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

Citation
27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2022)
Contribution to Conference
27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022  
Publisher DOI
10.1109/ETFA52439.2022.9921501
Scopus ID
2-s2.0-85141366265
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.
Subjects
Multi-Agent Path Finding
Path Planning
Robotic Mobile Fulfillment Systems
Rolling Horizon Collision Resolution