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Bayesian State Estimation of a Flexible Industrial Robot
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 (Swedish)Report (Other academic)
Abstract [en]

A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 9 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3027
Keyword [en]
Industrial robot, Positioning, Estimation, Particle filter, Extended Kalman filter, Cramér-Rao lower bound
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-88975ISRN: LiTH-ISY-R-3027OAI: oai:DiVA.org:liu-88975DiVA: diva2:606581
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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Axelsson, PatrikKarlsson, RickardNorrlöf, Mikael

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Citation style
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