Iterative Kalman filter for biological tissue identification

摘要

Abstract Dynamic soft tissue identification plays an important role in robotic‐assisted minimally invasive surgery to achieve realistic force feedback for precise and safe surgical operations. This article studies a dynamic soft tissue identification method by combination of the Hunt Crossley contact model with an iterated Kalman filter. The dynamic system equation of tool‐tissue interaction is constructed by combining mechanical tool's kinematic dynamics with the Hunt Crossley contact dynamics. Upon this, an iterative Kalman filter is developed for online soft tissue identification by integrating the maximum a posteriori principle into the Kalman filtering framework to optimize the posterior state estimate to account for the strong nonlinearity of the Hunt Crossley contact model. Results and comparison analysis demonstrate that the proposed method can effectively identify the Hunt‐Crossley model parameters, resulting in improved accuracy compared with the conventional recursive least square method and extended Kalman filter.