Autonomous Quadrotor Landing using Deep Reinforcement Learning
This addresses the open problem of UAV landing for robotics applications, but it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackles autonomous quadrotor landing on a ground marker using only low-resolution images from a down-looking camera, achieving performance comparable to state-of-the-art algorithms and human pilots in simulated and real-world tests.
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, and a partitioned buffer replay. Using domain randomization we trained the vehicle on uniform textures and we tested it on a large variety of simulated and real-world environments. The overall performance is comparable with a state-of-the-art algorithm and human pilots.