  The paper discusses an intelligent vision-based control solution for
autonomous tracking and landing of Vertical Take-Off and Landing (VTOL) capable
Unmanned Aerial Vehicles (UAVs) on ships without utilizing GPS signal. The
central idea involves automating the Navy helicopter ship landing procedure
where the pilot utilizes the ship as the visual reference for long-range
tracking; however, refers to a standardized visual cue installed on most Navy
ships called the "horizon bar" for the final approach and landing phases. This
idea is implemented using a uniquely designed nonlinear controller integrated
with machine vision. The vision system utilizes machine learning-based object
detection for long-range ship tracking and classical computer vision for the
estimation of aircraft relative position and orientation utilizing the horizon
bar during the final approach and landing phases. The nonlinear controller
operates based on the information estimated by the vision system and has
demonstrated robust tracking performance even in the presence of uncertainties.
The developed autonomous ship landing system was implemented on a quad-rotor
UAV equipped with an onboard camera, and approach and landing were successfully
demonstrated on a moving deck, which imitates realistic ship deck motions.
Extensive simulations and flight tests were conducted to demonstrate vertical
landing safety, tracking capability, and landing accuracy.
