Stochastic Decision-Making Framework for Human-Robot Collaboration in Industrial Applications

TL;DR

This paper introduces a stochastic decision-making framework for human-robot collaboration in industrial settings, enabling robots to adapt to human factors like motivation and aggression for safer, more efficient teamwork.

Contribution

It presents a novel probabilistic approach for robots to reason about human emotions and intentions, enhancing collaboration safety and effectiveness.

Findings

Simulation results demonstrate improved robot adaptability.

Probabilistic models predict human actions with higher accuracy.

Framework supports real-time decision-making in HRC environments.

Abstract

Collaborative robots, or cobots, are increasingly integrated into various industrial and service settings to work efficiently and safely alongside humans. However, for effective human-robot collaboration, robots must reason based on human factors such as motivation level and aggression level. This paper proposes an approach for decision-making in human-robot collaborative (HRC) environments utilizing stochastic modeling. By leveraging probabilistic models and control strategies, the proposed method aims to anticipate human actions and emotions, enabling cobots to adapt their behavior accordingly. So far, most of the research has been done to detect the intentions of human co-workers. This paper discusses the theoretical framework, implementation strategies, simulation results, and potential applications of the bilateral collaboration approach for safety and efficiency in collaborative robotics.