Network Digital Twin for 5G-Enabled Mobile Robots

2025-02-04

AI Summary

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.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The maturity and commercial roll-out of 5G networks and its deployment for private networks makes 5G a key enabler for various vertical industries and applications, including robotics. Providing ultra-low latency, high data rates, and ubiquitous coverage and wireless connectivity, 5G fully unlocks the potential of robot autonomy and boosts emerging robotic applications, particularly in the domain of autonomous mobile robots. Ensuring seamless, efficient, and reliable navigation and operation of robots within a 5G network requires a clear understanding of the expected network quality in the deployment environment. However, obtaining real-time insights into network conditions, particularly in highly dynamic environments, presents a significant and practical challenge. In this paper, we present a novel framework for building a Network Digital Twin (NDT) using real-time data collected by robots. This framework provides a comprehensive solution for monitoring, controlling, and optimizing robotic operations in dynamic network environments. We develop a pipeline integrating robotic data into the NDT, demonstrating its evolution with real-world robotic traces. We evaluate its performances in radio-aware navigation use case, highlighting its potential to enhance energy efficiency and reliability for 5Genabled robotic operations.

Problem

• Real-time network condition insights
• Seamless robot navigation in 5G
• Energy-efficient 5G robotic operations

Innovation

• Network Digital Twin framework
• Real-time robotic data integration
• Radio-aware navigation optimization