Improving backdrivability in preoperative manual manipulability of minimally invasive surgery robot

Abstract

Purpose The purpose of this paper is to propose a control algorithm to improve the backdrivability performance of minimally invasive surgical robotic arms, so that precise manual manipulations of robotic arms can be performed in the preoperative operation. Design/methodology/approach First, the flexible-joint dynamic model of the 3-degree of freedom remote center motion (RCM) mechanisms of minimally invasive surgery (MIS) robot is derived and its dynamic parameters and friction parameters are identified. Next, the angular velocities and angular accelerations of joints are estimated in real time by the designed Kalman filter. Finally, a control algorithm based on Kalman filter is proposed to enhance the backdrivability of RCM mechanisms by compensating for the internally generated gravitational, frictional and inertial resistances experienced during the positioning and orientating. Findings The parameter identification for RCM mechanisms can be experimentally evaluated from comparison between the measured torques and the reconstructed torques. The accuracy and convergence of the real-time estimation of angular velocity and acceleration of the joint by the designed Kalman filter can be verified from corresponding simulation experiments. Manual adjustment experiments and animal experiments validate the effectiveness of the proposed backdrivability control algorithm. Research limitations/implications The backdrivability control algorithm presented in this paper is a universal method to enhance the manual operation performance of robots, which can be used not only in the medical robot preoperative manual manipulation but also in robot haptic interaction, industrial robot direct teaching and active rehabilitation training of rehabilitation robot and so on. Originality/value Compared with other backdrivability design methods, the proposed algorithm achieves good backdrivability for RCM mechanisms without using force sensors and accelerometers. In addition, this paper presents a new static friction compensation approach for a joint moving with very low velocity.

A resistance torque compensation algorithm based on Luenberger observer for minimally invasive surgical robot

“…Compared with the methods mentioned in (Zou et al , 2018), the proposed algorithm has less computational complexity. This is because the Luenberger observer requires only summation and multiplication operations.…”

“…Experiment results demonstrate that the surgical robot has good backdrivability. (Zou et al , 2018). The Luenberger observer has been proven effective in estimating dynamic information to achieve inertia force and friction compensation (Prisco et al , 2018).…”

“…The essence of the momentum observer is a first-order low-pass filter (Xiao et al , 2018). Similar to Zou et al (2018), filters may cause system delays, which are unsuitable for real-time control of surgical robot.…”

“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0143-991X.htm force by resistance torque compensation (Schabowsky et al, 2010;Zou et al, 2018). Various resistance torque compensation methods have been proposed, which can typically be divided into design-based and control-based methods.…”

Trajectory Correction Method of Motion Description Language of Vertebral Milling Robot based on Force Feedback

“…At this stage, with the practical application of milling robots, force feedback technology has become a very important information interaction technology for milling robots [13][14][15]. The introduction of force feedback into the milling robot system will increase the amount of information feedback and the milling accuracy of the doctor, which in turn will improve the milling quality, reducing misoperation, and shortening the milling operation time [16]. There are two types of force feedback methods for milling robots, direct feedback and perception substitution [17].…”