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An improved scheme for eliminating the coupled motion of surgical instruments used in laparoscopic surgical robots

机译：一种改进的方案，用于消除腹腔镜外科手术机器人使用的外科手术器械的耦合运动

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摘要

Considering the nonlinear characteristics such as backlash hysteresis and coupled motion commonly exist in cable-driven mechanism of laparoscopic surgical robot end-effector, it is a great challenge to control the motion of robotic end-effector precisely during the surgical procedure. Due to the effects of coupled motion, the surgical end-effector will not move accurately as surgeons expected. Previous studies mostly focused on the design of special compensation mechanisms and software compensation algorithms to solve coupled motion problem. However, these approaches are limited because the backlash hysteresis is ignored and the mechanism of end-effector is restricted. This paper shows an improved scheme to eliminate the coupled motion of end-effector and reduce the position tracking error. The proposed decoupling scheme is conducted in three stages. Firstly, the time and frequency domain information of the driving motor current and the motion information of surgical instrument are extracted in real-time. Thereafter, a feedforward neural network is designed to identify the movement stage of end-effector. Finally, a prediction model is designed to predict the coupling error, after that the coupling error can be eliminated by using feedforward compensation control. An experimental platform was set up to verify the effectiveness of the proposed control scheme, and the results of corresponding comparative experiments revealed that the proposed strategy can substantially improve the tracking accuracy. (C) 2018 Elsevier B.V. All rights reserved.
机译：考虑到腹腔镜手术机器人末端效应器的电缆驱动机制中通常存在诸如反冲滞后和耦合运动的非线性特性，在外科手术过程中控制机器人末端效应器的运动是一个很大的挑战。由于耦合运动的影响，外科末端效应器不会随着外科医生准确地移动。以前的研究主要集中在设计特殊补偿机制和软件补偿算法的设计中来解决耦合运动问题。然而，这些方法是有限的，因为忽略了反冲滞后并且末端效应器的机制受到限制。本文示出了一种改进的方案，以消除末端执行器的耦合运动并降低位置跟踪误差。所提出的去耦方案是三个阶段进行的。首先，实时提取驱动电动机电流的时间和频域信息和外科手术器械的运动信息。此后，设计前馈神经网络以识别端部执行器的运动级。最后，设计预测模型以预测耦合误差，之后可以通过使用前馈补偿控制来消除耦合误差。建立了一个实验平台以验证所提出的控制方案的有效性，相应的比较实验的结果表明，所提出的策略可以大大提高跟踪精度。 （c）2018 Elsevier B.v.保留所有权利。

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