Physiological Organ Motion Prediction and Compensation Based on Multirate, Delayed, and Unregistered Measurements in Robot-Assisted Surgery and Therapy


Physiological motion makes performing a surgical or therapeutic procedure a lot of difficult for the physician. In heart surgery, the heart is stopped as it's too difficult for the surgeon to follow the heart's beating motion and perform a surgical task. In radiation therapy, respiration causes the cancerous tissue to maneuver, rendering the therapy less effective and probably damaging to healthy tissue. This paper focuses on controlling a robot, that is used to perform the surgery or therapy, to compensate for the physiological motion along the surgical tool's axis such that the point of interest (POI) on the organ becomes stationary relative to the robot. The problem in creating such a system lies within the measurement of the POI's and robot's positions via different sensors that are unregistered, have totally different measurement rates, and have knowledge acquisition and processing delays. This paper presents the Kalman-filter-based mostly estimation of the organ motion despite the massive information acquisition/processing delays and low update rates inherent in some measurements used for robot management in robot-assisted surgeries and therapies. This paper additionally proposes Control Systems that make amends for the organ motion despite the delayed, multirate, and unregistered sensor data permitting the physician to perform a therapeutic or surgical treatment with a teleoperated robot on a seemingly stationary POI.

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