  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.
