Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning

TL;DR

This paper provides a comprehensive quantitative comparison of shortcutting techniques for multi-robot-arm motion planning, analyzing their effectiveness and proposing strategies to optimize performance and runtime in complex scenarios.

Contribution

It offers a detailed analysis of existing shortcutting methods for multi-arm systems and introduces simple strategies to combine them for improved performance.

Findings

Different shortcutting methods vary in effectiveness across scenarios.

Combining shortcutting techniques can optimize the tradeoff between performance and runtime.

The study provides guidelines for selecting and applying shortcutting in multi-arm planning.

Abstract

Generating high-quality motion plans for multiple robot arms is challenging due to the high dimensionality of the system and the potential for inter-arm collisions. Traditional motion planning methods often produce motions that are suboptimal in terms of smoothness and execution time for multi-arm systems. Post-processing via shortcutting is a common approach to improve motion quality for efficient and smooth execution. However, in multi-arm scenarios, optimizing one arm's motion must not introduce collisions with other arms. Although existing multi-arm planning works often use some form of shortcutting techniques, their exact methodology and impact on performance are often vaguely described. In this work, we present a comprehensive study quantitatively comparing existing shortcutting methods for multi-arm trajectories across diverse simulated scenarios. We carefully analyze the pros and cons of each shortcutting method and propose two simple strategies for combining these methods to achieve the best performance-runtime tradeoff. Video, code, and dataset are available at https://philip-huang.github.io/mr-shortcut/.