Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

翻译：在恶劣开阔水域中，将无人机降落在无人水面艇（USV）顶部是一个极具挑战性的问题，因为在触地过程中，USV的严重横摇和/或纵摇角度可能产生的力会损坏无人机。为解决这一问题，我们提出一种新颖的模型预测控制（MPC）方法，使无人机能够在这些恶劣条件下自主降落在USV上。该MPC采用一种新颖的目标函数，并对船体的振荡运动进行在线分解，以预测、尝试并在降落平台接近零倾斜时完成降落。船体运动的非线性预测通过机载摄像头的视觉数据实现，因此系统无需与USV或控制站进行任何通信。所提方法在严酷和极端条件下的众多机器人仿真中进行了分析，并在各种真实场景中得到了进一步验证。