Human Comfortability: Integrating Ergonomics and Muscular-Informed Metrics for Manipulability Analysis During Human-Robot Collaboration

摘要

The ability to compute a quality index for manipulation tasks, in different configurations, has been widely used in robotics. However, it is poorly explored in human manipulation and physical human-robot collaboration (pHRC). Existing works that evaluate efficiency of human manipulation often focus only on heurisitic-based, biomechanics or ergonomics methods/tasks. Complementarity between these performance features allows for a better evaluation and more general criteria, applicable across tasks. This letter addressess this gap by generating a new metric that combines offline pre-computation of biomechanics, ergonomics, muscle assessment and joint constraints, and reducing the online time complexity, enhancing the response query time. The proposed solution allow us to build a quality distribution in the human's workspace which can be quickly tailored to specific tasks and filtered for design purposes. This method simplifies human manipulability assessment for both general and task-specific applications and, in contrast to existing works, is suitable for real-time and/or resource-limited applications. Numerical evidence shows the proposed analysis greatly outperforms previous results in terms of computing time without compromising performance.