Autonomous environment generator for uav-based simulation

Detalhes bibliográficos
Autor(a) principal: Nakama, Justin
Data de Publicação: 2021
Outros Autores: Parada, Ricky, Matos-Carvalho, João P., Azevedo, Fábio, Pedro, Dário, Campos, Luís
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145944
Resumo: Funding Information: Funding: This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783119. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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spelling Autonomous environment generator for uav-based simulationArtificial intelligenceAutonomous vehiclesDeep learningMachine learningNeural networkReal-world testbedSatellite imagesUAVMaterials Science(all)InstrumentationEngineering(all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesFunding Information: Funding: This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783119. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The increased demand for Unmanned Aerial Vehicles (UAV) has also led to higher demand for realistic and efficient UAV testing environments. The current use of simulated environments has been shown to be a relatively inexpensive, safe, and repeatable way to evaluate UAVs before real-world use. However, the use of generic environments and manually-created custom scenarios leaves more to be desired. In this paper, we propose a new testbed that utilizes machine learning algorithms to procedurally generate, scale, and place 3D models to create a realistic environment. These environments are additionally based on satellite images, thus providing users with a more robust example of real-world UAV deployment. Although certain graphical improvements could be made, this paper serves as a proof of concept for an novel autonomous and relatively-large scale environment generator. Such a testbed could allow for preliminary operational planning and testing worldwide, without the need for on-site evaluation or data collection in the future.UNINOVA-Instituto de Desenvolvimento de Novas TecnologiasRUNNakama, JustinParada, RickyMatos-Carvalho, João P.Azevedo, FábioPedro, DárioCampos, Luís2022-12-02T22:13:36Z2021-03-022021-03-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article18application/pdfhttp://hdl.handle.net/10362/145944eng2076-3417PURE: 45534713https://doi.org/10.3390/app11052185info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:26:45Zoai:run.unl.pt:10362/145944Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:21.465093Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Autonomous environment generator for uav-based simulation
title Autonomous environment generator for uav-based simulation
spellingShingle Autonomous environment generator for uav-based simulation
Nakama, Justin
Artificial intelligence
Autonomous vehicles
Deep learning
Machine learning
Neural network
Real-world testbed
Satellite images
UAV
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
title_short Autonomous environment generator for uav-based simulation
title_full Autonomous environment generator for uav-based simulation
title_fullStr Autonomous environment generator for uav-based simulation
title_full_unstemmed Autonomous environment generator for uav-based simulation
title_sort Autonomous environment generator for uav-based simulation
author Nakama, Justin
author_facet Nakama, Justin
Parada, Ricky
Matos-Carvalho, João P.
Azevedo, Fábio
Pedro, Dário
Campos, Luís
author_role author
author2 Parada, Ricky
Matos-Carvalho, João P.
Azevedo, Fábio
Pedro, Dário
Campos, Luís
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
RUN
dc.contributor.author.fl_str_mv Nakama, Justin
Parada, Ricky
Matos-Carvalho, João P.
Azevedo, Fábio
Pedro, Dário
Campos, Luís
dc.subject.por.fl_str_mv Artificial intelligence
Autonomous vehicles
Deep learning
Machine learning
Neural network
Real-world testbed
Satellite images
UAV
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
topic Artificial intelligence
Autonomous vehicles
Deep learning
Machine learning
Neural network
Real-world testbed
Satellite images
UAV
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
description Funding Information: Funding: This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783119. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publishDate 2021
dc.date.none.fl_str_mv 2021-03-02
2021-03-02T00:00:00Z
2022-12-02T22:13:36Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145944
url http://hdl.handle.net/10362/145944
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2076-3417
PURE: 45534713
https://doi.org/10.3390/app11052185
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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