This work presents a predictive-control approach to active mechanical filtering of complex, periodic motions of organs induced by respiration or heart beating in robotized surgery. Two different predictive-control schemes are proposed for the compensation of respiratory motions or cardiac motions. For respiratory motions, the periodic property of the disturbance has been included into the input-output model of the controlled system so as to have the robotic system learn and anticipate perturbation motions. A new cost function is proposed for the unconstrained generalized predictive controller (GPC), where reference tracking is decoupled from the rejection of predictable periodic motions. Cardiac motions are more complex, since they are the combination of two periodic nonharmonic components. An adaptive disturbance predictor is proposed which outputs future predicted disturbance values. These predicted values are used to anticipate the disturbance by using the predictive feature of a regular GPC. Experimental results are presented on a laboratory testbed and in vivo on pigs. They demonstrate the effectiveness of the two proposed methods to compensate complex physiological motion.

This work presents first in-vivo results of beating heart tracking with a surgical robot arm in off-pump cardiac surgery. The tracking is performed in a 2D visual servoing scheme using a 500 frame per second video camera. Heart motion is measured by means of active optical markers that are put onto the heart surface. Amplitude of the motion is evaluated along the two axis of the image reference frame. This is a complex and fast motion that mainly reflects the influence of both the respiratory motion and the electro-mechanical activity of the myocardium. A model predictive controller is setup to track the two degrees of freedom of the observed motion by computing velocities for two of the robot joints. The servoing scheme takes advantage of the ability of predictive control to anticipate over future references provided they are known or they can be predicted. An adaptive observer is defined along with a simple cardiac model to estimate the two components of the heart motion. The predictions are then fed into the controller references and it is shown that the tracking behaviour is greatly improved.