Digital Twin based Test- and Verify Framework of Human-Robot Collaboration Solutions

Abstract

While human-robot collaboration is emerging as a new paradigm for solving problems in the industrial domain, the lack of suitable testing methods has led to insufficient design feedback and unconvincing safety certifications during deployment, which impede its long-term development. These include insufficient design feedback and unconvincing safety certifications during deployment. Adhering to a human-centric principle, this work analyzes deficiencies in existing standards and testing methodologies for human-robot collaboration testing. By integrating digital twin and AI technologies, a simulation-based testing framework is proposed for human-robot collaboration. AI-generated human factors are introduced in the simulation environment to emulate capabilities of the human-robot collaboration solutions under evaluation across diverse scenarios. This explores corner cases while mitigating equipment and personnel risks inherent in physical testing workflows. Through the exploration of corner cases, this research has discovered that developing human-robot collaboration solutions requires greater attention to human factors, rather than primarily considering electromechanical devices, to ensure operator safety during real-world deployment of human-robot collaboration schemes.