An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/LARS/SBR/WRE51543.2020.9307102 http://hdl.handle.net/11449/205827 |
Resumo: | Machine learning methods have been widely employed in robotics over the years, and recent developments in machine learning have completely re-shaped problem-solving in the area. Indeed, if we consider multi-objective planning, these models' optimization and learning capabilities can derive more robust strategies. Inspired by the species natural selection mechanism, Evolutionary Algorithms (EA) are among the best known computational approaches available for this purpose. In this scenario, this work proposed an EA model developed to find the best travel trajectory for a quadcopter in the 'Desafio Petrobras' challenge. In the challenge, a set of landing platforms that the robot has to visit are displaced in the 3D-space. To find the best trajectory possible, we optimize an EA over a low-level control that can take the quadcopter from point A to B. We vary our fitness function to support more complex decisions. The software-in-the-loop technique was applied for a simulated quadrotor in the Coppelia simulated environment. The proposed approach has shown the capability to generate short trajectories while considering variables like UAV dynamics and energy consumption. |
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Repositório Institucional da UNESP |
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An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial ChallengesMachine learning methods have been widely employed in robotics over the years, and recent developments in machine learning have completely re-shaped problem-solving in the area. Indeed, if we consider multi-objective planning, these models' optimization and learning capabilities can derive more robust strategies. Inspired by the species natural selection mechanism, Evolutionary Algorithms (EA) are among the best known computational approaches available for this purpose. In this scenario, this work proposed an EA model developed to find the best travel trajectory for a quadcopter in the 'Desafio Petrobras' challenge. In the challenge, a set of landing platforms that the robot has to visit are displaced in the 3D-space. To find the best trajectory possible, we optimize an EA over a low-level control that can take the quadcopter from point A to B. We vary our fitness function to support more complex decisions. The software-in-the-loop technique was applied for a simulated quadrotor in the Coppelia simulated environment. The proposed approach has shown the capability to generate short trajectories while considering variables like UAV dynamics and energy consumption.Graduate Program in Electrical Engineering (PGEE) Sao Paulo State University (Unesp)Institute of Computing (IC) of the University of Campinas (Unicamp)Graduate Program in Electrical Engineering (PGEE) Sao Paulo State University (Unesp)Universidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Nogueira Alves, Adson [UNESP]Ferreira, Murillo Augusto S.Colombini, Esther Luna [UNESP]Da Silva Simoes, Alexandre [UNESP]2021-06-25T10:21:59Z2021-06-25T10:21:59Z2020-11-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/LARS/SBR/WRE51543.2020.93071022020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020.http://hdl.handle.net/11449/20582710.1109/LARS/SBR/WRE51543.2020.93071022-s2.0-85100305844Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020info:eu-repo/semantics/openAccess2021-10-22T18:13:00Zoai:repositorio.unesp.br:11449/205827Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:31:31.714561Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges |
title |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges |
spellingShingle |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges Nogueira Alves, Adson [UNESP] |
title_short |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges |
title_full |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges |
title_fullStr |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges |
title_full_unstemmed |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges |
title_sort |
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges |
author |
Nogueira Alves, Adson [UNESP] |
author_facet |
Nogueira Alves, Adson [UNESP] Ferreira, Murillo Augusto S. Colombini, Esther Luna [UNESP] Da Silva Simoes, Alexandre [UNESP] |
author_role |
author |
author2 |
Ferreira, Murillo Augusto S. Colombini, Esther Luna [UNESP] Da Silva Simoes, Alexandre [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Estadual de Campinas (UNICAMP) |
dc.contributor.author.fl_str_mv |
Nogueira Alves, Adson [UNESP] Ferreira, Murillo Augusto S. Colombini, Esther Luna [UNESP] Da Silva Simoes, Alexandre [UNESP] |
description |
Machine learning methods have been widely employed in robotics over the years, and recent developments in machine learning have completely re-shaped problem-solving in the area. Indeed, if we consider multi-objective planning, these models' optimization and learning capabilities can derive more robust strategies. Inspired by the species natural selection mechanism, Evolutionary Algorithms (EA) are among the best known computational approaches available for this purpose. In this scenario, this work proposed an EA model developed to find the best travel trajectory for a quadcopter in the 'Desafio Petrobras' challenge. In the challenge, a set of landing platforms that the robot has to visit are displaced in the 3D-space. To find the best trajectory possible, we optimize an EA over a low-level control that can take the quadcopter from point A to B. We vary our fitness function to support more complex decisions. The software-in-the-loop technique was applied for a simulated quadrotor in the Coppelia simulated environment. The proposed approach has shown the capability to generate short trajectories while considering variables like UAV dynamics and energy consumption. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-09 2021-06-25T10:21:59Z 2021-06-25T10:21:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/LARS/SBR/WRE51543.2020.9307102 2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020. http://hdl.handle.net/11449/205827 10.1109/LARS/SBR/WRE51543.2020.9307102 2-s2.0-85100305844 |
url |
http://dx.doi.org/10.1109/LARS/SBR/WRE51543.2020.9307102 http://hdl.handle.net/11449/205827 |
identifier_str_mv |
2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020. 10.1109/LARS/SBR/WRE51543.2020.9307102 2-s2.0-85100305844 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1808128667303280640 |