An ergonomic role allocation framework for dynamic human–robot collaborative tasks

By incorporating ergonomics principles into the task allocation processes, human–robot collaboration (HRC) frameworks can favour prevention of work-related musculoskeletal disorders (WMSDs). In this context, existing offline methodologies do not account for variability human actions and states; therefore, planning dynamically assigning roles in teams remains an unaddressed challenge. This study aims to create ergonomic role framework that optimises HRC, taking features state measurements. The presented consists two main modules: first provides HRC model, exploiting AND/OR Graphs (AOG)s, which we adapted solve problem; second module describes risk assessment during execution through a indicator updates AOG-related variables influence future allocation. proposed be combined with any time-varying evaluates cognitive physical burden. work, tested our assembly scenario, introducing index named Kinematic Wear. overall has been multi-subject experiment. results subjective evaluations, measured questionnaires, show high-risk are correctly recognised assigned humans, reducing fatigue frustration collaborative tasks.