Towards Connecting Control to Perception: High-Performance Whole-Body Collision Avoidance Using Control-Compatible Obstacles

TL;DR

This paper presents a real-time collision avoidance method for robotic manipulators that integrates high-frequency control with low-frequency environmental perceptions via digital twin technology, enhancing safety and responsiveness.

Contribution

It introduces a control-compatible obstacle representation using primitive skeletons derived from digital twins, enabling real-time collision avoidance in complex environments.

Findings

Effective collision avoidance demonstrated on a 9-DOFs robot

Real-time performance achieved with millisecond reaction times

Framework successfully integrates perception and control for safety

Abstract

One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Although several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully hand-crafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and real-time compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework.