LAUSR

In human-robot collaboration, the mitigation of human physical workload is a crucial factor to avoid musculoskeletal disorders that might jeopardize the operator’s safety and job performance. In this work, we propose a novel, non-invasive method to estimate online the muscle fatigue experienced by the worker during the task execution. The estimation process relies on a sophisticated musculoskeletal model of the human upper body and on a 3D vision system used to track human motions in real-time. Based on this estimate, we develop a strategy that dynamically allocates the task activities to the human and to the robot with the aim of minimizing his/her muscular fatigue, thus improving the quality of the cooperation. The proposed approach has been experimentally validated in a collaborative industrial use case and compared to a static allocation strategy.