Realization of Force Detection and Feedback Control for Slave Manipulator of Master/Slave Surgical Robot.

Robot-assisted minimally invasive surgery (MIS) has received increasing attention, both in the academic field and clinical operation. Master/slave control is the most widely adopted manipulation mode for surgical robots. Thus, sensing the force of the surgical instruments located at the end of the slave manipulator through the main manipulator is critical to the operation. This study mainly addressed the force detection of the surgical instrument and force feedback control of the serial surgical robotic arm. A measurement device was developed to record the tool end force from the slave manipulator. An elastic element with an orthogonal beam structure was designed to sense the strain induced by force interactions. The relationship between the acting force and the output voltage was obtained through experiment, and the three-dimensional force output was decomposed using an extreme learning machine algorithm while considering the nonlinearity. The control of the force from the slave manipulator end was achieved. An impedance control strategy was adopted to restrict the force interaction amplitude. Modeling, simulation, and experimental verification were completed on the serial robotic manipulator platform along with virtual control in the MATLAB/Simulink software environment. The experimental results show that the measured force from the slave manipulator can provide feedback for impedance control with a delay of 0.15 s.

Variable Admittance Control Based on Fuzzy Reinforcement Learning for Minimally Invasive Surgery Manipulator.

In order to get natural and intuitive physical interaction in the pose adjustment of the minimally invasive surgery manipulator, a hybrid variable admittance model based on Fuzzy Sarsa(λ)-learning is proposed in this paper. The proposed model provides continuous variable virtual damping to the admittance controller to respond to human intentions, and it effectively enhances the comfort level during the task execution by modifying the generated virtual damping dynamically. A fuzzy partition defined over the state space is used to capture the characteristics of the operator in physical human-robot interaction. For the purpose of maximizing the performance index in the long run, according to the identification of the current state input, the virtual damping compensations are determined by a trained strategy which can be learned through the experience generated from interaction with humans, and the influence caused by humans and the changing dynamics in the robot are also considered in the learning process. To evaluate the performance of the proposed model, some comparative experiments in joint space are conducted on our experimental minimally invasive surgical manipulator.

Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach.

The absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of integrating force sensors on minimally invasive surgical tools in existing systems, a clamping force prediction method based on mechanical clamp blade motion parameters is proposed. The interrelation between clamping force, displacement, compression speed, and the contact area of the clamp blade indenter was analyzed through compression experiments conducted on isolated pig kidney tissue. Subsequently, a prediction model was developed using a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA). This model enables real-time prediction of clamping force, facilitating more accurate estimation of forces between instruments and tissues during surgery. The results indicate that the SSA-optimized model outperforms traditional BP networks and genetic algorithm-optimized (GA) BP models in terms of both accuracy and convergence speed. This study not only provides technical support for enhancing surgical safety and efficiency, but also offers a novel research direction for the design of force feedback systems in minimally invasive surgical robots in the future.

Virtual-force-guided intraoperative ultrasound scanning with online lesion location prediction: A phantom study.

BACKGROUND: In ultrasound-guided minimally invasive surgery (MIS) of tumours, it is crucial to discover the optimal scanning plane (OSP) and organise the MIS scalpel work trajectory in this plane. The OSP can be altered and is challenging to track when the scalpel interacts with deformed tissues. Therefore, tracking the OSP becomes critical in MIS. In master-slave control, virtual force (VF) is used to assist the operator in completing the task. However, most literature assumes that the environment is sufficiently stable. No specific method focuses on tracking the OSP of the lesion within largely deformed tissues. METHODS: This paper used the improved artificial potential field method to establish the VF that could guide the operator to track the OSP. When tissue deformation occurred, an artificial neural network (ANN) was used to predict the target position, guiding the operator to find the new OSP. An experimental robot platform was built to verify the proposed algorithm's effects. Experiments to track the OSP were performed on a phantom. RESULTS: The results showed that the presented method could reduce the trajectory redundancy of ultrasonic scanning, shorten the time of OSP discovery and tracking, and decrease the deviation between the ultrasonic scanning position and the OSP. CONCLUSIONS: This method has significance for the accurate localization and successful removal of tumours. Future work will focus on improving the adaptability of the proposed ANN prediction model in different phantoms.

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.

Structure Design and Workspace Analysis of Robotic Arm for Minimally Invasive Surgical Robot / 医用生物力学

Objective The current manipulator with double parallel quadrilateral mechanism should be connected in series with a flexible degree of freedom (DOF) mechanism, which increases the volume of the manipulator, decreases the motion flexibility and creates the interference between the mechanical arms that hold the mirror and the device. Aimed at solving this problem, a novel mechanical arm was put forward to enhance the motion flexibility and reduce the volume of the manipulator. Methods The mechanical arm was designed by using the mechanism of five-link, slider and slide rail lower pair and wire transmission to realize the telescopic movement of the end effector. The kinematics model of the manipulator was established, and the MATLAB was used as the simulation tool to verify the correctness of the D-H parameters under the specific zero joint angle, and the motion equation of the manipulator was solved. Meanwhile, the three-dimensional workspace of the end effector was obtained by using Monte Carlo algorithm, and the preoperative plan of animal experiment for 3 arms was performed. Finally, cholecystectomy and other operations were acted in pigs, to verify the rationality and maneuverability of the design of double 5-link 2-DOF manipulator. Results The working space of Monte Carlo algorithm under MATLAB environment was -650.4 mm＜x＜649 mm, 163.8 mm＜y＜1 202 mm and -254.6 mm＜z＜829.8 mm. Sixteen cases of pig cholecystectomy were successfully completed, with an average operation time of 51 minutes. Conclusions The novel double 5-link 2-DOF manipulator could successfully complete cholecystectomy and other operations in pigs, which had no other symptoms after the operation. There was no interference between the mechanical arms, which fully verified the feasibility of the design scheme of the robot manipulator for minimally invasive surgery.