Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding

TL;DR

This paper introduces FA-MAPF, a framework that improves multi-robot path planning in shared environments by incorporating learned motion patterns of uncontrollable agents, significantly reducing conflicts and maintaining efficiency.

Contribution

The paper presents a novel flow-aware MAPF framework that leverages environmental motion patterns, enhancing conflict mitigation in dynamic shared environments.

Findings

Reduces conflicts with uncontrollable agents by up to 55%.

Maintains task efficiency despite conflict reduction.

Effective on both simulated and real-world data.

Abstract

Deploying multi-robot systems in environments shared with dynamic and uncontrollable agents presents significant challenges, especially for large robot fleets. In such environments, individual robot operations can be delayed due to unforeseen conflicts with uncontrollable agents. While existing research primarily focuses on preserving the completeness of Multi-Agent Path Finding (MAPF) solutions considering delays, there is limited emphasis on utilizing additional environmental information to enhance solution quality in the presence of other dynamic agents. To this end, we propose Flow-Aware Multi-Agent Path Finding (FA-MAPF), a novel framework that integrates learned motion patterns of uncontrollable agents into centralized MAPF algorithms. Our evaluation, conducted on a diverse set of benchmark maps with simulated uncontrollable agents and on a real-world map with recorded human trajectories, demonstrates the effectiveness of FA-MAPF compared to state-of-the-art baselines. The experimental results show that FA-MAPF can consistently reduce conflicts with uncontrollable agents, up to 55%, without compromising task efficiency.