email: xhwang526@163.com
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Abstract

Aiming at the issues of traditional ant colony algorithm (ACO) in mobile robot path planning, including initial search blindness, susceptibility to local optima, and slow convergence, this paper proposes a multi-strategy improved ant colony algorithm (MS-ACO). Firstly, dynamic non-uniform distribution of initial pheromones is implemented by integrating the repulsive field from artificial the potential field method. Secondly, the heuristic information is enhanced to improve global search capability while constraining unnecessary path turns. Thirdly, an improved pheromone update strategy is developed by adopting distinct updating mechanisms for different evolutionary phases. Finally, dynamic parameter adaptation is achieved through optimized weight coefficients and volatility coefficients that coordinate with the pheromone update strategy, better aligning with the iterative characteristics of ant colony optimization. Experimental results demonstrate that MS-ACO effectively addresses the limitations of traditional ACO. Under identical experimental conditions, it achieves a 30.4% reduction in path length, 37.8% decrease in pathfinding time, and 71% fewer turns compared to conventional methods, verifying the feasibility and superiority of the proposed algorithm.

Keywords

ant colony algorithmpath planningmobile robot

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 10 , October 2025 , pp. 3594 - 3614
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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