Network Digital Twin for 5G-Enabled Mobile Robots
To address the challenge of achieving seamless, efficient, and reliable navigation and operation of mobile robots in dynamic environments under 5G networks, this paper proposes a robot-oriented Network Digital Twin (NDT) framework. We pioneer the integration of robots as mobile sensing nodes, synergistically combining 5G channel measurements, SLAM-based localization, temporal data fusion, and radio-aware navigation algorithms to establish a data-driven, dynamically evolving NDT mechanism that enables closed-loop network–robot collaborative decision-making. Validated on real-world robot trajectories, the framework demonstrates significant improvements: 18.7% reduction in navigation energy consumption and 92% decrease in communication outages—thereby enhancing task reliability. This work establishes a scalable, digital twin–enabled paradigm for 5G-powered autonomous robotics.