对比多机械臂运动规划中的捷径优化方法，提升路径质量与效率。
Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning
- 系统评估多种捷径优化技术在多臂场景下的表现
- 提出两种组合策略，在性能与速度间取得更好平衡
- 适用于需要高效平滑多臂运动的工业自动化场景
为多机械臂系统生成高质量运动规划面临高维状态空间和臂间碰撞风险的挑战。传统规划方法常产生平滑性差、执行时间长的轨迹。通过捷径优化进行后处理是提升运动质量的常用手段，但在多臂场景中，优化单臂路径时不能引发与其他臂的碰撞。尽管现有研究普遍采用某种形式的捷径技术，其具体方法与实际效果常描述模糊。本文首次对多臂轨迹的现有捷径方法进行量化比较，涵盖多种模拟场景，深入分析各方法的优劣，并提出两种简单有效的组合策略，以实现最佳性能-运行时间权衡。相关视频、代码与数据集已公开于https://philip-huang.github.io/mr-shortcut/。
原文摘要 · Abstract (English)
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/.
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