Digital twin-based biofeedback controlling of human-cobot interaction upon a manufacturing application

Cobots (collaborative robots) are widely exploited in the manufacturing industry as smart assistants in proximity to human operators. The race towards mass automation brought by the fourth industrial revolution has made the safety of humans a widely discussed topic. Industrial guidelines have been introduced to accommodate this change in the manufacturing industry for better use of cobots without compromising human safety. Built-in safety is encouraged to be incorporated from the cobot programming stage itself to facilitate this safe collaborative environment. To achieve that, research is being done to train the cobots with various contact avoidance algorithms. Mitigating productivity loss while the cobots are in these trained safe operating modes, has been identified as a requirement by the researchers to take real advantage of collaborative workspaces. To address this requirement, the authors are proposing a novel cobot-controlling algorithm for human-cobot interaction by considering the biofeedback of the human operator. The proposed algorithm is part of a model workcell development which will be remotely controlled using a digital twin platform.