Intelligent Vision-based Autonomous Ship Landing of VTOL UAVs
This addresses the problem of enabling UAVs to land autonomously on ships in GPS-denied environments, which is crucial for naval operations, though it builds incrementally on existing ship landing procedures.
The paper tackled autonomous landing of VTOL UAVs on moving ships without GPS by developing a vision-based control system that integrates machine learning for ship tracking and computer vision for precise positioning using a horizon bar, successfully demonstrating landing on a moving deck with robust performance.
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.