A connective framework to minimize the anxiety of collaborative Cyber-Physical System

The role of Cyber-Physical systems (CPS) is well recognized in the context of Industry 4.0, which consists of human operators working with machines/robots. The interactions among them can be quite demanding in terms of cognitive resources. Existing systems do not yet consider the psychological aspects of safety in the domain. This lack can lead to hazardous situations, thus compromising the performance of the working system. This work proposes a connective decision-making framework for a flexible CPS, which can quickly respond to dynamic changes and be resilient to emergent hazards. First, Anxiety is defined and categorized for expected/unforeseen situations that a CPS could encounter through historical data using the Ishikawa method. Second, visual cues are used to gather the CPS's current state (such as human pose and object identification). Third, a mathematical model is developed using Mixed-integer programming (MIP) to allocate optimal resources, to tackle high-impact situations generating Anxiety. Finally, the logic is designed for an effective counter-mechanism to mitigate Anxiety. The proposed method was tested on a realistic industrial scenario incorporating a collaborative CPS. The results demonstrated that the proposed method improves the decision-making of a CPS facing a complex scenario, ensures physical safety, and effectively enhances the human-machine team's productivity.