Human-Robot Collision Avoidance Scheme for Industrial Settings Based on Injury Classification

Abstract

The objective of this paper is to develop a real-time, depth-sensing surveillance method to be used in factories that require human operators to complete tasks alongside collaborative robots. Traditionally, collision detection and analysis have been achieved with extra sensors that are attached to the robot to detect torque or current. In this study, a novel method using 3D object detection and raw 3D point cloud data is proposed to ensure safety by deriving the change in distance between humans and robots from depth maps. By not having to deal with any potential delay associated with extra sensor-based data, both the likelihood and severity of collaborative robot-induced injuries are expected to decrease.