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Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

1
School of Electronic and Electrical Engineering, TU Dublin, Central Quad, Grangegorman Lower, D07 ADY7 Dublin, Ireland
2
School of Computer Science, TU Dublin, Central Quad, Grangegorman Lower, D07 ADY7 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: George Nikolakopoulos
Drones 2021, 5(2), 52; https://doi.org/10.3390/drones5020052
Received: 4 May 2021 / Revised: 3 June 2021 / Accepted: 4 June 2021 / Published: 17 June 2021
(This article belongs to the Topic Autonomy for Enabling the Next Generation of UAVs)
With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future. View Full-Text
Keywords: artificial intelligence; deep learning; neural networks; artificial neural networks; multi-layer neural network; neural network hardware; autonomous systems; internet of things; machine vision; unmanned autonomous vehicles; unmanned aerial vehicles artificial intelligence; deep learning; neural networks; artificial neural networks; multi-layer neural network; neural network hardware; autonomous systems; internet of things; machine vision; unmanned autonomous vehicles; unmanned aerial vehicles
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MDPI and ACS Style

Lee, T.; Mckeever, S.; Courtney, J. Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy. Drones 2021, 5, 52. https://doi.org/10.3390/drones5020052

AMA Style

Lee T, Mckeever S, Courtney J. Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy. Drones. 2021; 5(2):52. https://doi.org/10.3390/drones5020052

Chicago/Turabian Style

Lee, Thomas, Susan Mckeever, and Jane Courtney. 2021. "Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy" Drones 5, no. 2: 52. https://doi.org/10.3390/drones5020052

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