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

Abstract

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.