As goods-to-person systems become increasingly common in warehouses, multiload automated guided vehicles (AGVs) face growing challenges in avoiding path conflicts and deadlocks during collaborative operations. This study aims to propose a conflict-free path planning strategy to enhance overall efficiency and system stability.
The warehouse is modeled as a uniform cellular grid, with AGVs represented as dynamic cells possessing state attributes. Task sequencing is optimized via dynamic programming based on the Traveling Salesman Problem. An improved A* algorithm – integrating turning penalties and dynamic weights – generates initial AGV paths. These serve as guides for a cellular automaton-based simulation that resolves Head-on, Intersection and Parking Conflicts through a Von Neumann neighborhood rule set and a hierarchical decision-making mechanism.
Simulation results demonstrate that the proposed method achieves optimal single-AGV path, improves coordination among multiple AGVs and enhances system robustness. It effectively prevents collisions and deadlocks, significantly boosting warehouse operational efficiency.
This study introduces the CA*-MLACFP framework: a conflict-free, multistage path planning method for multiload AGVs that integrates improved A* search with a cellular automaton and a layered conflict resolution strategy. The approach offers high practical applicability and scalability.
