Autonomous Quadrotor Landing using Deep Reinforcement Learning
Abstract
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.
Cite
@article{arxiv.1709.03339,
title = {Autonomous Quadrotor Landing using Deep Reinforcement Learning},
author = {Riccardo Polvara and Massimiliano Patacchiola and Sanjay Sharma and Jian Wan and Andrew Manning and Robert Sutton and Angelo Cangelosi},
journal= {arXiv preprint arXiv:1709.03339},
year = {2018}
}
Comments
The actual copy of the manuscript is a revised version resubmitted to IROS