In human-robot collaboration, there has been a trade-off relationship between the speed of collaborative robots and the safety of human workers. In our previous paper, we introduced a time-optimal path tracking algorithm designed to maximize speed while ensuring safety for human workers. This algorithm runs in real-time and provides the safe and fastest control input for every cycle with respect to ISO standards. However, true optimality has not been achieved due to inaccurate distance computation resulting from conservative model simplification. To attain true optimality, we require a method that can compute distances 1. at many robot configurations to examine along a trajectory 2. in real-time for online robot control 3. as precisely as possible for optimal control. In this paper, we propose a batched, fast and precise distance checking method based on precomputed link-local SDFs. Our method can check distances for 500 waypoints along a trajectory within less than 1 millisecond using a GPU at runtime, making it suited for time-critical robotic control. Additionally, a neural approximation has been proposed to accelerate preprocessing by a factor of 2. Finally, we experimentally demonstrate that our method can navigate a 6-DoF robot earlier than a geometric-primitives-based distance checker in a dynamic and collaborative environment.

翻译：在人机协作中，协作机器人的速度与人工人员的安全性之间存在权衡关系。在上一篇论文中，我们提出了一种时间最优路径跟踪算法，旨在在确保人工人员安全的同时最大限度地提高速度。该算法能够实时运行，并根据ISO标准在每个周期内提供安全且最快的控制输入。然而，由于保守模型简化导致的距离计算不准确，未能实现真正的最优性。为达成真正最优性，我们需要一种能够满足以下要求的方法：1）沿轨迹在多个机器人构型处计算距离；2）实时在线控制机器人；3）尽可能精确以实现最优控制。本文提出了一种基于预计算连杆局部有符号距离场（SDF）的批处理式快速精确距离检测方法。该方法利用GPU在运行时能在1毫秒内检测轨迹上500个路径点的距离，适用于时间关键的机器人控制。此外，我们提出了一种神经近似方法，将预处理速度提升至原来的两倍。最后，实验表明，在动态协作环境中，我们的方法能比基于几何基元的距离检测器更早地引导六自由度机器人完成导航。