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Journal Articles ACM Journal of Experimental Algorithmics Year : 2022

Shadoks Approach to Low-Makespan Coordinated Motion Planning

Abstract

This article describes the heuristics used by the Shadoks 1 team for the CG:SHOP 2021 challenge. This year’s problem is to coordinate the motion of multiple robots in order to reach their targets without collisions and minimizing the makespan. It is a classical multi-agent path finding problem with the specificity that the instances are highly dense in an unbounded grid. Using the heuristics outlined in this article, our team won first place with the best solution to 202 out of 203 instances and optimal solutions to at least 105 of them. The main ingredients include several different strategies to compute initial solutions coupled with a heuristic called Conflict Optimizer to reduce the makespan of existing solutions.
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Dates and versions

hal-04051506 , version 1 (30-03-2023)

Identifiers

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Loïc Crombez, Guilherme D da Fonseca, Yan Gerard, Aldo Gonzalez-Lorenzo, Pascal Lafourcade, et al.. Shadoks Approach to Low-Makespan Coordinated Motion Planning. ACM Journal of Experimental Algorithmics, 2022, 27, pp.1-17. ⟨10.1145/3524133⟩. ⟨hal-04051506⟩
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