Landing UAV on Moving Surface Vehicle: Visual Tracking and Motion Prediction of Landing Deck

Landing an uncrewed aerial vehicle (UAV) on a moving surface vehicle in open waters is a formidable challenge due to the dynamic and complicated nature of the environment. The UAV is at risk of damage during the touch-down process, as the oscillatory motion of the surface vehicle can lead to hard impacts or tip-overs. In response, this paper introduces a trajectory optimization strategy for quadrotor for tracking moving surface vehicle using visual feedback from an onboard camera. The visual data is used for real time forecasting of the future motion of the surface vehicle. This prediction leverages a deep learning approach to determine the landing period in future when the surface vehicle's roll and pitch will fall below the critical landing threshold for a certain period, thereby providing a safe window for the impending landing. Our proposed method utilizes visual data from onboard camera on UAV only, obviating the need for external communication between UAV and surface vehicle. The proposed trajectory optimization method has been extensively tested in simulation environment and able to follow the target travel up to 5m/s even with highly maneuvering path. The proposed surface vehicle motion prediction was tested with recorded data and able to achieve the prediction error less than 3°, and in real-time manner.