Abstract
In this paper, the problem of collision avoidance path optimization for multi-AGV systems in unmanned warehouses is studied. A multi-AGV collision avoidance path optimization strategy based on elastic time window and improved ant colony algorithm is proposed. In this paper, the traditional ant colony algorithm is improved by heuristic information and pheromone update strategy to improve the execution speed and optimization ability of the algorithm. The priority scheduling of AGV tasks and the improvement of conflict resolution strategies are proposed to solve the different path conflicts between multiple AGVs. Based on the environment of the e-commerce logistics unmanned warehouse, the MATLAB simulation software is used to model and analyze the multi-AGV collision avoidance path planning. The experimental results show that the multi-AGV collision avoidance path planning can be realized based on the elastic time window and the improved ant colony algorithm, and the optimal collision avoidance path can be found in a short time.
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Notes
- 1.
E represents the end grid; P represents the grid where the ants are located.
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Yang, Y., Zhang, J., Liu, Y., Song, X. (2020). Multi-AGV Collision Avoidance Path Optimization for Unmanned Warehouse Based on Improved Ant Colony Algorithm. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_41
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DOI: https://doi.org/10.1007/978-981-15-3425-6_41
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