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Article

Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks

by
Mehmet Bahadır Çetinkaya
1,
Kürşat Yildirim
2 and
Şahin Yildirim
1,*
1
Faculty of Engineering, Department of Mechatronics Engineering, University of Erciyes, Kayseri 38039, Turkey
2
Graduate School of Natural and Applied Sciences, University of Erciyes, Kayseri 38039, Turkey
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(13), 4416; https://doi.org/10.3390/s24134416
Submission received: 7 June 2024 / Revised: 4 July 2024 / Accepted: 5 July 2024 / Published: 8 July 2024
(This article belongs to the Section Sensors and Robotics)

Abstract

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision, and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analyses based on artificial neural networks (ANNs) were performed for robot manipulator systems used in the textile industry, and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analyses of a 6-degree-of-freedom (DOF) industrial denim robot manipulator were carried out via four different learning algorithms, delta-bar-delta (DBD), online back propagation (OBP), quick back propagation (QBP), and random back propagation (RBP), for the proposed neural network predictor. From the results obtained, it was observed that the QBP-based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modeling of trajectory control. In addition, the 3-5-6 type ANN structure was also improved, and its root mean square error (RMSE) and statistical R2 performances were compared with that of the 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.
Keywords: industrial robot manipulator; trajectory planning and analysis; artificial neural networks; learning algorithms industrial robot manipulator; trajectory planning and analysis; artificial neural networks; learning algorithms

Share and Cite

MDPI and ACS Style

Çetinkaya, M.B.; Yildirim, K.; Yildirim, Ş. Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks. Sensors 2024, 24, 4416. https://doi.org/10.3390/s24134416

AMA Style

Çetinkaya MB, Yildirim K, Yildirim Ş. Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks. Sensors. 2024; 24(13):4416. https://doi.org/10.3390/s24134416

Chicago/Turabian Style

Çetinkaya, Mehmet Bahadır, Kürşat Yildirim, and Şahin Yildirim. 2024. "Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks" Sensors 24, no. 13: 4416. https://doi.org/10.3390/s24134416

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