Off-pump totally endoscopic coronary artery bypass grafting is a milestone for cardiac surgery, and still a technical challenge. Indeed, the fast and complex cardiac motion makes this operating method technically demanding. Therefore, several robotic systems have been designed to assist the surgeons by compensating for the cardiac motion and providing a virtually motionless operating area. In the proposed systems, the servoing schemes often take advantage of a prediction algorithm that supplies the controller with some future heart motion. This prediction enlarges the control-loop bandwidth, thus allowing a better motion compensation. Obviously, improving the prediction accuracy will lead to better motion-compensation results. Thus, a current challenge in computer-assisted cardiac surgery research is the design of efficient heart-motion-prediction algorithms. In this paper, a detailed survey of the main existing prediction approaches is given and a classification is provided. Then, a novel prediction technique based on amplitude modulation is proposed, and compared with other techniques using in vivo collected datasets. A final discussion summarizes the main features of all the proposed approaches.
An alternative approach to standard computed torque with feedback linearization is proposed in this work to control cable-driven parallel robots (CDPRs) with highly flexible cables. Exteroceptive feedback is used to measure the end-effector Cartesian position at a high sampling rate. Stability is demonstrated using singular perturbation theory. The proposed control scheme is experimentally validated on a planar 3-degree-of-freedom CDPR and its efficiency is assessed by comparison to a simple kinematic control law.
Abstract. Active robotic filtering is a promising solution for beating heart Totally Endoscopic Coronary Artery Bypass Grafting (TECABG). In this work, we assess the heart motion dynamics using simultaneously high speed imaging of optical markers attached to the heart, ECG signals and ventilator airflow acquisitions. Our goal is to make an assessment of the heart motion (shape, velocity, acceleration) in order to be able to make more accurate specifications for a dedicated robot that could follow this motion in real-time. Furthermore, using the 2 additional inputs (ECG, airflow), we propose a prediction algorithm of the motion that could be used with a predictive control algorithm to improve the tracking accuracy.
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