Ffau—framework for fully autonomous uavs

Detalhes bibliográficos
Autor(a) principal: Pedro, Dário
Data de Publicação: 2020
Outros Autores: Matos-Carvalho, João P., Azevedo, Fábio, Sacoto-Martins, Ricardo, Bernardo, Luís, Campos, Luís, Fonseca, José M., Mora, André
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/115145
Resumo: Nr. 024539 (POCI-01-0247-FEDER-024539) under grant agreement No 783221 UID/EEA/00066/2019
id RCAP_e50e261929f3a5f6bd1773707adbf2fe
oai_identifier_str oai:run.unl.pt:10362/115145
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Ffau—framework for fully autonomous uavsArtificial intelligenceCollision avoidanceDeep learningDronesFrameworkMachine learningNeuronal networkResilienceUAVEarth and Planetary Sciences(all)Nr. 024539 (POCI-01-0247-FEDER-024539) under grant agreement No 783221 UID/EEA/00066/2019Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.CTS - Centro de Tecnologia e SistemasUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasDEE - Departamento de Engenharia Electrotécnica e de ComputadoresDEE2010-A1 TelecomunicaçõesDEE2010-C1 Sistemas Digitais e PercepcionaisRUNPedro, DárioMatos-Carvalho, João P.Azevedo, FábioSacoto-Martins, RicardoBernardo, LuísCampos, LuísFonseca, José M.Mora, André2021-04-07T22:15:51Z2020-10-282020-10-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article23application/pdfhttp://hdl.handle.net/10362/115145eng2072-4292PURE: 27014899https://doi.org/10.3390/rs12213533info: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:RCAAP2024-03-11T04:57:47Zoai:run.unl.pt:10362/115145Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:41.895423Repositório 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 Ffau—framework for fully autonomous uavs
title Ffau—framework for fully autonomous uavs
spellingShingle Ffau—framework for fully autonomous uavs
Pedro, Dário
Artificial intelligence
Collision avoidance
Deep learning
Drones
Framework
Machine learning
Neuronal network
Resilience
UAV
Earth and Planetary Sciences(all)
title_short Ffau—framework for fully autonomous uavs
title_full Ffau—framework for fully autonomous uavs
title_fullStr Ffau—framework for fully autonomous uavs
title_full_unstemmed Ffau—framework for fully autonomous uavs
title_sort Ffau—framework for fully autonomous uavs
author Pedro, Dário
author_facet Pedro, Dário
Matos-Carvalho, João P.
Azevedo, Fábio
Sacoto-Martins, Ricardo
Bernardo, Luís
Campos, Luís
Fonseca, José M.
Mora, André
author_role author
author2 Matos-Carvalho, João P.
Azevedo, Fábio
Sacoto-Martins, Ricardo
Bernardo, Luís
Campos, Luís
Fonseca, José M.
Mora, André
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv CTS - Centro de Tecnologia e Sistemas
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
DEE - Departamento de Engenharia Electrotécnica e de Computadores
DEE2010-A1 Telecomunicações
DEE2010-C1 Sistemas Digitais e Percepcionais
RUN
dc.contributor.author.fl_str_mv Pedro, Dário
Matos-Carvalho, João P.
Azevedo, Fábio
Sacoto-Martins, Ricardo
Bernardo, Luís
Campos, Luís
Fonseca, José M.
Mora, André
dc.subject.por.fl_str_mv Artificial intelligence
Collision avoidance
Deep learning
Drones
Framework
Machine learning
Neuronal network
Resilience
UAV
Earth and Planetary Sciences(all)
topic Artificial intelligence
Collision avoidance
Deep learning
Drones
Framework
Machine learning
Neuronal network
Resilience
UAV
Earth and Planetary Sciences(all)
description Nr. 024539 (POCI-01-0247-FEDER-024539) under grant agreement No 783221 UID/EEA/00066/2019
publishDate 2020
dc.date.none.fl_str_mv 2020-10-28
2020-10-28T00:00:00Z
2021-04-07T22:15:51Z
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/10362/115145
url http://hdl.handle.net/10362/115145
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2072-4292
PURE: 27014899
https://doi.org/10.3390/rs12213533
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 23
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
_version_ 1799138038435020800

Registros relacionados