Real-time Batched Distance Computation for Time-Optimal Safe Path Tracking

TL;DR

This paper presents a GPU-accelerated, batched distance computation method using precomputed SDFs for real-time, precise, and efficient safe path tracking in human-robot collaboration, improving over conservative models.

Contribution

It introduces a novel batched distance checking approach with neural acceleration, enabling real-time, accurate distance evaluation for multiple configurations along a robot trajectory.

Findings

Distance checks for 500 waypoints in under 1 ms using GPU

Neural approximation accelerates preprocessing by 2x

Method outperforms geometric-primitives-based checkers in dynamic environments

Abstract

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.