Neural network-based physiological organ motion prediction and robot impedance control for teleoperated beating-heart surgery

摘要

Compared to conventional arrested heart surgery, beating-heart surgery is promising as the advantages of eliminating adverse effects caused by a heart-lung bypass machine and enabling intraoperative evaluation of heart motion. However, the fast motion of the heart introduces a significant challenge for beating-heart surgery. In this paper, a teleoperation system, which employs an impedance control for the master robot and an ultrasound image-based position control for the slave robot (surgical robot), is proposed to achieve non-oscillatory force feedback and heart motion compensation, respectively. Specifically, an impedance model is designed for the master robot to provide the human operator (surgeon) with non-oscillatory haptic feedback. To compensate for the beating heart's motion, ultrasound imaging is used to obtain the position of the point of interest (POI) on the heart tissue. As the use of ultrasound imaging introduces non-negligible time delay caused by image acquisition and processing, a recurrent neural network (NN)-based physiological organ motion predictor is proposed. The predicted POI position is used to control the slave robot to automatically compensate for the beating heart's motion. The proposed method is validated through experiments. The proposed control strategy with NN-based heart motion predictor is compared to the other two strategies without heart motion predictor and with an extended Kalman filter (EKF)-based heart motion predictor. The experimental results present that the proposed strategy with NN algorithm shows significant advantages (higher synchronization accuracy and relatively steady slave-heart contact force) over the other two strategies.