Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications
Joint Authors
Liu, Nan
Li, Liangyu
Hao, Bing
Yang, Liusong
Hu, Tonghai
Xue, Tao
Wang, Shoujun
Shao, Xingmao
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-30
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Nan& Li, Liangyu& Hao, Bing& Yang, Liusong& Hu, Tonghai& Xue, Tao…[et al.]. 2020. Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications. Complexity،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1145365
Modern Language Association (MLA)
Liu, Nan…[et al.]. Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications. Complexity No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1145365
American Medical Association (AMA)
Liu, Nan& Li, Liangyu& Hao, Bing& Yang, Liusong& Hu, Tonghai& Xue, Tao…[et al.]. Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications. Complexity. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1145365
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1145365