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29 August 2024 Path planning for AGVs based on a hybrid particle swarm optimization and ant colony optimization algorithm
Yunying Zhou, Yan Jiang
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Proceedings Volume 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024); 132490N (2024) https://doi.org/10.1117/12.3041835
Event: 2024 International Conference on Computer Vision, Robotics and Automation Engineering, 2024, Kunming, China
Abstract
To address the issues of low search efficiency and slow convergence in the later stages of traditional ant colony optimization (ACO) algorithms for AGV path planning, this paper proposes an AGV path planning method based on a hybrid particle swarm optimization (PSO) and ant colony optimization (ACO) algorithm. Firstly, a grid map method is used to establish the environmental model. Then, a diversity enhancement mechanism and dynamic adjustment of the inertia factor in the PSO are introduced to perform initial path planning. Based on the initially planned path, pheromone-rich regions are identified on the map. Improvements are made to the pheromone update mechanism and the heuristic function in the node transition probability formula of the ACO to enhance the algorithm's convergence. The improved ACO is then used for path planning. Finally, a secondary optimization is performed on the planned path to obtain the optimal path. The effectiveness of the hybrid algorithm is validated through a comparison with results from a solely improved ACO.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yunying Zhou and Yan Jiang "Path planning for AGVs based on a hybrid particle swarm optimization and ant colony optimization algorithm", Proc. SPIE 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024), 132490N (29 August 2024); https://doi.org/10.1117/12.3041835
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KEYWORDS
Particle swarm optimization
Particles
Mathematical optimization
Computer simulations
Detection and tracking algorithms
Algorithm testing
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