Abstract: In recent years, the traditional storage system has been unable to meet the increasing demand and has gradually turned to intelligent storage. Aiming at the scheduling problem of robots in intelligent warehouse and optimizing the turning times, distance cost and the maximum task waiting time, a scheduling algorithm for both task assignment and path planning is proposed. To ensure that the tasks assigned to each robot are not repeated, tasks are assigned with genetic algorithm and tasks are assigned for multiple mobile robots. Then Q-learning algorithm is used to carry out path planning for tasks assigned by the robot. The path is constrained according to the account of turns and the cost of the distance, and the penalty value is set for the turning of the path and the feasible action in each step. Finally, a path with less turning times and shorter travel is formed. The effectiveness of the proposed algorithm is verified by comparing it with other algorithms.

Key words: intelligent storage, genetic algorithm, Q-learning, task assignment, path planning