DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김연훈 | ko |
dc.contributor.author | 정재원 | ko |
dc.contributor.author | 김수현 | ko |
dc.contributor.author | 곽윤근 | ko |
dc.date.accessioned | 2013-02-27T20:53:55Z | - |
dc.date.available | 2013-02-27T20:53:55Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1999-07 | - |
dc.identifier.citation | 한국정밀공학회지, v.16, no.7, pp.66 - 72 | - |
dc.identifier.issn | 1225-9071 | - |
dc.identifier.uri | http://hdl.handle.net/10203/70790 | - |
dc.description.abstract | Robot calibration is very important to improve the accuracy of robot manipulators. However, the calibration procedure is very time consuming and laborious work for users. In this paper, we propose a method of relative error compensation to make the calibration procedure easier. The method is completed by a Pi-Sigma network architecture which has sufficient capability to approximate the relative relationship between the accuracy compensations and robot configurations while maintaining an efficient network learning ability. By experiment of 4-DOF SCARA robot, KIRO-3, it is shown that both the error of joint angles and the positioning error of end effector are drop to 15%. These results are similar to those of other calibration methods, but the number of measurement is remarkably decreased by the suggested compensation method. | - |
dc.language | Korean | - |
dc.publisher | 한국정밀공학회 | - |
dc.title | 신경 회로망을 이용한 로봇의 상대 오차 보상 | - |
dc.title.alternative | Relative Error Compensation of Robot Using Neural Network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.citation.volume | 16 | - |
dc.citation.issue | 7 | - |
dc.citation.beginningpage | 66 | - |
dc.citation.endingpage | 72 | - |
dc.citation.publicationname | 한국정밀공학회지 | - |
dc.contributor.localauthor | 김수현 | - |
dc.contributor.localauthor | 곽윤근 | - |
dc.contributor.nonIdAuthor | 김연훈 | - |
dc.contributor.nonIdAuthor | 정재원 | - |
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