Autonomous Quadrotor Landing using Deep Reinforcement Learning
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Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this article, we propose a method based on deep reinforcement learning that only requires low-resolution images taken from a down-looking camera in order to identify the position of the marker and land the UAV on it. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level control policy for the navigation toward the marker. We implemented different technical solutions, such as the combination of vanilla and double DQNs trained using a partitioned buffer replay.The results show that policies trained on uniform textures can accomplish autonomous landing on a large variety of simulated environments. The overall performance is comparable with a state-of-the-art algorithm and human pilots.
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