An Error Compensation Method for Surgical Robot Based on RCM Mechanism

Ming Bai,He Zhang,Jie Zhao,Chunyan Gao,Minglu Zhang,Linjun Pang

doi:10.1109/access.2021.3117350

Abstract

Remote center of motion (RCM) mechanisms, which are widely used in surgical robots, use mechanical constraints to realize movement around the RCM point. Errors in processing and assembly cause deviation of the RCM point and position error of the end-effector, these deviations cause the robot to exert excessive force on the incision during movement, leading to adverse effects that reduce the safety and accuracy of surgical robots based on the RCM. Based on kinematic calibration, this paper proposes a solution of forward and inverse kinematics considering the RCM point deviation, and this solution can simultaneously reduce the positioning error of the end-effector and the deviation of the RCM point. First, a calibration method based on the combination of the model and neural network is used to improve the kinematic accuracy of the robot. Second, a forward and inverse kinematics solution applied to RCM mechanisms is proposed, and this solution reduces the RCM point deviation during surgery. Finally, experiments were performed with an established surgery robot, verifying that the proposed method can simultaneously reduce the deviation of the RCM point and the position error of the end-effector. The proposed method is found to be superior to other methods and can improve the safety and accuracy of surgical robots.

Highlights

In minimally invasive surgery (MIS), surgical instruments are operated through incision, and the remote center of motion (RCM) mechanism uses mechanical constraints to realize the movement around the remote center of motion point (RCM point) [1]–[3], which provides a fixed point for the robot in the surgical incision, so that the surgical instruments can reach the lesion area through the body surface incision to perform surgical operations [4], [5], this mechanism is widely used in surgical robots
In assisted or semiautonomous surgery based on the Remote center of motion (RCM) mechanism surgical robot, the kinematic errors of the RCM mechanism cause two problems: 1) The position of the RCM point changes with the posture of the RCM mechanism; excessive force exerted by the robot on the incision during movement may lead to soft tissue damage or tearing at the incision [6]–[8] and may even cause movement of the soft tissue near the incision, leading to changes in the location of the lesion
The results show that the calibration method based on the combination of the model and the ANN can effectively improve the kinematic accuracy of the robot by compensating the geometric error and the residual position error

Summary

Introduction

In minimally invasive surgery (MIS), surgical instruments are operated through incision, and the remote center of motion (RCM) mechanism uses mechanical constraints to realize the movement around the remote center of motion point (RCM point) [1]–[3], which provides a fixed point for the robot in the surgical incision, so that the surgical instruments can reach the lesion area through the body surface incision to perform surgical operations [4], [5], this mechanism is widely used in surgical robots. In assisted or semiautonomous surgery based on the RCM mechanism surgical robot, the kinematic errors of the RCM mechanism cause two problems: 1) The position of the RCM point changes with the posture of the RCM mechanism; excessive force exerted by the robot on the incision during movement may lead to soft tissue damage or tearing at the incision [6]–[8] and may even cause movement of the soft tissue near the incision, leading to changes in the location of the lesion. Reducing the positioning error of the end-effector and the deviation of the RCM point is of great significance for improving the safety and accuracy of the surgery [5]–[12]. The DH model proposed by Denavit et al is widely used in robot calibration [15], but it violates the requirement of continuity when the adjacent joints are parallel. Other researchers used screw theory to describe the transformation of joint motion and proposed the POE model [19] and the FIS model

Objectives

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Conclusion