Edge-Computing Framework for Human-Robot Collaboration in Industry 5.0: Enhancing Operator Well-Being and Efficiency in Manufacturing

Abstract

Industry 5.0 emphasizes human-centric automation, integrating advanced robotics and artificial intelligence to enhance operator well-being and manufacturing efficiency. This paper presents an edge-computing framework for human-robot collaboration, incorporating real-time physiological monitoring, machine learning-based fatigue prediction, and adaptive decision-making. The proposed system consists of two core modules: a fatigue monitor that continuously evaluates operator conditions and a decision-making system that dynamically adjusts task distribution. The framework optimises workload balancing by leveraging 10T-enabled sensors, artificial vision, and fuzzy logic-based decision-making while ensuring safety and productivity. Experimental validation in an industrial setting demonstrates significant improvements in reducing operator fatigue and enhancing production efficiency. Future research will focus on refining fatigue prediction models and expanding the system's applicability across various industrial domains.