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Zeitschriftenartikel:

S. Chroust, M. Vincze:
"Improvement of the Prediction Quality for Visual Servoing with a Switching Kalman Filter";
International Journal of Robotics Research, 22 (2003), 10-11; S. 905 - 922.



Kurzfassung englisch:
The main control problem of visual servoing is to cope with the
delay introduced by image acquisition and image processing. This
delay is the main reason for limited tracking velocity and acceleration.
Predictive algorithms are one solution to handle the delay. The
drawback of prediction algorithms is the bad prediction behavior for
the discontinuity in the target motion, for example, a velocity step. In
this paper, a switching Kalman filter (SKF) is proposed to overcome
this problem. The SKF introduces three cooperating components.
A prediction monitor supervises the prediction quality of an adaptive
Kalman filter (AKF). If a discontinuity is detected, a transition
filter switches to an appropriate steady-state Kalman filter, which handles a discontinuity better than the AKF. During
this transition, an auxiliary controller ensures that overall control is
continuous. This new prediction algorithm is able to achieve a good
prediction quality for smooth and for discontinuous motions. It is
evaluated using a pan/tilt unit to track a colored object. The SKF
and its components are compared to the classical AKF with four
different target motions.

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.