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Industry 5.0-Oriented Human-Centric Robotic Manufacturing Systems

2024 · International Journal of Intelligent Automation & Robotics Engineering · Vol 7, pp. 01-09 · 0 citations

TL;DR

This study presents a comprehensive research framework for Industry 5.0-oriented human-centric robotic manufacturing systems by integrating collaborative robotics, AI-assisted decision-making, adaptive sensing, real-time monitoring, and intelligent manufacturing analytics.

Abstract

Industry 5.0 represents a paradigm shift from fully automated production toward intelligent, human-centric manufacturing environments where collaborative robots, artificial intelligence (AI), edge computing, digital twins, Industrial Internet of Things (IIoT), and cyber-physical systems (CPS) operate in harmony with human workers. Unlike Industry 4.0, which primarily emphasized automation and productivity, Industry 5.0 focuses on resilience, sustainability, worker well-being, and personalized manufacturing. This study presents a comprehensive research framework for Industry 5.0-oriented human-centric robotic manufacturing systems by integrating collaborative robotics, AI-assisted decision-making, adaptive sensing, real-time monitoring, and intelligent manufacturing analytics. The proposed framework enables seamless interaction between human operators and robotic systems while ensuring operational safety, productivity, flexibility, and energy efficiency. A detailed literature review identifies current advancements, research gaps, and technological challenges associated with human-robot collaboration. The research methodology introduces an intelligent architecture incorporating multi-modal sensing, AI-based decision support, digital twin simulation, and adaptive robotic control. Comparative performance metrics demonstrate improvements in production efficiency, safety compliance, response time, and system adaptability. The findings indicate that Industry 5.0 technologies significantly enhance manufacturing performance while promoting sustainable and worker-centered industrial environments. The study concludes with future research directions involving explainable artificial intelligence, federated learning, autonomous collaborative robots, and resilient cyber-physical manufacturing ecosystems.

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