External force estimation and implementation in robotically assisted minimally invasive surgery.

BACKGROUND: Robotically assisted minimally invasive surgery can offer many benefits over open surgery and laparoscopic minimally invasive surgery. However, currently, there is no force sensing and force feedback. METHODS: This research was implemented using the da Vinci research kit. An external force estimation and implementation method was proposed based on dynamics and motor currents. The dynamics of the Patient Side Manipulator was modeled. The dynamic model was linearly parameterized. The estimation principle of external force was derived. The dynamic parameters were experimentally identified using a least squares method. RESULTS: Several experiments including dynamic parameter identification, joint torque estimation, and external force estimation were performed. The results showed that the proposed method could implement force estimation without using a force sensor. CONCLUSIONS: The force estimation method was proposed and implemented and experimental results showed the method worked and was feasible. This method could be used for force sensing in minimally invasive surgical robotics in the future.

A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive surgery.

BACKGROUND: It is very important for robotically assisted minimally invasive surgery to achieve a high-precision and smooth motion control. However, the surgical instrument tip will exhibit vibration caused by nonlinear friction and unmodeled dynamics, especially when the surgical robot system is attempting low-speed, fine motion. METHODS: A fuzzy neural network sliding mode controller (FNNSMC) is proposed to suppress vibration of the surgical robotic system. Nonlinear friction and modeling uncertainties are compensated by a Stribeck model, a radial basis function (RBF) neural network and a fuzzy system, respectively. RESULTS: Simulations and experiments were performed on a 3 degree-of-freedom (DOF) minimally invasive surgical robot. The results demonstrate that the FNNSMC is effective and can suppress vibrations at the surgical instrument tip. CONCLUSIONS: The proposed FNNSMC can provide a robust performance and suppress the vibrations at the surgical instrument tip, which can enhance the quality and security of surgical procedures.

A zero phase adaptive fuzzy Kalman filter for physiological tremor suppression in robotically assisted minimally invasive surgery.

BACKGROUND: Hand physiological tremor of surgeons can cause vibration at the surgical instrument tip, which may make it difficult for the surgeon to perform fine manipulations of tissue, needles, and sutures. METHODS: A zero phase adaptive fuzzy Kalman filter (ZPAFKF) is proposed to suppress hand tremor and vibration of a robotic surgical system. The involuntary motion can be reduced by adding a compensating signal that has the same magnitude and frequency but opposite phase with the tremor signal. RESULTS: Simulations and experiments using different filters were performed. Results show that the proposed filter can avoid the loss of useful motion information and time delay, and better suppress minor and varying tremor. CONCLUSIONS: The ZPAFKF can provide less error, preferred accuracy, better tremor estimation, and more desirable compensation performance, to suppress hand tremor and decrease vibration at the surgical instrument tip. Copyright © 2016 John Wiley & Sons, Ltd.