Shadoks Approach to Low-Makespan Coordinated Motion Planning

Abstract

This paper 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 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.

On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning

“…Shadoks Approach to Low-Makespan: The Shadoks approach [35] utilized a few algorithms considering a coordinated motion planning issue in the two-dimensional grid Z 2 . The objective is to transport a collection of robots, a set of n labeled unit squares, between the specified start and target grid cells without causing any collisions.…”

Conflict Optimization for Binary CSP Applied to Minimum Partition into Plane Subgraphs and Graph Coloring

“…The three top-ranking teams (Lasa, Gitastrophe, and Shadoks) on the CG:SHOP 2022 challenge all used a common approach called conflict optimization [7,26,3] while the fourth team used a SAT-Boosted Tabu Search [25]. Conflict optimization is a technique used by Shadoks to obtain the first place in the CG:SHOP 2021 challenge for low-makespan coordinated motion planning [4], and the main ideas of the technique lent themselves well to the 2022 challenge. Next, we describe the conflict optimizer as a metaheuristic to solve constraint satisfaction problems (CSP) [29].…”