A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform

Deep learning techniques for motion control have recently been qualitatively improved, since the successful application of Deep Q- Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide impressive results in continuous state and action domains, which are closely linked to most of the robotics-related tasks. In this paper, a vision-based autonomous multirotor landing maneuver on top of a moving platform is presented. The behaviour has been completely learned in simulation without prior human knowledge and by means of deep reinforcement learning techniques. Since the multirotor is controlled in attitude, no high level state estimation is required. The complete behaviour has been trained with continuous action and state spaces, and has provided proper results (landing at a maximum velocity of 2 m/s), Furthermore, it has been validated in a wide variety of conditions, for both simulated and real-flight scenarios, using a low-cost, lightweight and out-of-the-box consumer multirotor.