Robotik-destekli kalp ameliyatları için uyabilen tahmin algoritmalarına dayalı kalp hareketi tahmini

Robotic assisted beating heart surgery aims to allow surgeons to operate on a beatingheart without stabilizers as if the heart is stationary. The robot actively cancels heartmotion by closely following a point of interest (POI) on the heart surface?a processcalled Active Relative Motion Canceling (ARMC). Due to the high bandwidth of thePOI motion, it is necessary to supply the controller with an estimate of the immediatefuture of the POI motion over a prediction horizon in order to achieve sufficienttracking accuracy. In this thesis two prediction algorithms, using an adaptive filterto generate future position estimates, are studied. In addition, the variation in heartrate on tracking performance is studied and the prediction algorithms are evaluatedusing a 3 degrees of freedom test-bed with prerecorded heart motion data.Besides this, a probabilistic robotics approach is followed to model and characterizenoise of the sensor system that collects heart motion data used in this study. Thegenerated model is employed to filter and clean the noisy measurements collectedfrom the sensor system. Then, the filtered sensor data is used to localize POI onthe heart surface accurately. Finally, estimates obtained from the adaptive predictionalgorithms are integrated to the generated measurement model with the aim ofimproving the performance of the presented approach.