JIN, Zujin ;CHENG, Gang ;XU, Shichang ;YUAN, Dunpeng . Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 68, n.3, p. 175-184, march 2022. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/error-prediction-for-large-optical-mirror-processing-robot-based-on-deep-learning-2/>. Date accessed: 26 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2021.7455.
Jin, Z., Cheng, G., Xu, S., & Yuan, D. (2022). Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning. Strojniški vestnik - Journal of Mechanical Engineering, 68(3), 175-184. doi:http://dx.doi.org/10.5545/sv-jme.2021.7455
@article{sv-jmesv-jme.2021.7455,
author = {Zujin Jin and Gang Cheng and Shichang Xu and Dunpeng Yuan},
title = {Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {68},
number = {3},
year = {2022},
keywords = {Bayesian optimization; BO-LSTM; error prediction; optical mirror processing; hybrid manipulator; hyperparametrics; },
abstract = {Predicting the errors of a large optical mirror processing robot (LOMPR) is very important when studying a feedforward control error compensation strategy to improve the motion accuracy of the LOMPR. Therefore, an end trajectory error prediction model of a LOMPR based on a Bayesian optimized long short-term memory (BO-LSTM) was established. First, the batch size, number of hidden neurons and learning rate of LSTM were optimized using a Bayesian method. Then, the established prediction models were used to predict the errors in the X and Y directions of the spiral trajectory of the LOMPR, and the prediction results were compared with those of back-propagation (BP) neural network. The experimental results show that the training time of the BO-LSTM is reduced to 21.4 % and 15.2 %, respectively, in X and Y directions than that of the BP neural network. Moreover, the MSE, RMSE, and MAE of the prediction error in the X direction were reduced to 9.4 %, 30.5 %, and 31.8 %, respectively; the MSE, RMSE, and MAE of the prediction error in the Y direction were reduced to 9.6 %, 30.8 %, and 37.8 %, respectively. It is verified that the BO-LSTM prediction model could improve not only the accuracy of the end trajectory error prediction of the LOMPR but also the prediction efficiency, which provides a research basis for improving the surface accuracy of an optical mirror.},
issn = {0039-2480}, pages = {175-184}, doi = {10.5545/sv-jme.2021.7455},
url = {https://www.sv-jme.eu/article/error-prediction-for-large-optical-mirror-processing-robot-based-on-deep-learning-2/}
}
Jin, Z.,Cheng, G.,Xu, S.,Yuan, D. 2022 March 68. Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 68:3
%A Jin, Zujin %A Cheng, Gang %A Xu, Shichang %A Yuan, Dunpeng %D 2022 %T Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning %B 2022 %9 Bayesian optimization; BO-LSTM; error prediction; optical mirror processing; hybrid manipulator; hyperparametrics; %! Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning %K Bayesian optimization; BO-LSTM; error prediction; optical mirror processing; hybrid manipulator; hyperparametrics; %X Predicting the errors of a large optical mirror processing robot (LOMPR) is very important when studying a feedforward control error compensation strategy to improve the motion accuracy of the LOMPR. Therefore, an end trajectory error prediction model of a LOMPR based on a Bayesian optimized long short-term memory (BO-LSTM) was established. First, the batch size, number of hidden neurons and learning rate of LSTM were optimized using a Bayesian method. Then, the established prediction models were used to predict the errors in the X and Y directions of the spiral trajectory of the LOMPR, and the prediction results were compared with those of back-propagation (BP) neural network. The experimental results show that the training time of the BO-LSTM is reduced to 21.4 % and 15.2 %, respectively, in X and Y directions than that of the BP neural network. Moreover, the MSE, RMSE, and MAE of the prediction error in the X direction were reduced to 9.4 %, 30.5 %, and 31.8 %, respectively; the MSE, RMSE, and MAE of the prediction error in the Y direction were reduced to 9.6 %, 30.8 %, and 37.8 %, respectively. It is verified that the BO-LSTM prediction model could improve not only the accuracy of the end trajectory error prediction of the LOMPR but also the prediction efficiency, which provides a research basis for improving the surface accuracy of an optical mirror. %U https://www.sv-jme.eu/article/error-prediction-for-large-optical-mirror-processing-robot-based-on-deep-learning-2/ %0 Journal Article %R 10.5545/sv-jme.2021.7455 %& 175 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 68 %N 3 %@ 0039-2480 %8 2022-03-15 %7 2022-03-15
Jin, Zujin, Gang Cheng, Shichang Xu, & Dunpeng Yuan. "Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning." Strojniški vestnik - Journal of Mechanical Engineering [Online], 68.3 (2022): 175-184. Web. 26 Dec. 2024
