Vision-Based Safety System for Barrierless Human-Robot Collaboration

TL;DR

This paper presents a vision-based safety system for collaborative robots that detects human operators in 3D space, enabling dynamic speed adjustments and safety zone compliance to prevent accidents.

Contribution

It introduces a deep learning-based vision system integrated with robot control to enhance safety in barrierless human-robot collaboration environments.

Findings

The system accurately detects and classifies operator zones.

Reaction times meet safety standards.

Multiple operation modes effectively ensure safety.

Abstract

Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.