Adaptive state estimation for 4-wheel steerable industrial vehicles

Addresses the multi-sensor data fusion problem in the navigation of a four-wheel steerable industrial vehicle with substantial load variations. An adaptive estimation approach based on the extended Kalman filter is used to realise the multi-model filtering. The vehicle plant is represented using a m...

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Bibliographic Details
Published inProceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171) Vol. 4; pp. 4509 - 4514 vol.4
Main Authors Yew Keong Tham, Han Wang, Eam Khwang Teoh
Format Conference Proceeding
LanguageEnglish
Published IEEE 1998
Subjects
Adaptive estimation
Adaptive filters
Filtering
Kinematics
Load management
Navigation
Position measurement
State estimation
Vehicles
Wheels
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ISBN9780780343948
0780343948
ISSN0191-2216
DOI10.1109/CDC.1998.762031

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Summary:Addresses the multi-sensor data fusion problem in the navigation of a four-wheel steerable industrial vehicle with substantial load variations. An adaptive estimation approach based on the extended Kalman filter is used to realise the multi-model filtering. The vehicle plant is represented using a modified kinematic model to effectively describe the side-slip which causes the vehicle to deviate from its ideal course. In view of the large mass variations and wheels' deflections, a method to constantly calibrate the odometry encoder's resolution is proposed to maintain an accurate position report even with long dead-reckoning distance. The position measurements from a landmark-based local reference system are fused with the odometry measurements to provide an optimal estimate of the vehicle's states. The filter performance is evaluated at different speeds and loading patterns using data obtained from field trials.
ISBN:9780780343948
0780343948
ISSN:0191-2216
DOI:10.1109/CDC.1998.762031