Bayesian State Estimation of a Flexible Industrial Robot
2005 (English)Report (Other academic)
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.
LiTH-ISY-R, ISSN 1400-3902 ; 2677
Industrial robot, Positioning, Estimation, Particle filter, Extended Kalman filter, Cramér–Rao lower bound
National CategoryControl Engineering
IdentifiersURN: urn:nbn:se:liu:diva-56016ISRN: LiTH-ISY-R-2677OAI: oai:DiVA.org:liu-56016DiVA: diva2:316715
ProjectsVinnova Excellence Center LINK-SICSSF project Collaborative Localization
FunderVinnovaSwedish Foundation for Strategic Research