Task and intelligent path planning algorithm for teams of AGVs based on multi-stage method

Attributed to the rapid development of ecommerce, the demand for logistics is increasing day by day. In this context, modern warehousing technology with Multi-AGV Cooperation System (MACS) as the core came into being. The large-scale Automated Guided Vehicle (AGV) can transport multiple cargos in a single task owing to its large load capacity and strong endurance, which makes it a trend to use large-scale AGV to transport cargos in indoor warehouses or outdoor parks. However, it should be noted that there are a series of problems, such as resource allocation and path conflict, when using teams of AGVs to perform multiple transportation tasks, and it is a challenging task to find an ideal task and path planning scheme for the scenario where multi-AGV perform multiple transportation tasks. In view of this, the present study proposed a multi-stage optimization method to effectively obtain the approximate optimal solution to solve the aforesaid problem. In this proposed method, a 0-1 integer linear programming model was constructed to solve the problem of assigning multiple transportation tasks to multiple AGVs in the first stage, and then in the second stage we planned the optimization sequence in which AGV passes through each pick-up/delivery location when performing multiple transportation tasks. In the third stage we solved the path planning problem of teams of AGVs moving to each target location without collision. In addition, simulation experiments were carried out, which verified that the proposed method can achieve superior performance in path distance and task completion time when performing multiple tasks.