Machine Learning-Assisted UAV Operations with UTM: Requirements, Challenges, and Solutions

TL;DR

This paper explores how machine learning can enhance unmanned aircraft system traffic management (UTM) by addressing operational challenges and leveraging data-driven solutions for safer, more efficient UAV operations.

Contribution

It introduces the four pillars of UTM, discusses ML opportunities in each, and highlights the importance of data-driven algorithms and online learning for future UAV management.

Findings

ML can improve operation planning and situational awareness.

Data-driven algorithms enhance UTM service effectiveness.

Online learning adapts to new UAV operational scenarios.

Abstract

Unmanned aerial vehicles (UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. The unmanned aircraft system (UAS) traffic management (UTM) provides a framework for safe UAV operation integrating UAV controllers and central data bases via a communications network. This paper discusses the challenges and opportunities for machine learning (ML) for effectively providing critical UTM services. We introduce the four pillars of UTM---operation planning, situational awareness, status and advisors and security---and discuss the main services, specific opportunities for ML and the ongoing research. We conclude that the multi-faceted operating environment and operational parameters will benefit from collected data and data-driven algorithms, as well as online learning to face new UAV operation situations.