A Survey of Offline and Online Learning-Based Algorithms for Multirotor UAVs

TL;DR

This survey reviews recent offline and online learning algorithms used for multirotor UAV navigation and control, emphasizing real-time applicability and categorizing methods into machine learning, deep learning, and reinforcement learning since 2015.

Contribution

It provides a comprehensive classification and analysis of learning-based algorithms for multirotor UAVs, focusing on real-time implementation and recent advancements since 2015.

Findings

Online learning algorithms suitable for real-time UAV control identified

Classification of algorithms into machine learning, deep learning, reinforcement learning

Insights into what information is learned and how quickly in UAV contexts

Abstract

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Navigation controllers endowed with different attributes and onboard sensor suites enable multirotor autonomous or semi-autonomous, safe flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the faster-than-exponential increase of available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been, and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environment in which multirotors operate. Learning algorithms combined with model-based control techniques have been proven beneficial when applied to multirotors. This survey summarizes published research since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories, and then, further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey are on online learning algorithms as applied to multirotors with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, and how, and how fast. The outcome of the survey offers a clear understanding of the recent state-of-the-art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real-time.