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Improving efficiency with orthogonal exploration for online robotic assembly parameter optimization
Wu BL(吴炳龙); Qu DK(曲道奎); Xu F(徐方)
Department其他
Conference Name2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015
Conference DateDecember 6-9, 2015
Conference PlaceZhuhai, China
Source PublicationProceedings of the 2015 IEEE International Conference on Robotics and Biomimetics
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages958-963
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161802327994
WOS IDWOS:000380476200162
Contribution Rank1
ISBN978-1-4673-9674-5
AbstractIn this paper, an online robotic assembly parameter optimization method is developed, this method make industrial robots can assemble workpiece from unskilled to skilled just like human's learning behavior. With the development of the force sensor technology and force control technology, the industrial robots are used in high precision assembly tasks which are more complicated. In order to ensure the efficiency and success rate of assembly, it is necessary to select the appropriate assembly parameters, this problem is called robotic assembly parameters optimization. The traditional solutions are used by artificial methods, a lot of experiments are carried out to get the optimal parameters, which are very time-consuming and laborious. Especially when the production line changes, using the traditional solutions have to do heavy experiments again, it can't meet the requirements of today's flexible manufacturing requirements. This paper presents an online robotic assembly parameter optimization method, which is called Gaussian Process Regression surrogated Bayesian Optimization Algorithm based on the Orthogonal Exploration (OE-GPRBOA), this method can liberate the labor, does not require artificial participation. The algorithm can optimize the parameters autonomously, finally find the optimal parameters for robotic assembly. For GPR is suitable for processing high dimension, small size of sample and nonlinear complex regression problems, the proposed OE-GPRBOA method can be used for various assembly tasks. In this paper, peg-in-hole assembly experiments are performed. The proposed method also compared with design of experiments (DOE) method and GPRBOA method. Experimental results show that, the proposed OE-GPRBOA method has more efficiency to find the optimal assembly parameters, this method can generate big economic impact.
Language英语
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/18605
Collection其他
AffiliationState Key Laboratory of Robotics, Shenyang Institute of Automation, University of Chinese Academy of Sciences, Shenyang, Liaoning, China
Recommended Citation
GB/T 7714
Wu BL,Qu DK,Xu F. Improving efficiency with orthogonal exploration for online robotic assembly parameter optimization[C]. Piscataway, NJ, USA:IEEE,2015:958-963.
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