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Error prediction for large optical mirror processing robot based on deep learning
ID Jin, Zujin (Author), ID Cheng, Gang (Author), ID Xu, Shichang (Author), ID Yuan, Dunpeng (Author)

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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.

Language:English
Keywords:Bayesian optimization, error prediction, optical mirror processing, hybrid manipulators, hyperparametrics, deep learning
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Publication status:Published
Publication version:Version of Record
Publication date:01.03.2022
Year:2022
Number of pages:Str. 175-184
Numbering:Vol. 68, no. 3
PID:20.500.12556/RUL-136403 This link opens in a new window
UDC:007.52:620.19
ISSN on article:0039-2480
DOI:10.5545/sv-jme.2021.7455 This link opens in a new window
COBISS.SI-ID:105665027 This link opens in a new window
Publication date in RUL:29.04.2022
Views:486
Downloads:85
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Record is a part of a journal

Title:Strojniški vestnik
Shortened title:Stroj. vestn.
Publisher:Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.
ISSN:0039-2480
COBISS.SI-ID:762116 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:09.02.2022
Applies to:Accepted for publication

Secondary language

Language:Slovenian
Title:Napovedovanje napak robotov za obdelavo velikih optičnih zrcal na osnovi globokega učenja
Keywords:Bayesova optimizacija, napovedovanje napak, obdelava optičnih zrcal, hibridni manipulatorji, hiperparametrika, globoko učenej

Projects

Funder:Other - Other funder or multiple funders
Funding programme:Financial support for this work, provided by the Priority Academic Program Development of Jiangsu Higher Education Institutions

Funder:Other - Other funder or multiple funders
Funding programme:National Key R&D Program of China

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