Autonomous Navigation Strategy for Drones in Indoor Environments Using Neural Networks
DOI:
https://doi.org/10.11606/issn.2526-8260.mecatrone.2024.232404Keywords:
Drones, Autonomous navigation, Neural networks, Indoor localizationAbstract
Drones are unmanned aerial vehicles, and their use in indoor environments has several applications, such as logistics, storage, and security. Autonomous navigation facilitates access, increases efficiency, and reduces operational costs. However, this type of application introduces positioning challenges, primarily due to the inaccuracy of GPS signals, which is the main method implemented in open environments. In this context, the development of a localization strategy based on convolutional neural network models is proposed, which are responsible for trajectory classification that informs the decision-making process of the system’s control algorithm.
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ARAFAT, M. Y.; ALAM, M. M.; MOH, S. Vision-based navigation techniques for unmanned aerial vehicles: Review and challenges. Drones, MDPI, v. 7, 2 2023. ISSN 2504446X.
ASHRAF, S. et al. A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment. International Journal of Distributed Sensor Networks, SAGE Publications Ltd, v. 14, 6 2018. ISSN 15501477.
CHANG, Y. et al. A review of UAV autonomous navigation in GPS-denied environments. [S.l.]: Elsevier B.V., 2023.
CHHIKARA, P. et al. Dcnn-ga: A deep neural net architecture for navigation of uav in indoor environment. IEEE Internet of Things Journal, Institute of Electrical and Electronics Engineers Inc., v. 8, p. 4448–4460, 3 2021. ISSN 23274662.
DJI Tello drone controller python package <https://github.com/hanyazou/TelloPy/tree/develop-0.7.0>. Acesso em: 21 nov. 2024.
DJITELLOPY API Reference <https://djitellopy.readthedocs.io/en/latest/>. Acesso em: 28 jun. 2024.
GARCIA, A. et al. A convolutional neural network feature detection approach to autonomous quadrotor indoor navigation. In: . [S.l.: s.n.], 2019. ISBN 9781728140049.
KEIPOUR, A. et al. Visual servoing approach to autonomous uav landing on a moving vehicle. Sensors, MDPI, v. 22, 9 2022. ISSN 14248220.
PEREIRA, S. C. R. A. Controle de drone por posição com decolagem rotacionada. 2024. Acesso em: 29 nov. 2024. Disponível em: <https://youtu.be/BS3JrjfHCTksi=T8CQW0FnR7B_unjE>.
PEREIRA, S. C. R. A. Controle de drone por posição com realização de curva no corredor.
Acesso em: 29 nov. 2024. Disponível em: <https://youtu.be/6NGsMPNJGzo?si=dAl4wOxFU1ZsZZ8x>.
PEREIRA, S. C. R. A. Controle de drone por velocidade com decolagem centralizada. 2024. Acesso em: 29 nov. 2024. Disponível em: <https://youtu.be/vaveyRGZkOs?si=loQ_k8KqbxShNti8>.
PEREIRA, S. C. R. A. Controle de drone por velocidade com realização de curva no corredor. 2024. Acesso em: 29 nov. 2024. Disponível em: <https://youtu.be/jgwsQm02n88?si=170TZ54bBhtkrBX_>.
ZHANG, X. et al. Part-based multi-task deep network for autonomous indoor drone navigation. Transactions of the Institute of Measurement and Control, SAGE Publications Ltd, v. 42, p. 3243–3253, 12 2020. ISSN 14770369
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Copyright (c) 2024 Sophia Celine Rafael Alves Pereira, Fábio Calça Carvalho
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
