###### Abstract  
In order to solve the problem of multi-AGV path planning in a packaging workshop, this paper proposes a multi-AGV path-planning method based on time windows. A grid method is selected to build a map model. A penalty function is added to the A* algorithm to reduce the number of invalid turns made by AGVs during transportation. The AGV priorities are set according to the differences in AGV transport states. At the same time, the raster time window method is used to describe the five types of conflict that may occur in the process of multi-AGV transport. Combined with the AGV priorities, a multi-AGV anti-collision strategy is provided to realize the conflict-free path planning of multiple AGVs. The algorithm's effectiveness was verified by simulation, and a reasonable quantity of AGVs was proposed based on AGV sensitivity analysis. The improved A* algorithm combined with the time window method can realize multi-AGV collision-free transportation and improve the efficiency and reliability of multi-AGV transportation.  
automated guided vehicles (AGVs); multi-AGV path planning; anti-collision strategy; improved A* algorithm; time window method +
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A* algorithm with the DWA algorithm to reduce the AGV travel distance and improve transportation efficiency. [PERSON] [13] used the weighted Manhattan distance as the heuristic function, reduced the number of nodes traversed by the A* algorithm, added the turning cost parameter, and improved the smoothness of the path.  
In the actual transportation process of AGVs, in the face of complex transportation requirements, a single AGV cannot meet the transportation needs, and multiple AGVs are needed to meet transport requirements at the same time. Therefore, the problem of collision-free or collision avoidance path optimization for multi-AGV transportation has also been designated a research hotspot in recent years. Algorithms are used to realize conflict-free path planning [14] or multi-AGV path planning in specific environments, such as docks [15], logistics warehousing [16], etc. In the research on multi-AGV path planning for the A* algorithm, [PERSON] et al. [17] proposed two A* algorithms to deal with multi-AGV path planning. In case of AGV conflict, AGVs that are automatically adjusted are set to point to the linked list according to the remaining AGV planning caused by AGV path replanning, and AGVs behind the linked list are replanned. The second method is a multi-step forward algorithm, which predicts the path that the AGV is about to pass to predict the conflict in advance and choose the path with the least possibility of conflict. Compared with passive AGV conflict processing, the proactive algorithm has better initiative and reduces the loss of path replanning after AGV conflict. However, the iterative method is used to gradually fit the path, greatly increasing the computation amount of the algorithm. [PERSON] et al. [18] added the degree of road congestion to the estimation function so that the AGV can avoid the congested path and actively choose the road with a clear path in the process of path specification. However, this method is suitable for one-way traffic paths. [PERSON] et al. [19] proposed a hybrid genetic algorithm based on scheduling and the A* algorithm, used the A* algorithm to carry out path planning and scheduling to avoid AGV node conflicts and designed a task-based chromosome structure to solve AGV scheduling and path planning problems for small and medium-sized flexible jobs. [PERSON] et al. [20] set the AGV priority according to the remaining current of the AGV battery, improved the A* algorithm, and realized multi-AGV dynamic collision-free path planning according to the priority strategy and collision avoidance rules.