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In this paper we consider multiple Automated Guided Vehicles (AGVs)navigating a common workspace to fulfill various intralogistics tasks,typically formulated as the Multi-Agent Path Finding (MAPF) problem. To keepplan execution deadlock-free, one approach is to construct an Action DependencyGraph (ADG) which encodes the ordering of AGVs as they proceed along theirroutes. Using this method, delayed AGVs occasionally require others to wait forthem at intersections, thereby affecting the plan execution efficiency. If theworkspace is shared by dynamic obstacles such as humans or third party robots,AGVs can experience large delays. A common mitigation approach is to re-solvethe MAPF using the current, delayed AGV positions. However, solving the MAPF istime-consuming, making this approach inefficient, especially for large AGVteams. In this work, we present an online method to repeatedly modify a givenacyclic ADG to minimize route completion times of each AGV. Our approachpersistently maintains an acyclic ADG, necessary for deadlock-free planexecution. We evaluate the approach by considering simulations with randomdisturbances on the execution and show faster route completion times comparedto the baseline ADG-based execution management approach.

A Feedback Scheme to Reorder a Multi-Agent Execution Schedule by Persistently Optimizing a Switchable Action Dependency Graph