Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions

Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. Among the available MAPF planners for workspaces modeled as a graph, A*-based approaches have been widely investigated and have demonstrated their efficiency in numerous scenarios. However, almost all of these A*-based approaches assume that each agent executes an action concurrently in that all agents start and stop together. This article presents a natural generalization of MAPF with asynchronous actions where agents do not necessarily start and stop concurrently. The main contribution of the work is a proposed approach called Loosely Synchronized Search (LSS) that extends A*-based MAPF planners to handle asynchronous actions. We show LSS is complete and finds an optimal solution if one exists. We also combine LSS with other existing MAPF methods that aims to trade-off optimality for computational efficiency. Extensive numerical results are presented to corroborate the performance of the proposed approaches. Finally, we also verify the applicability of our method in the Robotarium, a remotely accessible swarm robotics research platform.