Shahar Bardugo, Daniel Koyfman, Dor Atzmon

Finding All Optimal Solutions in Multi-Agent Path Finding

Introduction

Finding all optimal solutions in multi-agent path finding. Discover algorithms for Multi-Agent Path Finding (MAPF) to find all optimal, conflict-free paths for autonomous robots & vehicles. Includes empirical evaluation.

Abstract

The Multi-Agent Path Finding problem (MAPF) aims to find conflict-free paths for a group of agents, leading each agent to its respective goal. MAPF is applicable in navigating autonomous robots and vehicles to their destination. In this paper, we study the requirement of finding all optimal solutions in MAPF. We discuss the representation of all optimal solutions, propose four algorithms for finding them, and perform an extensive empirical evaluation of the proposed algorithms.

Review

The paper addresses the Multi-Agent Path Finding (MAPF) problem, a critical challenge in robotics and autonomous systems where conflict-free paths must be found for multiple agents. While much of the existing research focuses on identifying a single optimal solution, this work distinguishes itself by investigating the less-explored, yet potentially crucial, requirement of finding *all* optimal solutions. This objective could be highly relevant in scenarios where flexibility, robustness, or alternative choices among equally optimal solutions are desired, such as dynamic environments or collaborative planning, thus representing a valuable extension to traditional MAPF solvers. The authors propose a comprehensive approach to this challenging problem. A key strength lies in their attention to the fundamental aspect of representing all optimal solutions, which is a prerequisite for any practical search strategy. Building upon this, the paper introduces no less than four distinct algorithms specifically designed to achieve this goal. The promise of an "extensive empirical evaluation" further suggests a robust investigation into the performance characteristics and practical applicability of these proposed methods, providing valuable insights into their efficacy across various problem instances and problem scales. This study addresses a significant gap in the MAPF literature by moving beyond single-optimal solutions. The ability to identify all optimal solutions has profound implications, particularly for applications requiring diverse path options, resilience to unforeseen events, or the selection of solutions based on secondary criteria not captured by the primary optimality metric. While the abstract does not detail the nature of optimality (e.g., makespan, sum-of-costs), the exploration of representation and multiple algorithms suggests a thorough theoretical and practical grounding. This work has the potential to pave the way for more sophisticated multi-agent planning systems that can leverage a broader understanding of the solution space.

Full Text

Comments

You need to be logged in to post a comment.