Digital twin integration in edge-enabled robotics: a real-time framework for synchronization, simulation and maintenance

The integration of Digital Twin (DT) technology with robotics offers a powerful approach to modeling, monitoring and controlling physical robotic systems in real time. However, most DT implementations rely heavily on cloud infrastructure, which introduces latency, bandwidth limitations and dependency on continuous internet connectivity factors that are unsuitable for time-critical robotic applications. This paper proposes a novel edge-enabled digital twin integration framework specifically designed for robotics. The framework makes low-latency control, high-fidelity simulation, and real-time diagnostics possible by directly hosting the digital twin and related modules, including synchronization engines, simulation environments, and predictive maintenance analytics, on edge nodes. The suggested system architecture consists of a physical robot that is linked to a local edge node that is running a predictive maintenance module, synchronization and control engine, and DT manager. In addition to processing sensor data and enabling autonomous control and failure prediction, the edge node keeps the robot and its digital twin in real-time synchronization. Strong offline capabilities, enhanced responsiveness, and safer simulation and testing are made possible by this configuration. To illustrate the usefulness of the suggested system, application scenarios like robotic maintenance, remote diagnostics, and trajectory testing are investigated. With the help of this framework, intelligent, self-governing robotic systems that don’t require centralized cloud infrastructure can be made scalable and resilient.