An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.creatorNogueira Alves, Adson [UNESP]-
Autor(es): dc.creatorFerreira, Murillo Augusto S.-
Autor(es): dc.creatorColombini, Esther Luna [UNESP]-
Autor(es): dc.creatorDa Silva Simoes, Alexandre [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:45:25Z-
Data de disponibilização: dc.date.available2022-02-22T00:45:25Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-11-08-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/LARS/SBR/WRE51543.2020.9307102-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/205827-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/205827-
Descrição: dc.descriptionMachine 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.-
Descrição: dc.descriptionGraduate Program in Electrical Engineering (PGEE) Sao Paulo State University (Unesp)-
Descrição: dc.descriptionInstitute of Computing (IC) of the University of Campinas (Unicamp)-
Descrição: dc.descriptionGraduate Program in Electrical Engineering (PGEE) Sao Paulo State University (Unesp)-
Idioma: dc.languageen-
Relação: dc.relation2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020-
???dc.source???: dc.sourceScopus-
Título: dc.titleAn Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges-
Aparece nas coleções:Repositório Institucional - Unesp

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