Fantong Wang, Ling Wang, Suan Xu, Xiai Chen, and Binrui Wang
Multi-AGV path planning, ant colony optimisation (ACO), node vector time window, automated warehouse system, conflict resolution
For conflict-free path planning of multiple automated guided vehicles (multi-AGVs) in a flexible manufacturing system, this paper proposes a two-stage method that combines the improved ant colony optimisation (ACO) and the node vector time window algorithm. Firstly, considering the limitations of the traditional ACO in solving the shortest path problem, this paper proposes three improved versions of the ACO algorithm. The improved ACO algorithm effectively improves the convergence speed and quality of the ACO in shortest-path planning in the first stage, in which two types of conflicts are considered, including node conflicts and path conflicts. Then, a node vector time window algorithm is used to detect collisions and deadlocks among automated guided vehicles (AGVs) in the second stage. The conflict resolution strategies are formulated for different types of conflicts. In the path replanning resolution strategy, this paper considers the additional impact of path replanning on the remaining conflicts. Finally, in this paper, the simulations are performed for single AGV path planning and multi-AGV conflict-free scheduling in an automated warehouse system. The results indicate that, compared with other optimisation algorithms, the improved ACO algorithm can derive shorter paths with less time for computation, showing excellent stability and robustness. In contrast to other conflict resolution strategies, the conflict resolution strategies based on the node vector time window algorithm proposed in this paper reduce the occurrence of conflicts, significantly enhancing system operational efficiency. This study provides valuable insights for research or applications in the multi- AGV path planning fields.
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