Intelligent Vision-based Autonomous Ship Landing of VTOL UAVs
Bochan Lee, Vishnu Saj, Moble Benedict, Dileep Kalathil

TL;DR
This paper presents a vision-based autonomous landing system for VTOL UAVs on ships, using machine learning and classical vision techniques to enable GPS-free, precise, and robust ship landings in dynamic conditions.
Contribution
It introduces a novel nonlinear control system integrated with machine vision for autonomous ship landing of UAVs without GPS, validated through simulations and real-world tests.
Findings
Successful autonomous landings on moving ship decks
Robust tracking performance despite uncertainties
Effective vision-based relative position estimation
Abstract
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…
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Taxonomy
TopicsAerospace Engineering and Control Systems · Ship Hydrodynamics and Maneuverability · Adaptive Control of Nonlinear Systems
