Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Computer Sciences, Electrical and electronic engineering, Robotics and automatic control
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.
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