Identifying human-robot interaction (HRI) incident archetypes: a system and network analysis of accidents

摘要

• Seven human-robot interaction (HRI) incident archetypes identified from 303 accident reports. • Unexpected activation and sensor errors dominate HRI accident patterns across technology stages. • System thinking reveals complex interactions between robot design, human, and robot errors. • Hazard-error network analysis shows key risks linked to robot components and activation states. • Incident archetypes provide structured tools for targeted safety management in industrial HRI. Due to the significant advantages of industrial robots in production, they are increasingly used in the workplace, resulting in attention being paid to the safety issues of human-robot interaction (HRI). However, existing research still has gaps in understanding the patterns of relationships between robot characteristics, robot-human errors, and the physical working environment. To address the knowledge gaps, this paper aims to identify and examine the patterns of the relationship among robot characteristics, robot-human errors, and physical working environments and investigate how the patterns evolve along the technological advances in robotics design. This paper analyses 303 HRI accident reports by applying a network analysis. Based on the cluster analysis, seven HRI incident archetypes were identified, including (1) unexpected activation, (2) faulty commands, (3) blind automation danger, (4) sensor and signal communication errors, (5) ergonomics-related injuries, (6) secondary robot intrusion, and (7) classic hazard pitfalls in robot-assisted work. Temporal analysis reveals that ’Archetype 1: unexpected activation’ consistently dominated, accounting for over 60 % of accidents, and warrants the most attention in future safety management. Additionally, the increasing frequency of ’Archetype 4: sensor and signal communication errors’ in later stages highlights the growing need for targeted interventions“. This paper is the first to identify and categorize HRI incident archetypes systematically. It offers a useful framework for researchers and practitioners. These archetypes provide a structured tool for systematically investigating and diagnosing incidents and can also help workers and managers understand the patterns of relationships between these factors in different HRI scenarios.