FICO: Finite-Horizon Closed-Loop Factorization for Unified Multi-Agent Path Finding

TL;DR

This paper introduces FICO, a novel control-based algorithm for multi-agent pathfinding that integrates planning and execution, handles uncertainties, and scales efficiently to thousands of agents in real-time.

Contribution

It presents a unified system-level framework for MAPF, introducing FICO, a receding-horizon inspired algorithm that improves scalability, responsiveness, and robustness over existing methods.

Findings

Reduces computation time by up to 100x compared to open-loop methods.

Enables real-time responses within milliseconds.

Improves throughput under stochastic delays and agent arrivals.

Abstract

Multi-Agent Path Finding is a fundamental problem in robotics and AI, yet most existing formulations treat planning and execution separately and address variants of the problem in an ad hoc manner. This paper presents a system-level framework for MAPF that integrates planning and execution, generalizes across variants, and explicitly models uncertainties. At its core is the MAPF system, a formal model that casts MAPF as a control design problem encompassing classical and uncertainty-aware formulations. To solve it, we introduce Finite-Horizon Closed-Loop Factorization (FICO), a factorization-based algorithm inspired by receding-horizon control that exploits compositional structure for efficient closed-loop operation. FICO enables real-time responses -- commencing execution within milliseconds -- while scaling to thousands of agents and adapting seamlessly to execution-time uncertainties. Extensive case studies demonstrate that it reduces computation time by up to two orders of magnitude compared with open-loop baselines, while delivering significantly higher throughput under stochastic delays and agent arrivals. These results establish a principled foundation for analyzing and advancing MAPF through system-level modeling, factorization, and closed-loop design.