Digital Twin-Enabled Autonomous Robotic Maintenance Frameworks
The rapid evolution of Industry 5.0 has accelerated the integration of intelligent automation, artificial intelligence (AI), Industrial Internet of Things (IIoT), and cyber-physical systems into modern manufacturing environments. One of these newly developed technologies, DT technology has recently emerged as one of the transformational paradigms in realising predictive intelligence, autonomous maintenance and online operational optimisation of robotic systems. Conventional robotic maintenance strategies, such as corrective and preventive maintenance often lead to unexpected downtimes, unnecessary resources allocation and inflated maintenance costs largely due to the nature of scheduled inspections or post-failure interventions. The maintenance frameworks enabled by Digital Twin overcome these limitations as they deterministically establish a dynamically-updated virtual representation of physical robotic assets connecting sensor networks, cloud-edge computing, AI analytics and real-time simulation. This paper proposes a full Digital Twin-based autonomous robotic maintenance framework along with data acquisition from multiple sensors, edge intelligence, machine learning (ML)-based diagnosis and prediction of failures and autonomous decision-making for predictive maintenance. The proposed framework supports continuous monitoring of health, anomaly detection, RUL prediction and adaptive maintenance scheduling with minimal operational disruptions. A comparative analysis reveals that Digital Twin-assisted maintenance not only increases fault detection precision, maintenance efficiency, system availability, and operational reliability compared to traditional practices. It further discusses the current technological challenges, research gaps, and avenues for future work relating to federated Digital Twins, explainable AI (XAI), collaborative robotics, and sustainable intelligent maintenance systems. This framework lays a strong, scalable foundation for Industry 5.0 ecosystems of next generation autonomous robotic maintenance in the smart factory.