Predictive control algorithms using biological signals for active relative motion canceling in robotic assisted heart surgery

Abstract

Robotics technology promises an enhanced way of performing off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, surgeon performs the operation with intelligent robotic instruments controlled through teleoperation in place of conventional surgical tools. The robotic tools actively cancel the relative motion between the surgical instruments and the point-of-interest on the beating heart, in contrast to traditional off-pump CABG where the heart is passively constrained to dampen the beating motion. As a result, the surgeon operates on the heart as if it were stationary. This algorithm is called active relative motion canceling (ARMC). In this paper, the use of biological signals, such as electrocardiogram (ECG), to achieve better motion canceling in the model-based intelligent ARMC algorithm is proposed. An ECG contains records for the electrical activity of the heart, which forms a series of waves and complexes. Real time identification of these waves and complexes improve the estimation of the future heart motion and improve the performance of the ARMC algorithm. Finally, the experimental results of the algorithm implemented on a 3-DOF robotic test-bed system are reported