Sparrow Search Algorithm Based Path Planning for Warehouse AGV

With the advancement of the logistics sector, the path optimization is a key element for improving the quality, efficiency and cost reduction of warehouse logistics. In this paper, sparrow search algorithm (SSA) is studied for path planning. Firstly, a regular grid map model for the storage system is created, afterward, a general flowchart for optimizing the path to the given picking task is given by using SSA. Then, the path search direction is changed as four-direction moving mode, so as to avoid the collision between the automated guided vehicle (AGV) and the edge of the shelf in the process of picking, and to be more in line with the movement process of the AGV in the storage system. Finally, the efficiency of the designed scheme is confirmed by simulation examples. Compared with genetic algorithm (GA), the average optimal path for single task is shortened by 1.2 meters when using SSA in 8-direction search, the average running time is reduced by 24.35% in 4-direction search, the convergence speed of SSA is higher. The optimal path for multiple tasks is shortened by 1.9 meters and the running time is reduced by 4.37% averagely. The effectiveness of proposed approach is fully confirmed.