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Transfer Learning-Based Artificial Neural Network for Forward Kinematic Estimation of 6-DOF Robot

Transfer Learning-Based Artificial Neural Network for Forward Kinematic Estimation of 6-DOF Robot

Kesaba P., Bibhuti Bhusan Choudhury
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 15
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.298314
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MLA

Kesaba P., and Bibhuti Bhusan Choudhury. "Transfer Learning-Based Artificial Neural Network for Forward Kinematic Estimation of 6-DOF Robot." IJAMC vol.13, no.1 2022: pp.1-15. http://doi.org/10.4018/IJAMC.298314

APA

Kesaba P. & Choudhury, B. B. (2022). Transfer Learning-Based Artificial Neural Network for Forward Kinematic Estimation of 6-DOF Robot. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-15. http://doi.org/10.4018/IJAMC.298314

Chicago

Kesaba P., and Bibhuti Bhusan Choudhury. "Transfer Learning-Based Artificial Neural Network for Forward Kinematic Estimation of 6-DOF Robot," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-15. http://doi.org/10.4018/IJAMC.298314

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Abstract

Transfer Learning (TL) can significantly lower training time and reduce dependency on a large number of target domain datasets. Such an approach is still not exploited for robotic prediction tasks. Currently, a TL based Artificial Neural Network (ANN) is explored and validated for robotic forward kinematics estimation of a 6-DOF robot. The robotic positions are estimated from the available joint angle information. The 6-R MTAB Aristo-XT robot is selected as a case study to generate the target experimental training and testing data for validation of ML techniques. While, the PUMA 560 6-DOF robot is used as a source for prior training of the ANN model. Standard performance measures such as learning error, deviation error and Mean Square Error (MSE) are evaluated and graphical illustrations are presented for fair comparison of the results. Experimental results reveal that, instead of ANN, the TL-ANN is strongly suggested to improve the training time of ANN regressor, and it also reduces the randomness and improves the accuracy as compared to its counterpart.