Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM

Torres-Hernández, Mayra A.; Ibarra-Pérez, Teodoro; García-Sánchez, Eduardo; Guerrero-Osuna, Héctor A.; Solís-Sánchez, Luis O.; Martínez-Blanco, Ma. del Rosario

doi:10.3390/technologies13090405

Open AccessArticle

Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM

by

Mayra A. Torres-Hernández

^1,2,3

,

Teodoro Ibarra-Pérez

¹

,

Eduardo García-Sánchez

²,

Héctor A. Guerrero-Osuna

²

,

Luis O. Solís-Sánchez

^2,*

and

Ma. del Rosario Martínez-Blanco

^2,3,*

¹

Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería Campus Zacatecas (UPIIZ), Zacatecas 98160, Mexico

²

Posgrado en Ingeniería y Tecnología Aplicada, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico

³

Laboratorio de Inteligencia Artificial Avanzada (LIAA), Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico

^*

Authors to whom correspondence should be addressed.

Technologies 2025, 13(9), 405; https://doi.org/10.3390/technologies13090405

Submission received: 30 May 2025 / Revised: 21 August 2025 / Accepted: 1 September 2025 / Published: 5 September 2025

(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)

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Abstract

This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using data generated through the Denavit–Hartenberg (D-H) model, considering the robot’s workspace. The evaluation employed the mean squared error (MSE) as the loss metric and mean absolute error (MAE) and accuracy as performance metrics. The CNN model, featuring four convolutional layers and an input of 4 timesteps, achieved the best overall performance (95.9% accuracy, MSE of 0.003, and MAE of 0.040), significantly outperforming the LSTM model in training time. A hybrid web application was implemented, allowing offline training and real-time online inference under one second via an interactive interface developed with Streamlit 1.16. The solution integrates tools such as TensorFlow™ 2.15, Python 3.10, and Anaconda Distribution 2023.03-1, ensuring portability to fog or cloud computing environments. The proposed system stands out for its fast response times (1 s), low computational cost, and high scalability to collaborative robotics environments. It is a viable alternative for applications in educational or research settings, particularly in projects focused on industrial automation.

Keywords: deep learning; kinematics; web system; CNN; LSTM; Python

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MDPI and ACS Style

Torres-Hernández, M.A.; Ibarra-Pérez, T.; García-Sánchez, E.; Guerrero-Osuna, H.A.; Solís-Sánchez, L.O.; Martínez-Blanco, M.d.R. Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM. Technologies 2025, 13, 405. https://doi.org/10.3390/technologies13090405

AMA Style

Torres-Hernández MA, Ibarra-Pérez T, García-Sánchez E, Guerrero-Osuna HA, Solís-Sánchez LO, Martínez-Blanco MdR. Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM. Technologies. 2025; 13(9):405. https://doi.org/10.3390/technologies13090405

Chicago/Turabian Style

Torres-Hernández, Mayra A., Teodoro Ibarra-Pérez, Eduardo García-Sánchez, Héctor A. Guerrero-Osuna, Luis O. Solís-Sánchez, and Ma. del Rosario Martínez-Blanco. 2025. "Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM" Technologies 13, no. 9: 405. https://doi.org/10.3390/technologies13090405

APA Style

Torres-Hernández, M. A., Ibarra-Pérez, T., García-Sánchez, E., Guerrero-Osuna, H. A., Solís-Sánchez, L. O., & Martínez-Blanco, M. d. R. (2025). Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM. Technologies, 13(9), 405. https://doi.org/10.3390/technologies13090405

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Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM

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