Real-time order dispatching for a fleet of autonomous mobile robots using multi-agent reinforcement learning

Abstract Autonomous mobile robots (AMRs) are increasingly being used to enable efficient material flow in dynamic production environments. Dispatching transport orders in such environments is difficult due to the complexity arising from the rapid changes in the environment as well as due to a tight coupling between dispatching, path planning, and route execution. For order dispatching, an approach is proposed that uses multi-agent reinforcement learning, where AMR agents learn to bid on orders based on their individual observations. The approach is investigated in a robot simulation environment. The results show a more efficient order allocation compared to commonly used dispatching rules.