Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm

There is an increasing research interest in unmanned autonomous vehicles (UAVs) such as quadrotors. These researches applies these quadrotors for much more complicated tasks with most requiring cameras and GPS modules for positioning. This paper presents an alternative way of position localization o...

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Main Authors: Faelden, Gerard Ely U., Maningo, Jose Martin Z., Nakano, Reiichiro Christian S., Bandala, Argel A., Dadios, Elmer P.
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Published: Animo Repository 2014
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2341
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3340/type/native/viewcontent
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-33402021-08-24T08:02:53Z Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm Faelden, Gerard Ely U. Maningo, Jose Martin Z. Nakano, Reiichiro Christian S. Bandala, Argel A. Dadios, Elmer P. There is an increasing research interest in unmanned autonomous vehicles (UAVs) such as quadrotors. These researches applies these quadrotors for much more complicated tasks with most requiring cameras and GPS modules for positioning. This paper presents an alternative way of position localization of a quadrotor without the use of cameras and GPS modules by means of transceivers and Genetic Algorithm (GA). This paper uses the received signals from the transceivers as inputs for the genetic algorithm in order to locate the quadrotor in a xyz axis. Parameters such as location of transceivers, amount of transceivers and population size of the GA are tested in order to determine a successful way of locating the quadrotor. Results show that the different parameters tested were successful and converges to a point usually with a fitness measure greater than 99%. An average fitness measure greater than 99.9900% served as a benchmark for the tests done. The first test achieved this benchmark at about 130 generations and the second test achieved it at 110 generations. The time it took for the program to locate the quadrotor is about 60 milliseconds. Results show that this blind localization technique is successfully locates the quadrotor and may be calibrated to one's own need. © 2014 IEEE. 2014-01-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/2341 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3340/type/native/viewcontent Faculty Research Work Animo Repository Quadrotor helicopters Genetic algorithms Automatic tracking Electrical and Computer Engineering Electrical and Electronics Systems and Communications
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Quadrotor helicopters
Genetic algorithms
Automatic tracking
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
spellingShingle Quadrotor helicopters
Genetic algorithms
Automatic tracking
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
Faelden, Gerard Ely U.
Maningo, Jose Martin Z.
Nakano, Reiichiro Christian S.
Bandala, Argel A.
Dadios, Elmer P.
Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm
description There is an increasing research interest in unmanned autonomous vehicles (UAVs) such as quadrotors. These researches applies these quadrotors for much more complicated tasks with most requiring cameras and GPS modules for positioning. This paper presents an alternative way of position localization of a quadrotor without the use of cameras and GPS modules by means of transceivers and Genetic Algorithm (GA). This paper uses the received signals from the transceivers as inputs for the genetic algorithm in order to locate the quadrotor in a xyz axis. Parameters such as location of transceivers, amount of transceivers and population size of the GA are tested in order to determine a successful way of locating the quadrotor. Results show that the different parameters tested were successful and converges to a point usually with a fitness measure greater than 99%. An average fitness measure greater than 99.9900% served as a benchmark for the tests done. The first test achieved this benchmark at about 130 generations and the second test achieved it at 110 generations. The time it took for the program to locate the quadrotor is about 60 milliseconds. Results show that this blind localization technique is successfully locates the quadrotor and may be calibrated to one's own need. © 2014 IEEE.
format text
author Faelden, Gerard Ely U.
Maningo, Jose Martin Z.
Nakano, Reiichiro Christian S.
Bandala, Argel A.
Dadios, Elmer P.
author_facet Faelden, Gerard Ely U.
Maningo, Jose Martin Z.
Nakano, Reiichiro Christian S.
Bandala, Argel A.
Dadios, Elmer P.
author_sort Faelden, Gerard Ely U.
title Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm
title_short Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm
title_full Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm
title_fullStr Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm
title_full_unstemmed Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm
title_sort blind localization method for quadrotor-unmanned aerial vehicle (quav) utilizing genetic algortihm
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/faculty_research/2341
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3340/type/native/viewcontent
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