  This paper addresses the problem of developing an algorithm for autonomous
ship landing of vertical take-off and landing (VTOL) capable unmanned aerial
vehicles (UAVs), using only a monocular camera in the UAV for tracking and
localization. Ship landing is a challenging task due to the small landing
space, six degrees of freedom ship deck motion, limited visual references for
localization, and adversarial environmental conditions such as wind gusts. We
first develop a computer vision algorithm which estimates the relative position
of the UAV with respect to a horizon reference bar on the landing platform
using the image stream from a monocular vision camera on the UAV. Our approach
is motivated by the actual ship landing procedure followed by the Navy
helicopter pilots in tracking the horizon reference bar as a visual cue. We
then develop a robust reinforcement learning (RL) algorithm for controlling the
UAV towards the landing platform even in the presence of adversarial
environmental conditions such as wind gusts. We demonstrate the superior
performance of our algorithm compared to a benchmark nonlinear PID control
approach, both in the simulation experiments using the Gazebo environment and
in the real-world setting using a Parrot ANAFI quad-rotor and sub-scale ship
platform undergoing 6 degrees of freedom (DOF) deck motion.
