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Item type:Publication, Access status: with files , Bewertung von Multi-Agent Path Finding Verfahren für deren Einsatz in robotisierten Logistiksystemen(Wissenschaftliche Gesellschaft für Technische Logistik e.V. (WGTL), 2022-09-21); ; ; ; In recent time, logistics systems have been in place in which hundreds of autonomous mobile robots are used for tasks such as picking or sorting. Path planning for the robots navigating in these systems is still a relevant problem, for which Multi-Agent Path Finding (MAPF) methods have been considered for some time. However, there has been no consensus on which evaluation criteria allow for estimating the suitability of a MAPF method for a particular application. In this work, we therefore derive qualitative evaluation criteria (scalability, conflict management, and solution quality), that enable such a suitability assessment. We use these criteria to find that Priority Based Search is suitable for Robotic Mobile Fulfillment systems, while Explicit Estimation Conflict Based Search is appropriate in robotic sorting systems. For robotic-based production logistics, we recommend the Conflict Based Search algorithm.Publicationtype: Conference Paper141 151 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards Industry-Inspired Use-Cases for Path Finding in Robotic Mobile Fulfillment SystemsIn 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.Publicationtype: Conference Paper78