@misc{10481/70820,
year = {2021},
month = {10},
url = {http://hdl.handle.net/10481/70820},
abstract = {Unmanned Aerial Vehicles (UAVs) have recently
attracted both academia and industry representatives due to
their utilization in tremendous emerging applications. Most
UAV applications adopt Visual Line of Sight (VLOS) due to
ongoing regulations. There is a consensus between industry for
extending UAVs’ commercial operations to cover the urban and
populated area controlled airspace Beyond VLOS (BVLOS).
There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges
related to UAVs’ autonomy for detecting and avoiding static
and mobile objects. An intelligent component should either
be deployed onboard the UAV or at a Multi-Access Edge
Computing (MEC) that can read the gathered data from
different UAV’s sensors, process them, and then make the
right decision to detect and avoid the physical collision. The
sensing data should be collected using various sensors but
not limited to Lidar, depth camera, video, or ultrasonic. This
paper proposes probabilistic and Deep Reinforcement Learning
(DRL)-based algorithms for avoiding collisions while saving
energy consumption. The proposed algorithms can be either run
on top of the UAV or at the MEC according to the UAV capacity
and the task overhead. We have designed and developed
our algorithms to work for any environment without a need
for any prior knowledge. The proposed solutions have been
evaluated in a harsh environment that consists of many UAVs
moving randomly in a small area without any correlation. The
obtained results demonstrated the efficiency of these solutions
for avoiding the collision while saving energy consumption in
familiar and unfamiliar environments.},
organization = {This work has been partially funded by the Spanish national project TRUE-5G (PID2019-108713RB-C53).},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {Unmanned Aerial Vehicles (UAVs)},
keywords = {Collision Avoidance},
keywords = {Multi-Access Edge Computing (MEC)},
keywords = {Machine learning},
keywords = {Deep Reinforcement Learning},
title = {Deep Reinforcement Learning based Collision Avoidance in UAV Environment},
doi = {10.1109/JIOT.2021.3118949},
author = {Ouahouah, Sihem and Bagaa, Miloud and Prados Garzón, Jonathan and Taleb, Tarik},
}