An Intelligent Decision Support System for Aircraft Landing Based on the Runway Surface

Seitbattalov, Zhexen Y.; Atanov, Sabyrzhan; Moldabayeva, Zhanar S.

doi:10.1109/sist50301.2021.9466000

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Webots R2023b has been chosen as the 3-dimensional robot simulator [23,24] to develop an automated flight algorithm for UAV, as it has a wide scope of applications, allows programming the controller, contains a large library of robots and tools for their design, maintains a large number of widely used programming languages, and it is a cross-platform software.…”

Section: Methodsmentioning

confidence: 99%

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Tulembayeva,

Ashirov,

Makarov

et al. 2024

Acta Polytech

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In the last decade, unmanned aerial vehicles have been widely used in various civil and military field operations. Most modern drones are manually controlled by an operator and require decision making, which requires experience gained through training, which is time consuming and expensive, and even with experienced operators, the human factor can never be excluded. In this paper, an algorithm for an automatic flight mode of unmanned aerial vehicles without an operator’s participation has been developed. The proposed algorithm has been considered in the military reconnaissance of railways using edge computing and computer vision approaches to recognise railroads and moving trains for simulated and real cases as well as send report data to the web platform. As a result, the head of the operation received information about the number of carriages and Global Positioning System coordinates of the recognised train to make the necessary decisions.

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Section: Methodsmentioning

confidence: 99%

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Tulembayeva,

Ashirov,

Makarov

et al. 2024

Acta Polytech

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“…The work of [ 11 ] proposes a hierarchical sliding mode-based adaptive fuzzy control for underactuated nonlinear systems, such as a UAV, in which there is a mismatch between the control input and the degrees of freedom being controlled that increases the difficulty of control. In [ 12 ], a decision support system is introduced utilizing fuzzy logic to aid the pilots of Boeing 747-100 aircraft. The research aims to mitigate accidents associated with the aircraft landing assistance system and conduct a speed analysis of the Boeing 747-100.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

Tsitses,

Zacharia,

Xidias

et al. 2024

Sensors

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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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Development an Intelligent Task Offloading System for Edge-Cloud Computing Paradigm

Atanov

Seitbattalov

Moldabayeva

2021

2021 16th International Conference on Electronics Computer and Computation (ICECCO)

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An Intelligent Decision Support System for Aircraft Landing Based on the Runway Surface

Cited by 7 publications

References 13 publications

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

Development an Intelligent Task Offloading System for Edge-Cloud Computing Paradigm

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