Random weighting, strong tracking, and unscented Kalman filter for soft tissue characterization

Shin, J, Zhong, Y, Oetomo, D and Gu, C 2018, 'Random weighting, strong tracking, and unscented Kalman filter for soft tissue characterization', Sensors (Switzerland), vol. 18, no. 5, pp. 1-15.


Document type: Journal Article
Collection: Journal Articles

Title Random weighting, strong tracking, and unscented Kalman filter for soft tissue characterization
Author(s) Shin, J
Zhong, Y
Oetomo, D
Gu, C
Year 2018
Journal name Sensors (Switzerland)
Volume number 18
Issue number 5
Start page 1
End page 15
Total pages 15
Publisher MDPIAG
Abstract This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error.
Subject Control Systems, Robotics and Automation
Keyword(s) Contact model error
Hunt-Crossley model
Random weighting
Soft tissue characterization
Strong tracking
Unscented Kalman filter
DOI - identifier 10.3390/s18051650
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
ISSN 1424-8220
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