ML-Based UAV Routing with Dynamic Geofencing Using 5G NEF and CAMARA APIs

Abstract

In dense urban environments, traditional GNSS-based navigation for Unmanned Aerial Vehicles (UAVs) suffers from multipath interference and signal obstructions, compromising positioning accuracy and increasing risks of collisions and airspace violations. This paper proposes a novel machine learning-based system architecture for autonomous UAV parcel delivery, leveraging standardized 5G Network Exposure Function (NEF) and CAMARA Device Location API to achieve sub-meter location precision. Our approach integrates dynamic geofencing and predictive rerouting at the network edge, powered by a Random Forest-based collision prediction model that proactively adjusts UAV trajectories to avoid restricted zones in real time. Through simulations of six UAVs navigating dynamically updated no-fly zones, we demonstrate that our system significantly reduces time spent in restricted areas to near zero, compared to GNSS-only and rule-based methods, while limiting path-length inflation to approximately 30% for five of six flights. These results underscore the potential of combining 5G-enabled location services with edge intelligence to enhance safety and compliance in urban UAV operations.