Motion Hysteresis Compensation Based on Motor Current Segmentation for Elongated Cable-Driven Surgical Instruments

Abstract

Cable-driven surgical instrument is a reusable and disposable component of Robot-assisted Minimally Invasive Surgery, and it is time-consuming to identify the motion hysteresis compensation model of each instrument in large-scale identification. Besides, the identification results failure caused by the loss of cable tension or lubrication conditions during repeated usage interferes with the accuracy of motion hysteresis compensation. To simplify the identification process and lengthen the instrument life, this paper leverages the relationship between actuate motor current and hysteresis phases, developing a novel motion hysteresis compensation method to compensate the motor reference trajectory. Firstly, prior knowledges including motor current and hysteresis curves of several instruments are collected to train the Hysteresis Identification model and Curve Generation model. Once the two models are well-trained in practical use, the only sensor data in need is the motor current for the Hysteresis Identification model, and the compensation curve will be generated pertinently for the instrument under current use. Finally, Feedforward Compensation scheme is conducted to compensate the reference trajectory of the actuate motor. Experiments study the range of hysteresis compensation errors when the method is applied to numerous surgical instruments with different motion hysteresis, and the feasibility and accuracy are verified. The method can be potentially applied to a wide range of cable-driven mechanisms facing the problem of large-scale identification or the identification results failure after repeated use. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes a feasible motion hysteresis compensation method for cable-driven mechanism (CDM). For some CDMs like elongated cable-driven surgical instrument, their motion hysteresis characteristics will change during repeated use, and their distal angle and cable tension are not available in the environment of use. In these cases, the proposed method enables re-identification because the distal angle or cable tension are not needed during the practical use of our method. Besides, it is easy and low-cost for large-scale identification due to the simplicity of identification procedure. Once the Hysteresis Identification model and Curve Generation model are trained using the pre-collected prior knowledge including motor current and the angle of the end effector, the only sensor data in need to identify and compensate a certain CDM is the its motor current. Then, the pertinent compensation curve for the CDM device can be generated for the actuate motor to follow. The experimental results reveal that the competitive performance with other state-of-the-arts can be achieved. In the future research, we will integrate dynamic characteristics of CDM into the method to expand the scope of application.