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A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing.
Tsitses, Ioannis; Zacharia, Paraskevi; Xidias, Elias; Papoutsidakis, Michail.
Afiliação
  • Tsitses I; Department of Industrial Design and Production Engineering, University of West Attica, 12241 Egaleo, Greece.
  • Zacharia P; Department of Industrial Design and Production Engineering, University of West Attica, 12241 Egaleo, Greece.
  • Xidias E; Department of Product & Systems Design Engineering, University of the Aegean, 84100 Syros, Greece.
  • Papoutsidakis M; Department of Industrial Design and Production Engineering, University of West Attica, 12241 Egaleo, Greece.
Sensors (Basel) ; 24(2)2024 Jan 21.
Article em En | MEDLINE | ID: mdl-38276374
ABSTRACT
This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs the range from the deck, the relative wind direction and speed, the airspeed, and the UAV's flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship's velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Grécia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Grécia