Publication:
Quadcopter neural controller for take-off and landing in windy environments

Simple item page
dc.contributor.authorOlaz Moratinos, Xabier
dc.contributor.authorAláez Gómez, Daniel
dc.contributor.authorPrieto Míguez, Manuel
dc.contributor.authorVilladangos Alonso, Jesús
dc.contributor.authorAstrain Escola, José Javier
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.date.accessioned2023-06-16T16:04:59Z
dc.date.issued2023
dc.date.updated2023-06-16T15:58:48Z
dc.description.abstractThis paper proposes the design of a quadcopter neural controller based on Reinforcement Learning (RL) for controlling the complete maneuvers of landing and take-off, even in variable windy conditions. To facilitate RL training, a wind model is designed, and two RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), are adapted and compared. The first phases of the learning process consider extended exploration states as a warm-up, and a novel neural network controller architecture is proposed with the addition of an adaptation layer. The neural network’s output is defined as the forces and momentum desired for the UAV, and the adaptation layer transforms forces and momentum into motor velocities. By decoupling attitude from motor velocities, the adaptation layer enhances a more straightforward interpretation of the neural network output and helps refine the rewards. The successful neural controller training has been tested up to 36 km/h wind speed.en
dc.description.sponsorshipThis work has been supported in part by the Ministerio de Ciencia e Innovación (Spain) and European Union NextGenerationEU, Spain under the research grant TED2021-131716B-C21 SARA (Data processing by superresolution algorithms); in part by Agencia Estatal de Investigación (AEI), Spain and European Union NextGenerationEU/PRTR, Spain PLEC2021-007997: Holistic power lines predictive maintenance system; and in part by the Government of Navarre (Departamento de Desarrollo Económico), Spain under the research grants 0011-1411-2021-000021 EMERAL: Emergency UAVs for long range operations, 0011-1365-2020-000078 DIVA, and 0011-1411-2021-000025 MOSIC: Plataforma logística de largo alcance, eléctrica y conectada.en
dc.embargo.lift2025-09-01
dc.embargo.terms2025-09-01
dc.format.mimetypeapplication/pdfen
dc.identifier.citationOlaz, X., Alaez, D., Prieto, M., Villadangos, J., Astrain, J. J. (2023) Quadcopter neural controller for take-off and landing in windy environments. Expert Systems with Applications, 225, 1-15. https://doi.org/10.1016/j.eswa.2023.120146.en
dc.identifier.doi10.1016/j.eswa.2023.120146
dc.identifier.issn0957-4174
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/45521
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofExpert Systems with Applications 225 (2023) 120146en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI//TED2021-131716B-C21en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI//PLEC2021-007997en
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//0011-1411-2021-000021en
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//0011-1365-2020-000078en
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//0011-1411-2021-000025en
dc.relation.publisherversionhttps://doi.org/10.1016/j.eswa.2023.120146
dc.rights© 2023 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0en
dc.rights.accessRightsAcceso embargado / Sarbidea bahitua dagoes
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccessen
dc.subjectQuadcopteren
dc.subjectTake-offen
dc.subjectLandingen
dc.subjectDeep reinforcement learningen
dc.subjectWinden
dc.subjectPPOen
dc.subjectDDPGen
dc.titleQuadcopter neural controller for take-off and landing in windy environmentsen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dspace.entity.typePublication
relation.isAuthorOfPublication7b630e93-8e7d-4a6a-b5b9-2a6228f1ba49
relation.isAuthorOfPublicationb271d878-81b4-4aea-918a-e708bf967544
relation.isAuthorOfPublication70e13a05-bb1f-4321-ad39-49810754d6df
relation.isAuthorOfPublicationbbc8cfd2-e8ad-4eec-bf0e-ad22acb246ac
relation.isAuthorOfPublicationf02194ce-06af-4673-82b3-9b96137a08d3
relation.isAuthorOfPublication.latestForDiscovery7b630e93-8e7d-4a6a-b5b9-2a6228f1ba49

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Olaz_QuadcopterNeural.pdf
Size:
5.33 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.78 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

Artículos de revista DEIM - EIMS Aldizkari artikuluak
Artículos de revista - Aldizkari artikuluak
Artículos de revista ISC - ISC aldizkari artikuluak