智能仓储中的多机器人调度方法

近年来，传统仓储系统已满足不了日益增长的订单需求并已渐渐向智能仓储转变。针对智能仓储中移动机器人的调度问题，以移动机器人执行任务时的转向次数、路程代价、最大任务等待时间为优化目标，提出一种兼顾任务分配和路径规划的调度算法。算法采用遗传算法进行任务分配，同时以多个移动机器人为目标进行任务分配，保证每个机器人分配到的任务没有重复。然后采用Q-learning算法对机器人分配到的任务进行路径规划，根据转向次数和路程代价约束路径，对于路径转向和每一步可行的动作均设有惩罚值，最终形成一条转向次数少、行程较短的路径。通过将该算法与其他算法进行对比，证实了该算法的有效性。

Multi-robot Scheduling Method in Intelligent Warehouse

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.