Two-level vehicle path planning model for multi-warehouse robots with conflict solution strategies and improved ACO
Pan Wu, Lingshu Zhong, Jingwen Xiong, Yuhao Zeng, Mingyang Pei
Abstract
With the rapid development of warehouse robots in logistics and other industries, research on their path planning has become increasingly important. Based on the analysis of various conflicts that occur when the warehouse robot travels, this study proposes a two-level vehicle path planning model for multi-warehouse robots, which integrates static and dynamic planning to improve operational efficiency and reduce operating costs. In the static phase, the blockage factor is introduced to enhance the ant colony optimization (ACO) algorithm as a negative feedback mechanism to effectively avoid the blockage nodes during movement. In the dynamic stage, a dynamic priority mechanism is designed to adjust the routing strategy in real time and give the optimal path according to the real situation. To evaluate the model's effectiveness, simulations were performed under different operating environments and application strategies based on an actual grid environment map. The simulation results confirm that the proposed model outperforms other methods in terms of average running distance, number of blocked nodes, percentage of replanned paths, and average running time, showing great potential in optimizing warehouse operations.