Real-World Deployment of a 5G-Connected Edge-Controlled Aerial Robot in Industrial Subterranean Mines

2026-06-03

AI Summary

This work addresses the challenge of achieving high-reliability, low-latency autonomous drone flight in real-world industrial underground mines by proposing a closed-loop control architecture that integrates 5G New Radio Standalone (NR SA) with Kubernetes-based edge computing. The system deploys a Model Predictive Controller (MPC) on an edge cluster and incorporates a human-in-the-loop mechanism to enable smooth, collision-free semi-autonomous navigation. To the best of our knowledge, this is the first demonstration of 5G-connected, edge-controlled closed-loop drone flight in an active mining environment. The implementation successfully bridges the gap between laboratory research and real-world deployment, validating the feasibility and efficiency of the proposed architecture in time-sensitive, safety-critical scenarios.

Abstract

This article presents the first real-world autonomous flight of a 5G-connected aerial robot controlled by an edge-offloaded controller, and aims to bridge the gap between controlled and factual setups. The robot operates within an active industrial subterranean mine, while the high-level controller is deployed in a nearby Kubernetes-based edge cluster. Communication between the robot and the edge is enabled via a 5G New Radio (NR) Standalone (SA) network. The chosen controller is a Model Predictive Controller (MPC), which generates control actions to allow the robot to navigate seamlessly through the mining environment. A human operator selects waypoints for the aerial robot, and the MPC generates smooth, collision-free paths for autonomous executions. The proposed 5G edge-based closed-loop system is evaluated in a real industrial setting and demonstrates the potential of edge-controlled robotic systems toward time-critical, safe and efficient future deployments.

Problem

• 5G
• edge computing
• aerial robot
• industrial subterranean mines
• autonomous flight

Innovation

• 5G edge computing
• Model Predictive Control
• autonomous aerial robot
• subterranean mining
• Kubernetes-based edge cluster