Network Digital Twin for 5G-Enabled Mobile Robots

TL;DR

This paper introduces a Network Digital Twin framework that leverages real-time robotic data to monitor and optimize 5G-enabled mobile robot operations, improving reliability and energy efficiency in dynamic environments.

Contribution

It presents a novel real-time data-driven NDT framework specifically designed for 5G-enabled mobile robots, integrating robotic traces for enhanced network-aware robotic navigation.

Findings

The NDT effectively monitors network conditions in real-time.

The framework improves energy efficiency in robotic operations.

It enhances reliability of robot navigation in dynamic environments.

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.