Coordinated Motion Planning Through Randomized k-Opt

TL;DR

This paper presents a novel coordinated motion planning method using randomized k-opt local search, successfully applied to a robotics challenge to optimize robot movement sequences.

Contribution

It introduces a combined initialization and k-opt local search approach for coordinated motion planning, achieving top results in a competitive challenge.

Findings

First place in distance minimization category

Third place in makespan minimization category

Effective combination of initialization and local search methods

Abstract

This paper examines the approach taken by team gitastrophe in the CG:SHOP 2021 challenge. The challenge was to find a sequence of simultaneous moves of square robots between two given configurations that minimized either total distance travelled or makespan (total time). Our winning approach has two main components: an initialization phase that finds a good initial solution, and a $k$-opt local search phase which optimizes this solution. This led to a first place finish in the distance category and a third place finish in the makespan category.