UAV-USV docking control system based on motion compensation deck and attitude prediction

Unmanned Aerial Vehicle (UAV) possess excellent maritime detection capabilities and have a fast cruising speed. Developing a collaborative mission system including UAV and Unmanned Surface Vehicle (USV) can significantly enhance the efficiency of maritime operations, making it a crucial developmental path for heterogeneous unmanned maritime systems. However, the rocking motion of USV due to sea wind and waves makes stable takeoff and landing of UAV difficult. To overcome this issue, we propose a pitch motion compensation deck that ensures the accuracy of sensor positioning and the stability of relative attitude during the docking process. To address the periodic changes in roll resulting from the interaction between the USV and waves, we present an online rolling prediction method based on the Hilbert-Huang Transform-Long Short-Term Memory network (HHT-LSTM). It segments the rolling time history data obtained from USV inertial navigation into distinct working conditions, which are fed into the trained LSTM network for training and forecasting. The optimal time for UAV landing is then determined using this data, which is sent to the UAV's motion controller to complete its precise landing. We validated the stability and feasibility of our proposed algorithm through practical experiments conducted on field water surfaces.