When Agents Break Down in Multiagent Path Finding

TL;DR

This paper introduces a framework for dynamically adapting multiagent paths in MAPF scenarios with agent malfunctions, ensuring efficient navigation despite delays without full replanning.

Contribution

It presents protocols for local coordination and network-assisted computation that bound makespan increases after malfunctions, enhancing robustness in multiagent path finding.

Findings

Protocols limit makespan increase to k turns after k malfunctions

Network-based computation ensures robustness with limited agent capabilities

Framework enables scalable, resilient multiagent navigation

Abstract

In Multiagent Path Finding (MAPF), the goal is to compute efficient, collision-free paths for multiple agents navigating a network from their sources to targets, minimizing the schedule's makespan-the total time until all agents reach their destinations. We introduce a new variant that formally models scenarios where some agents may experience delays due to malfunctions, posing significant challenges for maintaining optimal schedules. Recomputing an entirely new schedule from scratch after each malfunction is often computationally infeasible. To address this, we propose a framework for dynamic schedule adaptation that does not rely on full replanning. Instead, we develop protocols enabling agents to locally coordinate and adjust their paths on the fly. We prove that following our primary communication protocol, the increase in makespan after k malfunctions is bounded by k additional turns, effectively limiting the impact of malfunctions on overall efficiency. Moreover, recognizing that agents may have limited computational capabilities, we also present a secondary protocol that shifts the necessary computations onto the network's nodes, ensuring robustness without requiring enhanced agent processing power. Our results demonstrate that these protocols provide a practical, scalable approach to resilient multiagent navigation in the face of agent failures.