Heart motion prediction based on adaptive estimation aşgorithms fo robotic-assisted beating heart surgery
Author
Tuna, Eser Erdem
Advisor
Çavuşoğlu, Cenk
Date
2011Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
82
views
views
27
downloads
downloads
Abstract
Robotic assisted beating heart surgery aims to allow surgeons to operate on a beating
heart without stabilizers as if the heart is stationary. The robot actively cancels heart
motion by closely following a point of interest (POI) on the heart surface—a process
called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the
POI motion, it is necessary to supply the controller with an estimate of the immediate
future of the POI motion over a prediction horizon in order to achieve sufficient
tracking accuracy. In this thesis two prediction algorithms, using an adaptive filter
to generate future position estimates, are studied. In addition, the variation in heart
rate on tracking performance is studied and the prediction algorithms are evaluated
using a 3 degrees of freedom test-bed with prerecorded heart motion data.
Besides this, a probabilistic robotics approach is followed to model and characterize
noise of the sensor system that collects heart motion data used in this study. The
generated model is employed to filter and clean the noisy measurements collected
from the sensor system. Then, the filtered sensor data is used to localize POI on
the heart surface accurately. Finally, estimates obtained from the adaptive prediction
algorithms are integrated to the generated measurement model with the aim of
improving the performance of the presented approach.
Keywords
Active relative motion cancelingsignal estimation
medical robotics
surgical robotics
probabilistic robotics