Controlling Tiltrotors Unmanned Aerial Vehicles (UAVs) with Deep Reinforcement Learning

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.creatorDe Almeida, Aline Gabriel-
Autor(es): dc.creatorColombini, Esther Luna-
Autor(es): dc.creatorDa Silva Simoes, Alexandre-
Data de aceite: dc.date.accessioned2025-08-21T18:04:30Z-
Data de disponibilização: dc.date.available2025-08-21T18:04:30Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/LARS/SBR/WRE59448.2023.10333034-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/297822-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/297822-
Descrição: dc.descriptionUnmanned Aerial Vehicles (UAVs) have gained significant attention in various domains due to their versatility and potential applications. Effective control of UAVs is crucial for achieving desired flight behaviors and optimizing their performance. This paper presents a comprehensive exploration of learning-based approaches for controlling UAVs with fixed-rotors and tiltrotors, specifically focusing on the Proximal Policy Optimization (PPO) and Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithms. The study aims to compare and evaluate the efficacy of these two state-of-the-art reinforcement learning algorithms in controlling UAVs with varying designs and control complexities. By utilizing PPO and TD3, we address the challenges associated with maneuvering UAVs in dynamic environments and achieving precise control under different flight conditions. We conducted extensive simulations to assess the performance of PPO and TD3 algorithms in diverse UAV scenarios, considering multiple design configurations and control requirements. The evaluation criteria encompassed stability, robustness, trajectory tracking accuracy, and control efficiency. Results demonstrate the suitability and effectiveness of both PPO and TD3 in controlling UAVs.-
Descrição: dc.descriptionInst. of Science and Tech. of Sorocaba São Paulo State University (Unesp)-
Descrição: dc.descriptionInstitute of Computing University of Campinas (Unicamp)-
Descrição: dc.descriptionInst. of Science and Tech. of Sorocaba São Paulo State University (Unesp)-
Formato: dc.format107-112-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2023 Latin American Robotics Symposium, 2023 Brazilian Symposium on Robotics, and 2023 Workshop of Robotics in Education, LARS/SBR/WRE 2023-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectProximal Policy Optimization (PPO)-
Palavras-chave: dc.subjectReinforcement Learning-
Palavras-chave: dc.subjectTiltrotor-
Palavras-chave: dc.subjectTwin-Delayed Deep Deterministic Policy Gradient (TD3)-
Palavras-chave: dc.subjectUnmanned Aerial Vehicle (UAV)-
Título: dc.titleControlling Tiltrotors Unmanned Aerial Vehicles (UAVs) with Deep Reinforcement Learning-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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