TY - JOUR AU - Jin, Zujin AU - Cheng, Gang AU - Xu, Shichang AU - Yuan, Dunpeng PY - 2022 TI - Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7455 KW - Bayesian optimization; BO-LSTM; error prediction; optical mirror processing; hybrid manipulator; hyperparametrics; N2 - Predicting the errors of a large optical mirror processing robot (LOMPR) is very important when studying a feedforward control error compensation strategy to improve the motion accuracy of the LOMPR. Therefore, an end trajectory error prediction model of a LOMPR based on a Bayesian optimized long short-term memory (BO-LSTM) was established. First, the batch size, number of hidden neurons and learning rate of LSTM were optimized using a Bayesian method. Then, the established prediction models were used to predict the errors in the X and Y directions of the spiral trajectory of the LOMPR, and the prediction results were compared with those of back-propagation (BP) neural network. The experimental results show that the training time of the BO-LSTM is reduced to 21.4 % and 15.2 %, respectively, in X and Y directions than that of the BP neural network. Moreover, the MSE, RMSE, and MAE of the prediction error in the X direction were reduced to 9.4 %, 30.5 %, and 31.8 %, respectively; the MSE, RMSE, and MAE of the prediction error in the Y direction were reduced to 9.6 %, 30.8 %, and 37.8 %, respectively. It is verified that the BO-LSTM prediction model could improve not only the accuracy of the end trajectory error prediction of the LOMPR but also the prediction efficiency, which provides a research basis for improving the surface accuracy of an optical mirror. UR - https://www.sv-jme.eu/article/error-prediction-for-large-optical-mirror-processing-robot-based-on-deep-learning-2/
@article{{sv-jme}{sv-jme.2021.7455},
author = {Jin, Z., Cheng, G., Xu, S., Yuan, D.},
title = {Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {68},
number = {3},
year = {2022},
doi = {10.5545/sv-jme.2021.7455},
url = {https://www.sv-jme.eu/article/error-prediction-for-large-optical-mirror-processing-robot-based-on-deep-learning-2/}
}
TY - JOUR AU - Jin, Zujin AU - Cheng, Gang AU - Xu, Shichang AU - Yuan, Dunpeng PY - 2022/03/15 TI - Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 68, No 3 (2022): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7455 KW - Bayesian optimization, BO-LSTM, error prediction, optical mirror processing, hybrid manipulator, hyperparametrics, N2 - Predicting the errors of a large optical mirror processing robot (LOMPR) is very important when studying a feedforward control error compensation strategy to improve the motion accuracy of the LOMPR. Therefore, an end trajectory error prediction model of a LOMPR based on a Bayesian optimized long short-term memory (BO-LSTM) was established. First, the batch size, number of hidden neurons and learning rate of LSTM were optimized using a Bayesian method. Then, the established prediction models were used to predict the errors in the X and Y directions of the spiral trajectory of the LOMPR, and the prediction results were compared with those of back-propagation (BP) neural network. The experimental results show that the training time of the BO-LSTM is reduced to 21.4 % and 15.2 %, respectively, in X and Y directions than that of the BP neural network. Moreover, the MSE, RMSE, and MAE of the prediction error in the X direction were reduced to 9.4 %, 30.5 %, and 31.8 %, respectively; the MSE, RMSE, and MAE of the prediction error in the Y direction were reduced to 9.6 %, 30.8 %, and 37.8 %, respectively. It is verified that the BO-LSTM prediction model could improve not only the accuracy of the end trajectory error prediction of the LOMPR but also the prediction efficiency, which provides a research basis for improving the surface accuracy of an optical mirror. UR - https://www.sv-jme.eu/article/error-prediction-for-large-optical-mirror-processing-robot-based-on-deep-learning-2/
Jin, Zujin, Cheng, Gang, Xu, Shichang, AND Yuan, Dunpeng. "Error Prediction for Large Optical Mirror Processing Robot Based on Deep Learning" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 68 Number 3 (15 March 2022)
Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 175-184
© The Authors 2022. CC BY 4.0 Int.
Predicting the errors of a large optical mirror processing robot (LOMPR) is very important when studying a feedforward control error compensation strategy to improve the motion accuracy of the LOMPR. Therefore, an end trajectory error prediction model of a LOMPR based on a Bayesian optimized long short-term memory (BO-LSTM) was established. First, the batch size, number of hidden neurons and learning rate of LSTM were optimized using a Bayesian method. Then, the established prediction models were used to predict the errors in the X and Y directions of the spiral trajectory of the LOMPR, and the prediction results were compared with those of back-propagation (BP) neural network. The experimental results show that the training time of the BO-LSTM is reduced to 21.4 % and 15.2 %, respectively, in X and Y directions than that of the BP neural network. Moreover, the MSE, RMSE, and MAE of the prediction error in the X direction were reduced to 9.4 %, 30.5 %, and 31.8 %, respectively; the MSE, RMSE, and MAE of the prediction error in the Y direction were reduced to 9.6 %, 30.8 %, and 37.8 %, respectively. It is verified that the BO-LSTM prediction model could improve not only the accuracy of the end trajectory error prediction of the LOMPR but also the prediction efficiency, which provides a research basis for improving the surface accuracy of an optical mirror.