Abstract
Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new nonlinear recursive adaptive filtering methodology for online nonlinear soft tissue characterization. An adaptive unscented Kalman filter is developed based on the Hunt-Crossley model by windowing approximation to online estimate system and measurement noise covariances. To improve the accuracy of noise covariance estimations, a recursive formulation is subsequently developed for estimation of system and measurement noise covariances by introducing a weighting factor. This weighting factor is further modified to accommodate noise statistics of large variation which could be caused by rupture events and geometric discontinuities in robotic-assisted surgery. Simulations, experiments, and comparison analyses demonstrate that the proposed nonlinear recursive adaptive filtering methodology can characterize soft tissue parameters in the presence of system or measurement noise statistics in both small and large variations for robotic-assisted surgery. The proposed methodology can effectively estimate soft tissue parameters under system and measurement noises in both small and large variations, leading to improved filtering accuracy and robustness in comparison with UKF.
Highlights
Soft tissue properties are of great importance in robotic minimally invasive surgery to characterize the interaction between surgical tools and soft tissues for robotic control with force feedback
In spite of the less accuracy compared to the continuum mechanics approach, the analytical approach has the advantages of real-time performance, suitable for a robotic control purpose
This paper presents a new nonlinear recursive adaptive unscented Kalman filter (UKF) to estimate the parameters of the H-C contact model
Summary
Soft tissue properties are of great importance in robotic minimally invasive surgery to characterize the interaction between surgical tools and soft tissues for robotic control with force feedback. The typical examples in this category include the viscoelastic model [2] and finite element model (FEM) [3], where the mechanical behaviours of a soft tissue are accurately characterized based on continuum mechanics of elasticity This approach is very complex and involves a large amount of computational load. The existing studies are mainly dominated by the analytical approach, leading to various spring-damper models for online soft tissue characterization. A recursive adaptive UKF is developed by incorporating a weighting factor in the adaptive filtering process to improve the estimation accuracy This weighting factor is further modified to accommodate the noise statistics of large variation, which are occurring in robotic-assisted surgery due to rupture events and geometric discontinuities. Simulation and experimental results together with comparison analysis demonstrate the efficiency of the proposed method for online soft tissue characterization
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