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The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist
This paper studies the inverse kinematics of two non-spherical wrist configurations of painting robot. The simplest analytical solution of orthogonal wrist configuration is deduced in this paper for the first time. For the oblique wrist configuration, there is no analytical solution for the configur...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122721/ https://www.ncbi.nlm.nih.gov/pubmed/32243437 http://dx.doi.org/10.1371/journal.pone.0230790 |
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author | Wang, Xiaoqi Cao, Jianfu Chen, Lerui Hu, Heyu |
author_facet | Wang, Xiaoqi Cao, Jianfu Chen, Lerui Hu, Heyu |
author_sort | Wang, Xiaoqi |
collection | PubMed |
description | This paper studies the inverse kinematics of two non-spherical wrist configurations of painting robot. The simplest analytical solution of orthogonal wrist configuration is deduced in this paper for the first time. For the oblique wrist configuration, there is no analytical solution for the configuration. So it is necessary to solve by general method, which cannot achieve high precision and high speed as analytic solution. Two general methods are optimized in this paper. Firstly, the elimination method is optimized to reduce the solving speed to 20% of the original one, and the completeness of the method is supplemented. Based on the Gauss damped least squares method, a new optimization method is proposed to improve the solving speed. The enhanced step length coefficient is introduced to conduct studies with the machine learning correlation method. It has been proved that, on the basis of ensuring the stability of motion, the number of iterations can be effectively reduced and the average number of iterations can be less than 5 times, which can effectively improve the speed of solution. In the simulation and experimental environment, it is verified. |
format | Online Article Text |
id | pubmed-7122721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-71227212020-04-09 The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist Wang, Xiaoqi Cao, Jianfu Chen, Lerui Hu, Heyu PLoS One Research Article This paper studies the inverse kinematics of two non-spherical wrist configurations of painting robot. The simplest analytical solution of orthogonal wrist configuration is deduced in this paper for the first time. For the oblique wrist configuration, there is no analytical solution for the configuration. So it is necessary to solve by general method, which cannot achieve high precision and high speed as analytic solution. Two general methods are optimized in this paper. Firstly, the elimination method is optimized to reduce the solving speed to 20% of the original one, and the completeness of the method is supplemented. Based on the Gauss damped least squares method, a new optimization method is proposed to improve the solving speed. The enhanced step length coefficient is introduced to conduct studies with the machine learning correlation method. It has been proved that, on the basis of ensuring the stability of motion, the number of iterations can be effectively reduced and the average number of iterations can be less than 5 times, which can effectively improve the speed of solution. In the simulation and experimental environment, it is verified. Public Library of Science 2020-04-03 /pmc/articles/PMC7122721/ /pubmed/32243437 http://dx.doi.org/10.1371/journal.pone.0230790 Text en © 2020 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Xiaoqi Cao, Jianfu Chen, Lerui Hu, Heyu The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist |
title | The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist |
title_full | The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist |
title_fullStr | The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist |
title_full_unstemmed | The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist |
title_short | The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist |
title_sort | optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122721/ https://www.ncbi.nlm.nih.gov/pubmed/32243437 http://dx.doi.org/10.1371/journal.pone.0230790 |
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