Daniel Koyfman, Dor Atzmon, Shahaf Shperberg, Ariel Felner

Minimizing Fuel in Multi-Agent Pathfinding

Introduction

Minimizing fuel in multi-agent pathfinding. Minimize "Fuel" cost in Multi-Agent Pathfinding (MAPF) with novel A*-based and CBS-based algorithms. This paper analyzes Fuel's complexity and compares algorithm performance.

Abstract

The multi-agent pathfinding problem (MAPF) of finding conflict-free paths for multiple agents has attracted a large number of researchers in the past. The cost of the solution is commonly measured by the sum-of-costs (SOC) cost function or, less commonly, by Makespan. In this paper, we focus on the Fuel cost function, which is the number of physical steps the agents traverse. While Fuel was mentioned in many previous papers, our paper is the first to deepen into it. We introduce an A*-based algorithm and a CBS-based algorithm for Fuel. We study Fuel theoretically, showing that it can be (perhaps non-intuitively) more complex than SOC. Finally, we experimentally compare both algorithms against each other and against their SOC counter parts, studying their advantages and disadvantages.

Review

The paper "Minimizing Fuel in Multi-Agent Pathfinding" presents a compelling and timely contribution to the Multi-Agent Pathfinding (MAPF) domain by systematically exploring the "Fuel" cost function. While MAPF solutions traditionally prioritize sum-of-costs (SOC) or Makespan, this work uniquely focuses on Fuel, defined as the total physical steps taken by agents. The authors position this as the first dedicated investigation into Fuel, despite its previous mentions, thereby addressing a significant gap in the literature. This specialized focus promises substantial practical relevance, particularly in applications where energy efficiency, physical wear, or overall movement effort are critical considerations. Methodologically, the paper introduces two distinct algorithmic approaches specifically designed for Fuel minimization: an A*-based algorithm and a Conflict-Based Search (CBS)-based algorithm. This dual strategy suggests a robust attempt to address the problem across different computational paradigms. A notable theoretical contribution is also highlighted, asserting that Fuel can be "perhaps non-intuitively" more complex than SOC, a claim that challenges existing intuitions and warrants deeper examination. The proposed algorithms are then subjected to experimental validation, comparing their performance both against each other and against their SOC-optimized counterparts, providing insights into their respective advantages and disadvantages. In conclusion, this paper offers a valuable and comprehensive examination of the "Fuel" cost function within MAPF. The integration of novel algorithmic solutions, rigorous theoretical analysis, and thorough experimental evaluation forms a strong basis for future research in this area. The potential for Fuel to be more complex than conventionally studied metrics like SOC is particularly intriguing and could profoundly influence the design of future MAPF algorithms. This work has the potential to significantly impact real-world deployments of multi-agent systems where minimizing actual physical movement is a paramount objective, making it a significant addition to the field.

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