A Multi-Label A* Algorithm for Multi-Agent Pathfinding
Given a set of agents, the multi-agent pathfinding problem consists in determining, for each agent, a path from its start location to its assigned goal while avoiding collisions with other agents. Recent work has studied variants of the problem in which agents are assigned a sequence of goals (tasks) that become available over time, such as the online multi-agent pickup and delivery (MAPD) problem. In this paper, we propose a multi-label A* algorithm (MLA*) for this problem. It extends the classic A* algorithm by allowing the computation of paths with multiple ordered goals (such as a pickup and delivery). Moreover, we develop a new h-value-based centralized heuristic for the MAPD. Computational experiments show that our proposed MLA* obtains substantial improvements in terms of makespan and service time as compared to existing methods, while being more computationally efficient. On instances with a thousand tasks and hundreds of agents, our method reduces the average service time by 43% compared to the state of the art, with considerably less computational effort.