Model Predictive Control for Cancellation of Repetitive Organ Motions in Robotized Laparoscopic Surgery

Abstract

Periodic deformations of organs which are due to respiratory movements may be critical disturbances for surgeons manipulating robotic control systems during laparoscopic interventions or tele-surgery. Indeed, the surgeon has to manually compensate for these motions if accurate gestures are needed, like, e.g., during suturing. This work presents a model predictive control scheme that is applied to the problem of maintaining a constant distance in the endoscopic images from a surgical tool’s tip to the organ’s surface. A new optimization criterion is developed for an unconstrained generalized predictive controller based on a repetitive input-output model, where contributions of the control input to reference tracking and to disturbance rejection are split and computed separately. Thanks to this approach, mechanical filtering of the repetitive disturbances and teleoperation by the surgeon can run simultaneously and independently on the robot arm. The system is tested on both an endotraining box with a surgical robot and in in vivo conditions on a living pig. Results are shown to validate the control scheme and its application.