Deep Reinforcement Learning for Autonomous Navigation

Autonomous navigation in GPS denied environments poses significant challenges environmental knowledge in limited. Conventional path optimization methods struggle with these complexities. The motivation for this thesis is to develop a model-free learning algorithm based on Deep Reinforcement Learning...

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Detalles Bibliográficos
Autor: Verma, Preeti
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/28351
Acceso en línea:http://hdl.handle.net/10256/28351
https://hdl.handle.net/10256/28351
Access Level:acceso abierto
Palabra clave:Vehicles autònoms
Autonomous Vehicles
Autonomous Navigation
Deep learning (Machine learning)
Aprenentatge profund (Aprenentatge automàtic)
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spelling Deep Reinforcement Learning for Autonomous NavigationVerma, PreetiVehicles autònomsAutonomous VehiclesAutonomous NavigationDeep learning (Machine learning)Aprenentatge profund (Aprenentatge automàtic)Autonomous navigation in GPS denied environments poses significant challenges environmental knowledge in limited. Conventional path optimization methods struggle with these complexities. The motivation for this thesis is to develop a model-free learning algorithm based on Deep Reinforcement Learning (DRL) that can effectively navigate in unstructured environments, while avoiding collisions and minimizing time and battery consumption. The primary goal is to contribute a novel approach to navigation using DRL. The added value lies in enabling autonomous vehicles to navigate efficiently without requiring precise environmental or pose information. The algorithm's capability to adapt to uncertainties and produce optimized paths under realistic conditions is a significant contribution.9Universitat de Girona. Institut de Recerca en Visió per Computador i RobòticaPalomeras Rovira, NarcísNagy, Balázs2024info:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10256/28351https://hdl.handle.net/10256/28351Erasmus Mundus Joint Master in Intelligent Field Robotic Systems (IFROS)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/283512026-05-29T05:05:01Z
dc.title.none.fl_str_mv Deep Reinforcement Learning for Autonomous Navigation
title Deep Reinforcement Learning for Autonomous Navigation
spellingShingle Deep Reinforcement Learning for Autonomous Navigation
Verma, Preeti
Vehicles autònoms
Autonomous Vehicles
Autonomous Navigation
Deep learning (Machine learning)
Aprenentatge profund (Aprenentatge automàtic)
title_short Deep Reinforcement Learning for Autonomous Navigation
title_full Deep Reinforcement Learning for Autonomous Navigation
title_fullStr Deep Reinforcement Learning for Autonomous Navigation
title_full_unstemmed Deep Reinforcement Learning for Autonomous Navigation
title_sort Deep Reinforcement Learning for Autonomous Navigation
dc.creator.none.fl_str_mv Verma, Preeti
author Verma, Preeti
author_facet Verma, Preeti
author_role author
dc.contributor.none.fl_str_mv Palomeras Rovira, Narcís
Nagy, Balázs
dc.subject.none.fl_str_mv Vehicles autònoms
Autonomous Vehicles
Autonomous Navigation
Deep learning (Machine learning)
Aprenentatge profund (Aprenentatge automàtic)
topic Vehicles autònoms
Autonomous Vehicles
Autonomous Navigation
Deep learning (Machine learning)
Aprenentatge profund (Aprenentatge automàtic)
description Autonomous navigation in GPS denied environments poses significant challenges environmental knowledge in limited. Conventional path optimization methods struggle with these complexities. The motivation for this thesis is to develop a model-free learning algorithm based on Deep Reinforcement Learning (DRL) that can effectively navigate in unstructured environments, while avoiding collisions and minimizing time and battery consumption. The primary goal is to contribute a novel approach to navigation using DRL. The added value lies in enabling autonomous vehicles to navigate efficiently without requiring precise environmental or pose information. The algorithm's capability to adapt to uncertainties and produce optimized paths under realistic conditions is a significant contribution.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/28351
https://hdl.handle.net/10256/28351
url http://hdl.handle.net/10256/28351
https://hdl.handle.net/10256/28351
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat de Girona. Institut de Recerca en Visió per Computador i Robòtica
publisher.none.fl_str_mv Universitat de Girona. Institut de Recerca en Visió per Computador i Robòtica
dc.source.none.fl_str_mv Erasmus Mundus Joint Master in Intelligent Field Robotic Systems (IFROS)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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