Neural-network-based Algorithm for Cancelling Tremor in Surgical Robots

As a representation of teleoperated robots, surgical robots based on teleoperation are widely applied in the medical field. Generally, it can greatly guarantee the performance of microsurgery in terms of control and postoperative recovery by using a surgical robot in comparison with traditional surgical operation. However, the performance of surgical robots is greatly disturbed by the physiological tremor of surgeon in the process of operation. In order to cancel the impacts of tremor signal, a neural-network-based (NN-based) algorithm is developed in this paper. For the proposed NN-based approach, we develop a hybrid wavelet basis function to deal with the variable tremor signal. Additionally, the proposed method can cancel the tremor signals based on the excellent ability of nonlinear mapping and generalization and does not rely on a priori structural parameters. In order to evaluate the performance of the proposed method, comparative experiments of five different kinds of NN-based tremor filter are performed by using tremor signals with different frequencies and amplitudes. Experimental results validated that the proposed algorithm can achieve the performance of suppressing the tremor signal of the processing error. It is can be noted that the surgical robots can ensure the control performance of the surgical robots by using the developed NN-based filter. The developed method can also be applied as a filter for suppressing vibrations of processing operations in the future, such as chatter in micro-milling.