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.
Highlights
Soft tissue properties are important for robotic-assisted minimally invasive surgery to achieve realistic haptic feedback and stable robotic control
H-Conly contact model nonlinearwith soft computational correcting state covariance in the timeforsegments tissue characterization in the presence of model error. This method identifies model error using the Mahalanobis distance and further incorporates a dynamic scaling factor in predicted state covariance to online compensate identified model error. This scaling factor is determined by combining the principle of innovation orthogonality with the random weighting concept to avoid the cumbersome computation of Jacobian matrix and provide reliable estimation for innovation covariance
This paper presents a new method based on the nonlinear H-C contact model for nonlinear soft tissue characterization in the presence of model error
Summary
Soft tissue properties are important for robotic-assisted minimally invasive surgery to achieve realistic haptic feedback and stable robotic control. It can provide estimations in accuracy of minimum mean-square error Both RLS and KF are a linear estimation algorithm, unsuitable for the use with the nonlinear H-C model for soft tissue characterization [8]. This paper presents a new nonlinear filtering method based on the nonlinear H-C contact model by combining the concepts of ST and random weighting into UKF for online soft tissue characterization. This method adopts the ST concept to address the UKF problem of performance degradation due to contact model error. Simulations, practical experiments, and comparison analysis with UKF have been conducted to comprehensively evaluate the performance of the proposed method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
