Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding
🤖 AI Summary
This work addresses the challenge of task delays in multi-robot systems operating in dynamic shared environments, where interactions with uncontrollable agents—such as humans—often lead to conflicts that degrade path planning performance. Existing approaches struggle to effectively leverage environmental dynamics to improve planning quality. To overcome this limitation, the paper proposes Flow-Aware Multi-Agent Path Finding (FA-MAPF), a novel framework that explicitly integrates motion flow patterns of uncontrollable agents—extracted via machine learning—into a centralized MAPF solver for the first time. This integration enables the system to perceive and proactively respond to environmental dynamics. Experimental results demonstrate that FA-MAPF reduces conflicts by up to 55% across diverse benchmark maps and real-world human trajectory datasets, while maintaining high task completion efficiency, thereby significantly enhancing the robustness and adaptability of planned paths.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Path Finding
conflict mitigation
dynamic environments
uncontrollable agents
shared environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Flow-Aware MAPF
Multi-Agent Path Finding
Dynamic Obstacle Avoidance
Motion Pattern Learning
Conflict Mitigation
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