In human-robot collaborative frameworks the maximization of productivity is of paramount importance. However, it is also crucial to mitigate the cognitive workload induced on the operator during cooperation. Indeed, a high level of stress can negatively affect the human capabilities, thus compromising the performance of the working dyad. In this work, we propose a novel paradigm where the robot is enabled to adapt its behavior online to simultaneously optimize in real-time the human physiological stress and productivity. The proposed control strategy exploits a game theoretic approach to model and locally estimate the state of collaboration in terms of human productivity and stress. Based on this estimate, a learning automaton suitably adjusts the production pace of the robot, thus influencing the dynamics of the cooperation. The proposed method was tested on a realistic collaborative assembly task. The results demonstrated that the novel control strategy effectively enhances the productivity of the human-robot team, while significantly mitigating the stress induced in the operator.
|Titolo:||Human-Robot Collaboration: Optimizing Stress and Productivity Based on Game Theory|
|Data di pubblicazione:||2021|
|Appare nelle tipologie:||01.1 Articolo in Rivista|
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|RAL_Messeri_et_al_2021.pdf||Publisher’s version||Accesso riservato|