DeepAI AI Chat
Log In Sign Up

Coordinated Motion Planning Through Randomized k-Opt

by   Paul Liu, et al.

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.


page 1

page 2

page 3

page 4


Shadoks Approach to Low-Makespan Coordinated Motion Planning

This paper describes the heuristics used by the Shadoks team for the CG:...

Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge

This report describes our winning submission to the Real Robot Challenge...

Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs

Automation applications are pushing the deployment of many high DoF mani...

Unlabeled Multi-Robot Motion Planning with Tighter Separation Bounds

We consider the unlabeled motion-planning problem of m unit-disc robots ...

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software arc...

Compacting Squares

Edge-connected configurations of squares are a common model for modular ...