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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Stochastic Force-Based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery
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Stochastic Force-Based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery

机译：基于随机力的插入深度和尖端位置估计的机器人视网膜手术中的柔性FBG仪器

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摘要

Vitreoretinal surgery is among the most delicate surgical tasks during which surgeon hand tremor may severely attenuate surgeon performance. Robotic assistance has been demonstrated to be beneficial in diminishing hand tremor. Among the requirements for reliable assistance from the robot is to provide precise measurements of system states, e.g., sclera forces, tool tip position, and tool insertion depth. Providing this and other sensing information using existing technology would contribute toward development and implementation of autonomous robot-assisted tasks in retinal surgery such as laser ablation, guided suture placement/assisted needle vessel cannulation, among other applications. In this article, we use a state-estimating Kalman filtering (KF) to improve the tool tip position and insertion depth estimates, which used to be purely obtained by robot forward kinematics (FWK) and direct sensor measurements, respectively. To improve tool tip localization, in addition to robot FWK, we also use sclera force measurements along with beam theory to account for tool deflection. For insertion depth, the robot FWK is combined with sensor measurements for the cases where sensor measurements are not reliable enough. The improved tool tip position and insertion depth measurements are validated using a stereo camera system through preliminary experiments and a case study. The results indicate that the tool tip position and insertion depth measurements are significantly improved by 77% and 94% after applying KF, respectively.
机译：Vitoretinal手术是最精致的手术任务之一，外科医生手势可能会严重衰减外科医生性能。已经证明了机器人援助在减少手中越来越令人有益。从机器人提供可靠援助的要求是提供系统状态的精确测量，例如巩膜力，工具尖端位置和工具插入深度。提供现有技术的其他感测信息将有助于开发和实施视网膜手术中的自主机器人辅助任务，例如激光烧蚀，引导的缝合放置/辅助针血管插管等应用。在本文中，我们使用状态估计卡尔曼滤波（KF）来改善工具尖端位置和插入深度估计，其分别通过机器人前进运动学（FWK）和直接传感器测量来纯粹获得。为了提高工具尖端本地化，除了机器人FWK之外，我们还使用Sclera Force测量以及光束理论来解释刀具偏转。对于插入深度，机器人FWK与传感器测量相结合，用于传感器测量不可能的情况。通过初步实验和案例研究，使用立体声相机系统验证改进的工具尖端位置和插入深度测量。结果表明，在施加KF后，工具尖位置和插入深度测量分别在77％和94％下显着提高了77％和94％。

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