NDLI: Modeling complex robotic assembly process using Gaussian Process Regression

Content Provider	IEEE Xplore Digital Library
Author	Binbin Li Hongtai Cheng Heping Chen Tongdan Jin
Copyright Year	2014
Description	Author affiliation: Dept. of Mech. Eng. & Autom., Northeastern Univ., Shenyang, China (Hongtai Cheng) \|\| Ingram Sch. of Eng., Texas State Univ., San Marcos, TX, USA (Binbin Li; Heping Chen; Tongdan Jin)
Abstract	In the high precision robotic assembly processes, the process parameters have to be tuned in order to adapt to variations and satisfy the performance requirements. For complex systems and processes operating in a stochastic environment such as the assembly processes, experiments and evaluations could be costly and low efficiency because of resource utilization, energy consumption, and dedicated labor. In order to improve the assembly process performance, we investigate the modeling problem for robotic assembly processes. Gaussian Process Regression, a non-parametric modeling technique, is chosen to model the relationship between the assembly process parameters and performance. The main challenge in implementing Gaussian Process Regression is to find suitable covariance functions which can minimize the modeling errors. Therefore we investigated different combinations of basic covariance functions and implemented them to explore the most suitable covariance function for an assembly process. The performance of the built models is compared and the covariance functions with the best performance are identified. An off-line modeling algorithm is appropriately developed using the identified covariance function. The effectiveness and accuracy of the proposed algorithm are further demonstrated by experiments, which were performed using a robotic valve body assembly process.
Starting Page	456
Ending Page	461
File Size	9918790
Page Count	6
File Format	PDF
e-ISBN	9781479943159
DOI	10.1109/ICIEA.2014.6931207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Assembly Data models Valves Ground penetrating radar Gaussian processes Robots Predictive models hyperparameters parameter optimization Gaussian Process Regression covariance function
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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