Deep Learning Model for the Recognition of Its Environment of an Intelligent System

Jesús Ocaña; Guillermo Miñan; Luis Chauca; Víctor Ancajima; Luis Leiva

doi:10.1007/978-3-031-48858-0_28

Deep Learning Model for the Recognition of Its Environment of an Intelligent System

Jesús Ocaña
, Guillermo Miñan
, Luis Chauca
, Víctor Ancajima
, Luis Leiva

Academic Department of Education and Culture

Universidad Tecnológica del Perú
Universidad Católica Los Ángeles de Chimbote

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This research project consisted in the design of an Artificial Neural Network with Deep Learning for an intelligent system, they were installed twelve sensors ultrasonic HC-SR04, which ones detected and learned all kinds of obstacles, such as: walls, tables, chairs and others. The methodology used was the concurrent design has five phases: the first the conceptual plan was carried out, the second a kinematic study, the third a dynamic study, the fourth a mechanical project and finally the simulation of the system. Artificial Neural Networks were designed with Deep Learning and trained with the Backpropagation algorithm. The ANN was programmed and recorded in the Arduino Mega 2560 module. All the corresponding simulations were carried out, it was verified that the ultrasonic sensors have sent the signal to the Artificial Neural Network with deep learning and they carried out a learning of their environment, they were also checked the displacement of the mobile robot resulting in the desired performance. In conclusion, the proposed design was achieved and it was simulated with all kinds of events, in addition it was verified that Artificial Neural Networks with deep learning can detect and learn from all the obstacles that are in their environment.

Original language	English
Title of host publication	Advanced Research in Technologies, Information, Innovation and Sustainability - 3rd International Conference, ARTIIS 2023, Proceedings
Editors	Teresa Guarda, Filipe Portela, Jose Maria Diaz-Nafria
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	352-363
Number of pages	12
ISBN (Print)	9783031488573
DOIs	https://doi.org/10.1007/978-3-031-48858-0_28
State	Published - 2024
Event	3rd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023 - Madrid, Spain Duration: 18 Oct 2023 → 20 Oct 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1935 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023
Country/Territory	Spain
City	Madrid
Period	18/10/23 → 20/10/23

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Artificial neural networks
Deep Learning
mobile robot
security

Access to Document

10.1007/978-3-031-48858-0_28

Cite this

Ocaña, J., Miñan, G., Chauca, L., Ancajima, V., & Leiva, L. (2024). Deep Learning Model for the Recognition of Its Environment of an Intelligent System. In T. Guarda, F. Portela, & J. M. Diaz-Nafria (Eds.), Advanced Research in Technologies, Information, Innovation and Sustainability - 3rd International Conference, ARTIIS 2023, Proceedings (pp. 352-363). (Communications in Computer and Information Science; Vol. 1935 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-48858-0_28

Ocaña, Jesús ; Miñan, Guillermo ; Chauca, Luis et al. / Deep Learning Model for the Recognition of Its Environment of an Intelligent System. Advanced Research in Technologies, Information, Innovation and Sustainability - 3rd International Conference, ARTIIS 2023, Proceedings. editor / Teresa Guarda ; Filipe Portela ; Jose Maria Diaz-Nafria. Springer Science and Business Media Deutschland GmbH, 2024. pp. 352-363 (Communications in Computer and Information Science).

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Ocaña, J, Miñan, G, Chauca, L, Ancajima, V & Leiva, L 2024, Deep Learning Model for the Recognition of Its Environment of an Intelligent System. in T Guarda, F Portela & JM Diaz-Nafria (eds), Advanced Research in Technologies, Information, Innovation and Sustainability - 3rd International Conference, ARTIIS 2023, Proceedings. Communications in Computer and Information Science, vol. 1935 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 352-363, 3rd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023, Madrid, Spain, 18/10/23. https://doi.org/10.1007/978-3-031-48858-0_28

Deep Learning Model for the Recognition of Its Environment of an Intelligent System. / Ocaña, Jesús; Miñan, Guillermo; Chauca, Luis et al.
Advanced Research in Technologies, Information, Innovation and Sustainability - 3rd International Conference, ARTIIS 2023, Proceedings. ed. / Teresa Guarda; Filipe Portela; Jose Maria Diaz-Nafria. Springer Science and Business Media Deutschland GmbH, 2024. p. 352-363 (Communications in Computer and Information Science; Vol. 1935 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning Model for the Recognition of Its Environment of an Intelligent System

AU - Ocaña, Jesús

AU - Miñan, Guillermo

AU - Chauca, Luis

AU - Ancajima, Víctor

AU - Leiva, Luis

PY - 2024

Y1 - 2024

N2 - This research project consisted in the design of an Artificial Neural Network with Deep Learning for an intelligent system, they were installed twelve sensors ultrasonic HC-SR04, which ones detected and learned all kinds of obstacles, such as: walls, tables, chairs and others. The methodology used was the concurrent design has five phases: the first the conceptual plan was carried out, the second a kinematic study, the third a dynamic study, the fourth a mechanical project and finally the simulation of the system. Artificial Neural Networks were designed with Deep Learning and trained with the Backpropagation algorithm. The ANN was programmed and recorded in the Arduino Mega 2560 module. All the corresponding simulations were carried out, it was verified that the ultrasonic sensors have sent the signal to the Artificial Neural Network with deep learning and they carried out a learning of their environment, they were also checked the displacement of the mobile robot resulting in the desired performance. In conclusion, the proposed design was achieved and it was simulated with all kinds of events, in addition it was verified that Artificial Neural Networks with deep learning can detect and learn from all the obstacles that are in their environment.

AB - This research project consisted in the design of an Artificial Neural Network with Deep Learning for an intelligent system, they were installed twelve sensors ultrasonic HC-SR04, which ones detected and learned all kinds of obstacles, such as: walls, tables, chairs and others. The methodology used was the concurrent design has five phases: the first the conceptual plan was carried out, the second a kinematic study, the third a dynamic study, the fourth a mechanical project and finally the simulation of the system. Artificial Neural Networks were designed with Deep Learning and trained with the Backpropagation algorithm. The ANN was programmed and recorded in the Arduino Mega 2560 module. All the corresponding simulations were carried out, it was verified that the ultrasonic sensors have sent the signal to the Artificial Neural Network with deep learning and they carried out a learning of their environment, they were also checked the displacement of the mobile robot resulting in the desired performance. In conclusion, the proposed design was achieved and it was simulated with all kinds of events, in addition it was verified that Artificial Neural Networks with deep learning can detect and learn from all the obstacles that are in their environment.

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Ocaña J, Miñan G, Chauca L, Ancajima V, Leiva L. Deep Learning Model for the Recognition of Its Environment of an Intelligent System. In Guarda T, Portela F, Diaz-Nafria JM, editors, Advanced Research in Technologies, Information, Innovation and Sustainability - 3rd International Conference, ARTIIS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 352-363. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-48858-0_28

Deep Learning Model for the Recognition of Its Environment of an Intelligent System

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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