Towards Industry-Inspired Use-Cases for Path Finding in Robotic Mobile Fulfillment Systems

Abstract

In recent years, the Robotic Mobile Fulfillment System has been established as a new goods-to-person storage system, which particularly addresses the needs of e-commerce. In these systems, a fleet of mobile robots carries inventory pods (mobile racks) between picking stations and storage locations, a task that requires efficient path planning for potentially hundreds of robots. Therefore, this task can be considered an instance of the Multi-Agent Path Finding problem, where the goal is to find collision-free and goal-reaching paths for a set of agents. Previous publications addressing Multi-Agent Path Finding for Robotic Mobile Fulfillment Systems use oversimplified goal-assignment schemes and use-cases. To address these issues, we present an adapted version of the Multi-Agent Path Finding problem that mimics the goal assignment scheme of real-world picking systems and we introduce three industry-inspired use-cases. Finally, using the Rolling Horizon Collision Resolution framework, we apply three state-of-the-art solvers for Multi-Agent Path Finding problems to our use-cases. Our preliminary results indicate that two of the three solvers are suitable for usage in Robotic Mobile Fulfilment systems.