Leveraging digital twin and dynamic scheduling for enhanced human–robot collaboration

摘要

Industry 5.0 represents a paradigm shift toward human-centric, resilient, and sustainable production systems. At the core of this transformation lies digital twins, which enable predictive and prescriptive analytics in real time, improving decision-making capabilities such as visibility, transparency, and collaboration. By integrating advanced AI algorithms for data interpretation and facilitating seamless human-machine interactions, digital twins address critical challenges in modern industrial systems. This article explores the transformative role of digital twins in operational decision-making, focusing on their ability to optimize workflows, and foster collaboration between humans and robots. Through a dual-layer methodology macro-level task scheduling for efficiency and consideration of human factors and micro-level real-time control for adaptability, digital twins offer a powerful framework for aligning human and robotic capabilities while mitigating human fatigue and improving decision transparency. Highlighting applications in digital transformation, optimization, and human-AI collaboration, this study emphasizes how digital twins enhance operational visibility and resilience. The findings contribute to the evolution of Industry 5.0, offering innovative solutions for integrating predictive models and human-centered approaches in decision-making, redefining the future of sustainable and collaborative industrial systems. • Enhancing human–robot collaboration with dynamic task scheduling. • Integrating Digital Twin for real-time control and task optimization. • Leveraging Behavior Trees for adaptive and explainable robot actions. • Reducing ergonomic risks for workers and mitigating human fatigue in Industry 5.0. • Combining AI-based scheduling and human factors for resilient workflows.