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类工人的安全性之间一直存在权衡关系。在先前的研究中，我们提出了一种时间最优路径跟踪算法，旨在最大化速度的同时确保人类工人的安全。该算法实时运行，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距离计算不准确，尚未实现真正的最优性。为达到真正的最优性，我们需要一种方法，能够：1. 在轨迹上对大量机器人位姿进行距离计算；2. 实现实时在线机器人控制；3. 尽可能精确计算以支持最优控制。本文提出了一种基于预计算连杆局部有向距离场（SDF）的批量、快速且精确的距离检测方法。该方法利用GPU在运行时可在1毫秒内检查轨迹上500个航点的距离，适用于时间关键的机器人控制。此外，我们还提出了一种神经近似方法，将预处理速度提升至原来的2倍。最后，实验证明，在动态协作环境下，我们的方法能使六自由度机器人比基于几何基元的距离检测器更早完成导航。