  Urban air mobility is the new mode of transportation aiming to provide a fast
and secure way of travel by utilizing the low-altitude airspace. This goal
cannot be achieved without the implementation of new flight regulations which
can assure safe and efficient allocation of flight paths to a large number of
vertical takeoff/landing aerial vehicles. Such rules should also allow
estimating the effective capacity of the low-altitude airspace for planning
purposes. Path planning is a vital subject in urban air mobility which could
enable a large number of UAVs to fly simultaneously in the airspace without
facing the risk of collision. Since urban air mobility is a novel concept,
authorities are still working on the redaction of new flight rules applicable
to urban air mobility. In this study, an autonomous UAV path planning framework
is proposed using a deep reinforcement learning approach and a deep
deterministic policy gradient algorithm. The objective is to employ a
self-trained UAV to reach its destination in the shortest possible time in any
arbitrary environment by adjusting its acceleration. It should avoid collisions
with any dynamic or static obstacles and avoid entering prior permission zones
existing on its path. The reward function is the determinant factor in the
training process. Thus, two different reward function compositions are compared
and the chosen composition is deployed to train the UAV by coding the RL
algorithm in python. Finally, numerical simulations investigated the success
rate of UAVs in different scenarios providing an estimate of the effective
airspace capacity.

Robotics;Artificial Intelligence;Signal Processing