  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.
