Expansion-GRR: Efficient Generation of Smooth Global Redundancy Resolution Roadmaps

TL;DR

This paper introduces Expansion-GRR, a fast and smooth global redundancy resolution roadmap method for robotics, improving efficiency and path quality for applications like teleoperation and motion planning.

Contribution

We propose a novel Expansion-GRR approach that significantly accelerates roadmap generation and enhances path smoothness using efficient projections and multi-seed strategies.

Findings

Generation speed increased up to 100 times

Paths achieved higher smoothness

Outperformed prior methods in teleoperation success rate

Abstract

Global redundancy resolution (GRR) roadmaps is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent path planning, and motion primitives generation. However, previous methods to compute GRR roadmaps often necessitate a lengthy computation time and produce non-smooth paths, limiting their practical efficacy. To address this challenge, we introduce a novel method Expansion-GRR that leverages efficient configuration space projections and enables rapid generation of smooth roadmaps that satisfy the task constraints. Additionally, we propose a simple multi-seed strategy that further enhances the final quality. We conducted experiments in simulation with a 5-link planar manipulator and a Kinova arm. We were able to generate the Expansion-GRR roadmaps up to 2 orders of magnitude faster while achieving higher smoothness. We also demonstrate the utility of the GRR roadmaps in teleoperation tasks where our method outperformed prior methods and reactive IK solvers in terms of success rate and solution quality.