A Practical Evaluation of Multi-Agent Pathfinding in Automated Warehouse

Multi-agent pathfinding (MAPF) has drawn more attention with the increasing demand for deploying multi-robot applications in industry. Warehouse automation is one particular application of MAPF that is led by global logistics companies. In this application, a fleet of robots simultaneously navigates to their goal locations without collisions among themselves. The key purpose is to optimize operation efficiency in terms of throughput and operation costs. An increasing number of robots initially leads to higher throughput, but inefficiency in path-planning becomes unavoidable due to the dense robot population. In this work, we suggest a novel evaluation metric for automated warehouse applications, called a multi-agent efficiency factor. This metric attempts to quantify the efficiency of multi-robot operations in terms of time or energy consumption in congested environments. We simulate the lifelong version of MAPF in several environments using CCBS-PGA, a highly adaptive MAPF algorithm. Then we evaluate the efficiency of the multi-robot operations using the proposed factor, together with the throughput per agent. Our experiments demonstrate the effectiveness of the multi-agent efficiency factor as an evaluation metric for lifelong MAPF. Finally, we discuss the importance of agent density in designing multi-robot applications.