计算机与现代化

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仓储物流机器人批量拣选路径规划仿真

  

  1. (中北大学经济与管理学院,山西太原030051)
  • 收稿日期:2016-10-14 出版日期:2017-03-09 发布日期:2017-03-20
  • 作者简介:潘成浩(1990-),男,山东费县人,中北大学经济与管理学院硕士研究生,研究方向:仓储物流机器人路径规划与调度; 郭敏(1969-),男,山西太原人,教授,博士,研究方向:系统建模与仿真,证据推理理论,供应链管理。

Batch Picking Path Planning Simulation of Warehouse Mobile Robot

  1. (College of Economics and Management, North University of China, Taiyuan 030051, China)
  • Received:2016-10-14 Online:2017-03-09 Published:2017-03-20

摘要: 针对仓储物流机器人在拣选作业过程中难以进行高效实时的路径规划问题,提出一种有效的解决方法。首先,根据拣选作业的需要建立一个灵活的仓储空间模型并对拣选作业任务流程进行描述。其次,根据批量拣选作业任务的特点,建立以路径总长度最小为优化目标的旅行商问题的数学模型。再次,提出改进的自适应遗传算法解决旅行商问题。最后,在考虑路径转折角代价的前提条件下,提出改进的A*算法,并与改进的自适应遗传算法相结合实现批量拣选的路径规划。仿真结果表明,该方法具有较快的收敛速度、较小的平均路径长度以及较少的算法运行时间,能很好地适应机器人批量拣选路径规划的要求。

关键词: 移动机器人, 拣选作业, 路径规划, 旅行商问题, 遗传算法, A*算法

Abstract: In order to solve the problem of warehouse mobile robot path planning with high efficiency and real time in the process of picking operation, an effective method was proposed. First of all, according to the demand of order picking, a flexible storage space model was made and the task of order picking was described. Secondly, according to the characteristics of the batch picking task, the mathematical model of the Traveling Salesman Problem (TSP) was established. Once again, an improved adaptive genetic algorithm was proposed to solve the TSP. Finally, under the premise of considering the cost of the path turning angle, an improved A*algorithm combined with the improved adaptive genetic algorithm was used to solve the problem of batch picking path planning. The simulation results show that the method which meets the requirement of batch picking path planning of warehouse mobile robot is of faster convergence speed, less average path length and less algorithm running time.

Key words:  mobile robot, batch picking, path planning, traveling salesman problem (TSP), genetic algorithm, A* algorithm

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