Design of a software platform to generate convolutional neural networks for the parametric identification of a Cartesian robot

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Gonzalez-Arriaga, Daniel Marcelo, Vargas-Trevino, Maria Aurora D., Vergara-Limon, Sergio, De Leon, Carlos Leopoldo Carreon-Diaz, Lopez-Gomez, Jesus, Vargas-Trevino, Marciano, Guerrero-Garcia, Josefina
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Biological neural networks; Cartesian coordinates; Convolutional neural network; Convolutional neural networks; Deep learning; Dynamic models; Heuristic algorithms; Matlab; Neural networks; Parameter estimation; Parameter identification; Robot control; Robots; Similarity; Software; Software design; Torque
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3289078

This article presents a software platform (SP) that helps generate convolutional and feed-forward neural networks and a parametric identification algorithm is proposed for a Cartesian robot using convolutional neural networks (CNN) generated and trained with the proposed SP. The user interface is friendly and aids in developing convolutional and feed-forward neural networks. The SP is made in LabVIEW®. The user does not need the complete knowledge of mathematics used to perform the training or execution of the network or the programming skills to perform the code that performs such tasks. The SP is designed to reduce the deployment time of neural networks, providing the user with a graphical interface that guides the user through the formation and development of the neural network, this is one of the main contributions. The SP interface is described in this article, showing all the options offered. Five neural networks are presented to prove and demonstrate the software platform's use. The dynamic models of the Cartesian robot are used; one has six parameters and the other ten parameters. The maximum similarity achieved by the identification algorithm with the six-parameter dynamic model was 99.15% and 99.92% for the ten-parameter model; both are numerical torque analyses. A real Cartesian robot was identified, and five experimental torques were reconstructed for each axis; the maximum similarity was 97.87%. The proposed algorithm is compared with least squares, which obtain 95.81%. Therefore, the results show that the proposed method generates better parametric identification.