Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/27106 |
Resumo: | Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects. |
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Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVsAerial robotsMultiple robotsPlanningDynamic environmentPrecision agricultureUnmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.The authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. The authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança–IPB (UIDB/05757/2020 and UIDP/05757/2020), the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, and Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) and IPB, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).MDPIBiblioteca Digital do IPBCastro, Gabriel G.R.Berger, GuidoCantieri, Álvaro R.Teixeira, MarcoLima, JoséPereira, Ana I.Pinto, Milena F.2023-02-22T16:09:54Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/27106engCastro, Gabriel G.R.; Berger, Guido S.; Cantieri, Alvaro; Teixeira, Marco; Lima, José; Pereira, Ana I.; Pinto, Milena F. (2023). Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs. Agriculture.10.3390/agriculture13020354info: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:RCAAP2024-02-07T01:18:53Zoai:bibliotecadigital.ipb.pt:10198/27106Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:17:35.822021Repositório 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 |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs |
title |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs |
spellingShingle |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs Castro, Gabriel G.R. Aerial robots Multiple robots Planning Dynamic environment Precision agriculture |
title_short |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs |
title_full |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs |
title_fullStr |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs |
title_full_unstemmed |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs |
title_sort |
Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs |
author |
Castro, Gabriel G.R. |
author_facet |
Castro, Gabriel G.R. Berger, Guido Cantieri, Álvaro R. Teixeira, Marco Lima, José Pereira, Ana I. Pinto, Milena F. |
author_role |
author |
author2 |
Berger, Guido Cantieri, Álvaro R. Teixeira, Marco Lima, José Pereira, Ana I. Pinto, Milena F. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Castro, Gabriel G.R. Berger, Guido Cantieri, Álvaro R. Teixeira, Marco Lima, José Pereira, Ana I. Pinto, Milena F. |
dc.subject.por.fl_str_mv |
Aerial robots Multiple robots Planning Dynamic environment Precision agriculture |
topic |
Aerial robots Multiple robots Planning Dynamic environment Precision agriculture |
description |
Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-22T16:09:54Z 2023 2023-01-01T00:00:00Z |
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/10198/27106 |
url |
http://hdl.handle.net/10198/27106 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Castro, Gabriel G.R.; Berger, Guido S.; Cantieri, Alvaro; Teixeira, Marco; Lima, José; Pereira, Ana I.; Pinto, Milena F. (2023). Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs. Agriculture. 10.3390/agriculture13020354 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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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1799135466179526656 |