Li Yangming, Miyasaka Muneaki, Haghighipanah Mohammad, Cheng Lei, Hannaford Blake
Department of Electrical Engineering, University of Washington, Seattle, WA, USA 98195.
Department of Mechanical Engineering, University of Washington, Seattle, WA, USA 98195.
IEEE Int Conf Robot Autom. 2016 May;2016:4128-4134. doi: 10.1109/icra.2016.7487605. Epub 2016 Jun 9.
Haptic feedback plays a key role in surgeries, but it is still a missing component in robotic Minimally Invasive Surgeries. This paper proposes a dynamic model-based sensorless grip force estimation method to address the haptic perception problem for commonly used elongated cable-driven surgical instruments. Cable and cable-pulley properties are studied for dynamic modeling; grip forces, along with driven motor and gripper jaw positions and velocities are jointly estimated with Unscented Kalman Filter and only motor encoder readings and motor output torques are assumed to be known. A bounding filter is used to compensate for model inaccuracy and to improve method robustness. The proposed method was validated on a 10mm gripper which is driven by a Raven-II surgical robot. The gripper was equipped with 1-dimensional force sensors which served as ground truth data. The experimental results showed that the proposed method provides sufficiently good grip force estimation, while only motor encoder and the motor torques are used as observations.
触觉反馈在手术中起着关键作用，但在机器人微创手术中仍是一个缺失的组成部分。本文提出了一种基于动态模型的无传感器握力估计方法，以解决常用细长电缆驱动手术器械的触觉感知问题。研究了电缆和电缆滑轮的特性用于动态建模；使用无迹卡尔曼滤波器联合估计握力、驱动电机以及夹爪的位置和速度，并且仅假设电机编码器读数和电机输出扭矩是已知的。使用边界滤波器来补偿模型不准确并提高方法的鲁棒性。所提出的方法在由Raven-II手术机器人驱动的10mm夹爪上进行了验证。该夹爪配备了一维力传感器，用作真实数据。实验结果表明，所提出的方法提供了足够好的握力估计，同时仅将电机编码器和电机扭矩用作观测值。
