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ZHOU Bo
Robotics Laboratory,
Shenyang Institute of Automation,
Chinese Academy of Sciences,
Shenyang 110016, China
Graduate School,
Chinese Academy of Sciences,
Beijing 100039, China
HAN
Jianda
Robotics Laboratory,
Shenyang Institute of Automation,
Chinese Academy of Sciences,
Shenyang 110016, China
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NONLINEAR ESTIMATION METHODS FOR
AUTONOMOUS TRACKED VEHICLE WITH SLIP*
Abstract:
In order to achieve precise, robust autonomous guidance and control of a tracked vehicle, a kinematic model with longitudinal and lateral slip is established. Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly. The first filter is the well-known extended Kalman filter. The second filter is an unscented version of the Kalman filter. The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution. The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies. The four different approaches have different complexities, behavior and advantages that are surveyed and compared.
Key words:
Tracked vehicle Nonlinear estimation Kalman filter Particle filter
Set-membership filter
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