提出可随时优化的末端轨迹跟踪框架，提升机器人运动规划效率。
Anytime Planning for End-Effector Trajectory Tracking
- 用引导路径指导采样，实现快速初始规划与持续优化
- 重构两种已有算法后，在三组实验中均显著提升规划效率
- 适合需要实时响应的机器人轨迹跟踪场景
末端执行器轨迹跟踪算法旨在为机械臂生成使其实现参考轨迹运动的关节动作。在实际应用中，即时算法因其能快速生成初始运动并持续优化而更受青睐。本文提出一种算法框架，将常见的基于图的轨迹跟踪算法改造为即时算法，并提升其效率与效果。核心思路是识别近似跟踪参考轨迹的引导路径，并有策略地偏向采样这些路径。通过重构两种现有基于图的轨迹跟踪算法，并在三个实验中评估改进后的算法，验证了该框架的有效性。
原文摘要 · Abstract (English)
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.
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