Perception and Computation for Speed and Separation Monitoring Architectures

Abstract

Human–Robot Collaboration (HRC) has been a significant research topic within the Industry 4.0 movement over the past decade. The interest in HRC research has continued on with the dawn of Industry 5.0 focusing on worker experience. Within the study of HRC, the collaboration approach of Speed and Separation Monitoring (SSM) has been implemented through various architectures. The different configuration strategies involve different perception-sensing modalities, mounting strategies, data filtration, computational platforms, and calibration methods. This paper explores the evolution of the perception architectures used to perform SSM, and highlights innovations in sensing and processing technologies that can open up the door to significant advancements in this sector of HRC research.