Anytime Planning for End-Effector Trajectory Tracking

TL;DR

This paper introduces an anytime algorithmic framework for end-effector trajectory tracking in robot manipulators, improving efficiency and refinement capabilities by guiding sampling along approximate reference paths.

Contribution

The paper presents a novel framework that makes existing graph-based trajectory tracking algorithms anytime, enhancing their practical performance in real-time scenarios.

Findings

Framework effectively guides sampling along approximate paths

Restructured algorithms show improved initial motion generation

Experimental results validate enhanced efficiency and effectiveness

Abstract

End-effector trajectory tracking algorithms find joint motions that drive robot manipulators to track reference trajectories. In practical scenarios, anytime algorithms are preferred for their ability to quickly generate initial motions and continuously refine them over time. In this paper, we present an algorithmic framework that adapts common graph-based trajectory tracking algorithms to be anytime and enhances their efficiency and effectiveness. Our key insight is to identify guide paths that approximately track the reference trajectory and strategically bias sampling toward the guide paths. We demonstrate the effectiveness of the proposed framework by restructuring two existing graph-based trajectory tracking algorithms and evaluating the updated algorithms in three experiments.