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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.
2005 (English)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, 2005. , 10 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2677
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-56016ISRN: LiTH-ISY-R-2677OAI: oai:DiVA.org:liu-56016DiVA: diva2:316715
Projects
Vinnova Excellence Center LINK-SICSSF project Collaborative Localization
Funder
VinnovaSwedish Foundation for Strategic Research
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-13Bibliographically approved
In thesis
1. Particle filtering for positioning and tracking applications
Open this publication in new window or tab >>Particle filtering for positioning and tracking applications
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A Bayesian approach to positioning and tracking applications naturally leads to a recursive estimation formulation. The recently invented particle filter provides a numerical solution to the non-tractable recursive Bayesian estimation problem. As an alternative, traditional methods such as the extended Kalman filter. which is based on a linearized model and an assumption on Gaussian noise, yield approximate solutions.

In many practical applications, signal quantization and algorithmic complexity are fundamental issues. For measurement quantization, estimation performance is analyzed in detail. The algorithmic complexity is addressed for the marginalized particle filter, where the Kalman filter solves a linear subsystem subject to Gaussian noise efficiently.

The particle filter is adopted to several positioning and tracking applications and compared to traditional approaches. Particularly, the use of external database information to enhance estimation performance is discussed. In parallel, fundamental limits are derived analytically or numerically using the Cramér-Rao lower bound, and the result from estimation studies is compared to the corresponding lower bound. A framework for map-aided positioning at sea is developed, featuring an underwater positioning system using depth information and readings from a sonar sensor and a novel surface navigation system using radar measurements and sea chart information. Bayesian estimation techniques are also used to improve position accuracy for an industrial robot. The bearings-only tracking problem is addressed using Bayesian techniques and map information is used to improve the estimation performance. For multiple-target tracking problems data association is an important issue. A method to incorporate classical association methods when the estimation is based on the particle filter is presented. A real-time implementation of the particle filter as well as hypothesis testing is introduced for a collision avoidance application.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University Electronic Press, 2005. 55 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 924
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-29608 (URN)14987 (Local ID)91-85297-34-8 (ISBN)14987 (Archive number)14987 (OAI)
Public defence
2005-03-18, Sal Visionen, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-11-29Bibliographically approved

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Karlsson, RickardNorrlöf, Mikael

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CiteExportLink to record
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Citation style
  • apa
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