Bayesian state estimation of a flexible industrial robot [Elektronisk resurs] / Rickard Karlsson, Mikael Norrlöf.
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Karlsson, Rickard, 1970- (författare)
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- Norrlöf, Mikael, 1971- (författare)
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Alternativt namn: Norrlöf, Using iterative learning control to get better performance of robot control systems / Mikael Norrlöf, 1971-
- Publicerad: Linköping : Linköping University Electronic Press, 2005
- Engelska 10 s.
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Serie: LiTH-ISY-R, 1400-3902 ; 2677
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Läs hela texten (Sammanfattning och fulltext från Linköping University Electronic Press)
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- 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.
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- Industrial robot
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