摘要
Abstract
This study focuses on the task allocation methods for multi-robot systems in intelligent warehousing.Considering the large quantity of logistics tasks and the dynamic nature of real-time task additions in intelligent warehousing,a novel approach is proposed.It introduces an overall allocation method that dynamically inserts tasks based on current conditions.Within a certain time threshold,a lo-gistics task set is planned using an adaptive multi-layer encoding genetic algorithm that considers compre-hensive time cost,path cost,idle cost,and task balance cost.This method allocates logistics task se-quences and execution orders for each robot in the intelligent warehousing system.During task execution,each robot evaluates its remaining task cost and the path cost of inserting real-time tasks.Without affect-ing the total completion time of current tasks,robots compete to decide whether to insert real-time logis-tics tasks generated within the next time threshold into the current task sequence,enabling real-time task insertion.Tasks not inserted in real-time wait for the next overall allocation.Experimental results dem-onstrate that this method significantly reduces idle waiting time for robots,improves system efficiency,and reduces overall operation time and distance traveled.关键词
智能仓储系统/机器人集群/动态任务分配/自适应多层编码遗传算法/实时插入Key words
intelligent storage/robot cluster/real-time task/dynamic task allocation/adaptive mul-tilevel coding genetic algorithm/real-time insertion分类
信息技术与安全科学
