Shadoks Approach to Low-Makespan Coordinated Motion Planning

TL;DR

This paper presents the heuristics developed by the Shadoks team for the CG:SHOP 2021 challenge, focusing on multi-robot motion planning in dense grid environments to minimize makespan, achieving top performance on most instances.

Contribution

The paper introduces a set of heuristics, including initial solution strategies and the Conflict Optimizer, for effective multi-robot path planning in dense environments, leading to state-of-the-art results.

Findings

Won first place in 2021 challenge with 202 out of 203 instances solved

Achieved optimal solutions for at least 105 instances

Developed effective heuristics for dense multi-agent pathfinding

Abstract

This paper describes the heuristics used by the Shadoks 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 paper, 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.