Machine Learning Methods for Management UAV Flocks -- a Survey

TL;DR

This survey reviews machine learning techniques applied to UAV flock management, highlighting challenges, solutions, and open issues to guide future research and development in this rapidly evolving field.

Contribution

It provides a comprehensive overview of ML methods for UAV flock management, including challenges, solutions, and future directions, filling a gap in current literature.

Findings

Various ML methods address UAV flock challenges

Identification of open issues in UAV flock management

Guidance for future research in ML for UAVs

Abstract

The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.