Influence of task decision autonomy on physical ergonomics and robot performances in an industrial human–robot collaboration scenario - Pantano, Matteo; Yang, Qiaoyue; Blumberg, Adrian; Reisch, Raven; Hauser, Tobias; Lutz, Benjamin; Regulin, Daniel; Kamps, Tobias; Traganos, Konstantinos; Lee, Dongheui (Frontiers in Robotics and AI, 2022)
Adoption of human-robot collaboration is hindered by barriers in collaborative task design. A new approach for solving these problems is to empower operators in the design of their tasks. However, how this approach may affect user welfare or performance in industrial scenarios has not yet been studied. Therefore, in this paper, the results of an experiment designed to identify the influences of the operator's self-designed task on physical ergonomics and task performance are presented. At first, a collaborative framework able to accept operator task definition via parts’ locations and monitor the operator's posture is presented. Second, the framework is used to tailor a collaborative experience favoring decision autonomy using the SHOP4CF architecture. Finally, the framework is used to investigate how this personalization influences collaboration through a user study with untrained personnel on physical ergonomics. The results from this study are twofold. On one hand, a high degree of decision autonomy was felt by the operators when they were allowed to allocate the parts. On the other hand, high decision autonomy was not found to vary task efficiency nor the MSD risk level. Therefore, this study emphasizes that allowing operators to choose the position of the parts may help task acceptance and does not vary operators' physical ergonomics or task efficiency. Unfortunately, the test was limited to 16 participants and the measured risk level was medium. Therefore, this study stresses also that operators should be allowed to choose their own work parameters, but some guidelines should be followed to further reduce MSD risk levels.