Modern surgical tools provide no advanced features like automated error avoidance or diagnostic information regarding the tissues they interact with. This work motivates and presents the design of a “smart” laparoscopic surgical grasper that can identify the tissue it is grasping while the grasp is occurring. This allows automated prevention of certain errors like crush injury. A nonlinear dynamical model of tissue mechanics is adopted along with an extended Kalman filter to demonstrate the feasibility of this design in simulation and <italic>in situ</italic and <italic>in vivo</italic> on porcine models. Results indicate that while the approach is sensitive to initial conditions, tissue can be identified during the first 0.3s of a grasp.
University of Minnesota M.S.M.E. thesis. August 2013. Major: Mechanical Engineering. Advisor: Timothy Kowalewski. 1 computer file (PDF); xii, 95 pages.
Online Identification of Abdominal Tissues During Grasping Using an Instrumented Laparoscopic Grasper.
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