Gravity Compensation of the dVRK-Si Patient Side Manipulator based on Dynamic Model Identification

TL;DR

This paper introduces a new dynamic model and gravity compensation method for the dVRK-Si surgical robot's Patient Side Manipulator, improving control accuracy by addressing the effects of increased gravity due to structural upgrades.

Contribution

It develops a comprehensive kinematic model and a model-based gravity compensation technique tailored for the dVRK-Si system, enhancing control performance over previous models.

Findings

Improved control accuracy with gravity compensation

Effective dynamic model identification for dVRK-Si

Enhanced response time in surgical robot control

Abstract

The da Vinci Research Kit (dVRK, also known as dVRK Classic) is an open-source teleoperated surgical robotic system whose hardware is obtained from the first generation da Vinci Surgical System (Intuitive, Sunnyvale, CA, USA). The dVRK has greatly facilitated research in robot-assisted surgery over the past decade and helped researchers address multiple major challenges in this domain. Recently, the dVRK-Si system, a new version of the dVRK which uses mechanical components from the da Vinci Si Surgical System, became available to the community. The major difference between the first generation da Vinci and the da Vinci Si is in the structural upgrade of the Patient Side Manipulator (PSM). Because of this upgrade, the gravity of the dVRK-Si PSM can no longer be ignored as in the dVRK Classic. The high gravity offset may lead to relatively low control accuracy and longer response time. In addition, although substantial progress has been made in addressing the dynamic model identification problem for the dVRK Classic, further research is required on model-based control for the dVRK-Si, due to differences in mechanical components and the demand for enhanced control performance. To address these problems, in this work, we present (1) a novel full kinematic model of the dVRK-Si PSM, and (2) a gravity compensation approach based on the dynamic model identification.