Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications
In smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. However, the physical modeling methods cannot include the nondeterministic factors of the manipulator, such as flexibility, joint clearance, and friction. In th...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9053715 |
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| _version_ | 1849412623959851008 |
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| author | Nan Liu Liangyu Li Bing Hao Liusong Yang Tonghai Hu Tao Xue Shoujun Wang Xingmao Shao |
| author_facet | Nan Liu Liangyu Li Bing Hao Liusong Yang Tonghai Hu Tao Xue Shoujun Wang Xingmao Shao |
| author_sort | Nan Liu |
| collection | DOAJ |
| description | In smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. However, the physical modeling methods cannot include the nondeterministic factors of the manipulator, such as flexibility, joint clearance, and friction. In this paper, the Semiparametric Deep Learning (SDL) method is proposed to model robot inverse dynamics. SDL is a type of deep learning framework, designed for optimal inference, combining the Rigid Body Dynamics (RBD) model and Nonparametric Deep Learning (NDL) model. The SDL model takes advantage of the global characteristics of classic RBD and the powerful fitting capabilities of the deep learning approach. Moreover, the parametric and nonparametric parts of the SDL model can be optimized at the same time instead of being optimized separately. The proposed method is validated using experiments, performed on a UR5 robotic platform. The results show that the performance of SDL model is better than that of RBD model and NDL model. SDL can always provide relatively accurate joint torque prediction, even when the RBD or NDL model is not accurate. |
| format | Article |
| id | doaj-art-5f356ce0846444eea1bbd1ee137a8543 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5f356ce0846444eea1bbd1ee137a85432025-08-20T03:34:22ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/90537159053715Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial ApplicationsNan Liu0Liangyu Li1Bing Hao2Liusong Yang3Tonghai Hu4Tao Xue5Shoujun Wang6Xingmao Shao7School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, ChinaSchool of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, ChinaCITIC Heavy Industries Co., Ltd., Luoyang 471003, ChinaCITIC Heavy Industries Co., Ltd., Luoyang 471003, ChinaCITIC Heavy Industries Co., Ltd., Luoyang 471003, ChinaNational Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, ChinaNational Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, ChinaNational Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, ChinaIn smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. However, the physical modeling methods cannot include the nondeterministic factors of the manipulator, such as flexibility, joint clearance, and friction. In this paper, the Semiparametric Deep Learning (SDL) method is proposed to model robot inverse dynamics. SDL is a type of deep learning framework, designed for optimal inference, combining the Rigid Body Dynamics (RBD) model and Nonparametric Deep Learning (NDL) model. The SDL model takes advantage of the global characteristics of classic RBD and the powerful fitting capabilities of the deep learning approach. Moreover, the parametric and nonparametric parts of the SDL model can be optimized at the same time instead of being optimized separately. The proposed method is validated using experiments, performed on a UR5 robotic platform. The results show that the performance of SDL model is better than that of RBD model and NDL model. SDL can always provide relatively accurate joint torque prediction, even when the RBD or NDL model is not accurate.http://dx.doi.org/10.1155/2020/9053715 |
| spellingShingle | Nan Liu Liangyu Li Bing Hao Liusong Yang Tonghai Hu Tao Xue Shoujun Wang Xingmao Shao Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications Complexity |
| title | Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications |
| title_full | Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications |
| title_fullStr | Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications |
| title_full_unstemmed | Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications |
| title_short | Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications |
| title_sort | semiparametric deep learning manipulator inverse dynamics modeling method for smart city and industrial applications |
| url | http://dx.doi.org/10.1155/2020/9053715 |
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