面向智能仓储的多机器人任务分配及路径规划

Task allocation and path planning for multi-robot systems in intelligent warehousing

面对当今日益复杂的市场需求,传统的人工仓储系统已力不从心,仓储系统的智能化转型升级成为迫切需求。在这一背景下,针对仓储环境设计了一种仓储多机器人系统的任务分配与路径规划策略,以实现混杂单机与多机编队仓储任务的高效完成。文中提出一种将交通流量影响因子融入拍卖算法的仓储任务分配策略,通过预测环境中各区域机器人密度,实现任务分配的优化。该研究为多机编队任务设计了基于虚拟结构法的三机器人编队模型,并提出一种2层路径规划策略:外层基于Floyd算法进行全局路径规划,内层通过交通规则约束解决各类碰撞问题,实现局部路径规划。在MATLAB平台对设计的仓储多机器人系统进行仿真实验,实验结果表明,该多机器人系统能够灵活处理混杂2种类型的仓储任务,有效减少机器人之间的碰撞风险和机器人在密集区域的停滞现象,从而提高系统的安全性和工作效率。该研究为未来多机器人系统研究和现实应用提供参考。

Faced with today's increasingly complex market demands,traditional manual warehouse systems are becoming inadequate,necessitating the urgent intelligent transformation and upgrading of warehouse systems.In this context,this paper aims to design a task allocation and path planning strategy for a multi-robot warehouse system to efficiently accomplish mixed single-robot and multi-robot types of warehouse tasks.The study proposes a warehouse task allocation strategy that incorporates traffic flow impact factors into the auction algorithm,optimizing task allocation by predicting robot density in various areas of the environment.For multi-robot formation tasks,a three-robot formation model based on the virtual structure method is designed.Additionally,a two-layer path planning strategy is proposed:the outer layer conducts global path planning based on the Floyd algorithm,while the inner layer resolves various collision issues through traffic rule constraints,achieving local optimal path planning.Simulation experiments conducted on the MATLAB platform show that the multi-robot system can flexibly handle mixed types of warehouse tasks,effectively reducing collision risks between robots and stagnation in dense areas,thereby improving the safety and efficiency of the multi-robot system.This study provides a reference for future research and practical applications of multi-robot systems.