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Tool Position Estimation of a Flexible Industrial Robot using Recursive Bayesian Methods
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2011 (English)Report (Other academic)
Abstract [en]

A sensor fusion method for state estimation of a flexible industrial robot is presented. By measuring the acceleration at the end-effector, the accuracy of the arm angular position is improved significantly when these measurements are fused with motor angle observation. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; one using the extended Kalman filter (EKF) and one using the particle filter (PF). The technique is verified on experiments on the ABB IRB4600 robot, where the accelerometer method is showing a significant better dynamic performance, even when model errors are present.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 6 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3024
Keyword [en]
Estimation, Extended Kalman Filter, Particle Filter, Accelerometer, Industrial Robot
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-88972ISRN: LiTH-ISY-R-3024OAI: oai:DiVA.org:liu-88972DiVA: diva2:606579
Projects
Vinnova Excellence Center LINK-SICSSF project Collaborative Localization
Funder
VinnovaSwedish Foundation for Strategic Research
Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2014-06-18Bibliographically approved

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Tool Position Estimation of a Flexible Industrial Robot using Recursive Bayesian Methods(648 kB)139 downloads
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Axelsson, PatrikKarlsson, RickardNorrlöf, Mikael

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Language
  • de-DE
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