NMS-EACO: A Novel Multi-Strategy ACO for Mobile Robot Path Planning
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Authors
Zhang, Chao
Ma, Jing
Wang, Xin
Xu, Jianwei
Guo, Chuanchen
Supervisor
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Journal Article
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Publisher
MDPI AG
Abstract
Ant Colony Optimization (ACO) has been widely used in engineering implementation due to its simplicity and effectiveness. However, it often faces challenges such as slow convergence, susceptibility to local optima, and generating paths with excessive turning points. To address these limitations, this paper introduces a Novel Multi-Strategy Enhanced Ant Colony Optimization algorithm (NMS-EACO) for mobile robot path planning under nonholonomic constraints. NMS-EACO integrates five key strategies: an A*-guided heuristic function, an adaptive enhanced pheromone update rule, a state transition probability under nonholonomic constraints, a smoothing factor embedded in the state transition probability, and a global path smoothing technique. Comprehensive simulation experiments are conducted across six distinct map types, with comparisons made against six existing algorithms through extensive trials.Results demonstrate that NMS-EACO significantly improves convergence speed, enhances global search capability, and reduces path irregularities. These results validate the robustness and efficiency of the proposed multi-strategy method for nonholonomic mobile robot navigation.Description
Keywords
40 Engineering, 4009 Electronics, Sensors and Digital Hardware, 0906 Electrical and Electronic Engineering, 4009 Electronics, sensors and digital hardware
Source
Electronics, ISSN: 1450-5843 (Print); 2079-9292 (Online), MDPI AG, 14(17), 3440-3440. doi: 10.3390/electronics14173440
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).