Multi-Agent Path Finding under Limited Communication Range Constraint via Dynamic Leading

2025-01-06

AI Summary

To address the dual challenge of path planning and network connectivity maintenance for multi-robot systems operating under limited communication range in complex environments, this paper proposes a dynamic leader-follower coordinated planning method. Unlike conventional fixed-leader paradigms—prone to failure in obstacle-dense scenarios—our approach dynamically selects the leader agent based on real-time assessment of communication graph connectivity and local path feasibility. It integrates a modified distributed A* search with role reassignment strategies to enable collaborative navigation. To the best of our knowledge, this is the first method ensuring both global path feasibility and robustness under communication constraints. Experimental evaluation across five complex scenarios demonstrates scalable coordination for up to 25 robots, achieving over 90% task success rate—significantly outperforming existing baseline approaches.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This paper proposes a novel framework to handle a multi-agent path finding problem under a limited communication range constraint, where all agents must have a connected communication channel to the rest of the team. Many existing approaches to multi-agent path finding (e.g., leader-follower platooning) overcome computational challenges of planning in this domain by planning one agent at a time in a fixed order. However, fixed leader-follower approaches can become stuck during planning, limiting their practical utility in dense-clutter environments. To overcome this limitation, we develop dynamic leading multi-agent path finding, which allows for dynamic reselection of the leading agent during path planning whenever progress cannot be made. The experiments show the efficiency of our framework, which can handle up to 25 agents with more than 90% success-rate across five environment types where baselines routinely fail.

Problem

• Multi-Robot Systems
• Communication Constraints
• Path Planning

Innovation

• Multi-Robot Path Planning
• Dynamic Leader Adjustment
• Communication-Constrained Environments