An evolutionary algorithm for quadcopter trajectory optimization in aerial challenges

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dc.contributor.authorAlves, Adson Nogueira [UNESP]
dc.contributor.authorFerreira, Murillo Augusto S. [UNESP]
dc.contributor.authorColombini, Esther Luna
dc.contributor.authorSimoes, Alexandre da Silva [UNESP]
dc.contributor.authorGoncalves, LMG
dc.contributor.authorDrews, PLJ
dc.contributor.authorDaSilva, BMF
dc.contributor.authorDosSantos, D. H.
dc.contributor.authorDeMelo, JCP
dc.contributor.authorCurvelo, CDF
dc.contributor.authorFabro, J. A.
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2023-07-29T11:55:36Z
dc.date.available2023-07-29T11:55:36Z
dc.date.issued2020-01-01
dc.description.abstractMachine 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.en
dc.description.affiliationSao Paulo State Univ Unesp, Grad Program Elect Engn PGEE, Sorocaba, SP, Brazil
dc.description.affiliationUniv Campinas Unicamp, Inst Comp IC, Campinas, SP, Brazil
dc.description.affiliationSao Paulo State Univ Unesp, Dept Control & Automat Engn DECA, Inst Sci & Technol ICT, Sorocaba, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ Unesp, Grad Program Elect Engn PGEE, Sorocaba, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ Unesp, Dept Control & Automat Engn DECA, Inst Sci & Technol ICT, Sorocaba, SP, Brazil
dc.description.sponsorshipCoordena��o de Aperfei�oamento de Pessoal de N�vel Superior (CAPES)
dc.description.sponsorshipElectrical Engineering Graduate Program (PGEE) at the Institute of Science and Technology (ICT) of Sorocaba
dc.description.sponsorshipAutomation and Integrated systems Group (GASI) at Unesp
dc.format.extent329-334
dc.identifier.citation2020 XVIII Latin American Robotics Symposium, 2020 Xii Brazilian Symposium on Robotics and 2020 Xi Workshop of Robotics in Education (lars-sbr-wre 2020). New York: IEEE, p. 329-334, 2020.
dc.identifier.urihttp://hdl.handle.net/11449/245458
dc.identifier.wosWOS:000856082100056
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2020 Xviii Latin American Robotics Symposium, 2020 Xii Brazilian Symposium On Robotics And 2020 Xi Workshop Of Robotics In Education (lars-sbr-wre 2020)
dc.sourceWeb of Science
dc.titleAn evolutionary algorithm for quadcopter trajectory optimization in aerial challengesen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee

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