Safety Engineering of Human-Robot Collaboration Tasks Using a 3D LiDAR

Abstract

The advent of Industry 5.0 emphasizes human-centricity in autonomous systems, making safe Human-Robot Collaboration (HRC) a critical challenge. Most of the solutions to guarantee safe HRC rely on equipping robots with custom sensors and control systems, resulting in poor portability across different robotic platforms and a narrow focus on the robot's immediate surroundings, lacking broader environmental awareness. This paper describes the design and safety engineering of CogniSafe3D, a novel system that ensures safe HRC by leveraging a static 3D LiDAR sensor monitoring the collaborative environment. Unlike state-of-the-art methods, CogniSafe3D requires no sensing hardware on the robot nor on human collaborators, interfacing with the robot solely via an API to trigger safety actions and dynamic path planning. This paper reports our experience in the design of the CogniSafe3D model, following a standard-compliant safety process. While applying the process, the need to detect specific corruption in the LiDAR's point cloud data emerged as mitigation of high-risk hazards, leading to the development and evaluation of a point cloud anomaly detector, which is discussed and whose experimental details are publicly available for reproducibility.