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      • Dept. of Electrical and Electronics Engineering - Master's degree
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      •   BUIR Home
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      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
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      Heart motion prediction based on adaptive estimation aşgorithms fo robotic-assisted beating heart surgery

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      Author
      Tuna, Eser Erdem
      Advisor
      Çavuşoğlu, Cenk
      Date
      2011
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      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 canceling
      signal estimation
      medical robotics
      surgical robotics
      probabilistic robotics
      Permalink
      http://hdl.handle.net/11693/15269
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      • Dept. of Electrical and Electronics Engineering - Master's degree 595
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