  While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several
missions, their inability of reliable and consistent autonomous landing poses a
major setback for deploying such systems truly autonomously. In this paper we
present an autonomous UAV landing system for landing on a moving platform. In
contrast to existing attempts, the proposed system relies only on the camera
sensor, and has been designed as lightweight as possible. The proposed system
can be deployed on a low power platform as part of the drone payload, whilst
being indifferent to any external communication or any other sensors. The
system relies on a Neural Network (NN) based controller, for which a target and
environment agnostic simulator was created, used in training and testing of the
proposed system, via Reinforcement Learning (RL) and Proximal Policy
optimization (PPO) to optimally control and steer the drone towards landing on
the target. Through real-world testing, the system was evaluated with an
average deviation of 15cm from the center of the target, for 40 landing
attempts.
