Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-Assisted Eye Surgery

Notable challenges during retinal surgery lend themselves to robotic assistance, which has proven beneficial in providing safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing of surgery states (e.g., instrument tip localization and tool-to-tissue interaction forces). Many of the existing tool tip localization methods require preoperative frame registrations or instrument calibrations. In this study, using an iterative approach and by combining vision and force-based methods, we develop calibration- and registration-independent (RI) algorithms to provide online estimates of instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model based on the forward kinematics of the steady-hand eye robot and fiber Bragg grating sensor measurements. This is accomplished using a Kalman filtering approach to improve the deflected instrument tip position estimations during robot-assisted eye surgery. The conducted experiments demonstrate that when the online RI stiffness estimations are used, the instrument tip localization results surpass those obtained from preoperative offline calibrations for stiffness.