Multi-Agent Pathfinding Under Team-Connected Communication Constraint via Adaptive Path Expansion and Dynamic Leading

TL;DR

This paper introduces a new multi-agent pathfinding framework that ensures team-connected communication using adaptive path expansion and dynamic leader reselection, outperforming existing methods in complex environments.

Contribution

The paper presents a novel two-level planning framework combining adaptive path expansion and dynamic leading to handle communication constraints more effectively.

Findings

Successfully plans for up to 25 agents in various environments.

Achieves over 90% success rate where baseline methods fail.

Handles both limited-range and line-of-sight communication constraints.

Abstract

This paper proposes a novel planning framework to handle a multi-agent pathfinding problem under team-connected communication constraint, where all agents must have a connected communication channel to the rest of the team during their entire movements. Standard multi-agent path finding approaches (e.g., priority-based search) have potential in this domain but fail when neighboring configurations at start and goal differ. Their single-expansion approach -- computing each agent's path from the start to the goal in just a single expansion -- cannot reliably handle planning under communication constraints for agents as their neighbors change during navigating. Similarly, leader-follower approaches (e.g., platooning) are effective at maintaining team communication, but fixing the leader at the outset of planning can cause planning to become stuck in dense-clutter environments, limiting their practical utility. To overcome this limitation, we propose a novel two-level multi-agent pathfinding framework that integrates two techniques: adaptive path expansion to expand agent paths to their goals in multiple stages; and dynamic leading technique that enables the reselection of the leading agent during each agent path expansion whenever progress cannot be made. Simulation experiments show the efficiency of our planners, which can handle up to 25 agents across five environment types under a limited communication range constraint and up to 11-12 agents on three environment types under line-of-sight communication constraint, exceeding 90% success-rate where baselines routinely fail.