Marker-Based Safety Functionality for Human–Robot Collaboration Tasks by Means of Eye-Tracking Glasses

Abstract

Human–robot collaboration (HRC) remains an increasingly growing trend in the robotics research field. Despite the widespread usage of collaborative robots on the market, several safety issues still need to be addressed to develop industry-ready applications exploiting the full potential of the technology. This paper focuses on hand-guiding applications, proposing an approach based on a wearable device to reduce the risk related to operator fatigue or distraction. The methodology aims at ensuring operator’s attention during the hand guidance of a robot end effector in order to avoid injuries. This goal is achieved by detecting a region of interest (ROI) and checking that the gaze of the operator is kept within this area by means of a pair of eye-tracking glasses (Pupil Labs Neon, Berlin, Germany). The detection of the ROI is obtained primarily by the tracking camera of the glasses, acquiring the position of predefined ArUco markers, thus obtaining the corresponding contour area. In case of the misdetection of one or more markers, their position is estimated through the optical flow methodology. The performance of the proposed system is initially assessed with a motorized test bench simulating the rotation of operator’s head in a repeatable way and then in an HRC scenario used as case study. The tests show that the system can effectively identify a planar ROI in the context of a HRC application in real time.