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Learning optimal measurement and control of assembly robot for large-scale heavy-weight parts
Wan A(万安); Xu J(徐静); Zhang, Song; Zhang, Zonghua; Chen K(陈恳)
Department机器人学研究室
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
Pages1240-1246
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161802327762
WOS IDWOS:000380476200208
Contribution Rank1
ISBN978-1-4673-9674-5
AbstractDue to their advantages of high speed, high accuracy, high flexibility, and low cost, assembly robots are widely used in electronics and automotive industries. However, it is still a significant challenge for large-scale, heavy-weight part assembly using industrial robots. First, the deformation and motion errors of industrial robots caused by big payload cannot meet the accuracy requirement of large structure assembly. To solve this problem, an online kinematics compensation method based on Gaussian Process Regression is developed to predict and compensate the deformation and uncertainties of a large structure assembly robot. Second, before the assembly process, the optimal assembly path has to be planned. To this end, we propose an assembly path planning method based on learning from demonstration. Finally, an event-based control method is deployed to achieve optimal assembly cycle time to improve assembly efficiency and performance. An experimental system is developed to validate the proposed algorithm for large structure assembly and the results demonstrate that the proposed method can improve the assembly efficiency by more than 40%.
Language英语
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19181
Collection机器人学研究室
Affiliation1.Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
2.State key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
3.School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, United States
4.School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Recommended Citation
GB/T 7714
Wan A,Xu J,Zhang, Song,et al. Learning optimal measurement and control of assembly robot for large-scale heavy-weight parts[C]. Piscataway, NJ, USA:IEEE,2015:1240-1246.
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