Integrated Learning-based Framework for Autonomous Quadrotor UAV Landing on a Collaborative Moving UGV

Autonomous unmanned aerial vehicle (UAV) landing on a moving unmanned ground vehicle (UGV) remains a challenge as it is difficult for the UAV to track the real-time state of the UGV and adjust its landing policy accordingly. This paper proposes a learning framework for a quadrotor UAV to land on a moving UGV without knowing its motion dynamics. Specifically, the learning framework consists of two main systems: a Landing Vision System (LVS) using deep learning and a Landing Control System (LCS) using deep reinforcement learning. The LVS enables the UAV to recognize and localize the UGV in real time to estimate the relative position and velocity between them. Besides, the location of the UGV is tracked in the field of view of the UAV using consecutive images, alleviating the tracking failure problem. We propose a Memory Consolidated TD3 (MCTD3) algorithm to generate optimal policies to enable precise tracking and landing control of the UAV. In addition, we propose an adaptive COACH (ACOACH) algorithm that allows human intervention in the action space of the UAV to speed up the training process. We demonstrate the effectiveness of the proposed method in both simulation and real-world experiments.