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Novel Optimization-Based Design and Surgical Evaluation of a Treaded Robotic Capsule Colonoscope

机译:基于新的优化的螺纹机器人胶囊结肠镜检查的设计和外科评价

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Robotic capsule endoscopes (RCEs) are being widely investigated to improve the state of various endoscopy procedures. This article presents the novel design of a multi-DOF sensor-enabled RCE for colonoscopies (Endoculus) and evaluates porcine in vivo and ex vivo performance. The novelty of the design includes a custom “double-worm” drive that removes axial gear forces while reducing radial moments, and the full parameterization of gear geometries allows for size minimization via an optimization routine over design constraints. Two independently controlled motors drive micro-pillared treads above and below the device allowing for two-degrees of freedom (2-DOF) skid-steering, even in a collapsed lumen. The Endoculus contains all functionality of a traditional endoscope: a camera, adjustable light emitting diodes (LEDs), channels for insufflation and irrigation, and a tool port for endoscopy instruments (e.g., forceps, snares, etc.). Additionally, the Endoculus carries an inertial measurement unit, magnetometer, motor encoders, and motor current sensors to aid in future autonomy strategies. Porcine surgical evaluation demonstrated locomotion up to 40 mm/s on the colon mucosa, 2-DOF steering, the ability to traverse haustral folds, and functionality of endoscopy tools. This platform will enable future validation of feedback control, localization, and mapping algorithms in the unconventional in vivo environment.
机译:广泛研究机器人胶囊内窥镜(RCE)以改善各种内窥镜检查程序的状态。本文介绍了一种用于结肠镜(内腔)的多DOF传感器RCE的新颖设计,并评估猪在vivo 中> 和<斜体xmlns:mml =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http://www.w3.org/1999/xlink”> exvivo 表现。该设计的新颖性包括定制的“双蜗杆”驱动器,该驱动器除去轴向齿轮力,同时减少径向时刻,齿轮几何的完整参数化允许通过优化例程在设计约束上进行尺寸最小化。两个独立控制的电动机驱动微柱的胎面在装置上方和下方,允许两度自由(2-DOF)滑动转向,即使在坍塌的内腔中也是如此。内腔包含传统内窥镜的所有功能:相机,可调节发光二极管(LED),用于灌溉和灌溉的通道,以及用于内窥镜检查仪器的工具端口(例如,镊子,粪便等)。另外,内腔带有惯性测量单元,磁力计,电动机编码器和电机电流传感器,以帮助未来的自主策略。猪手术评估在结肠粘膜上展示了高达40毫米/秒的运动,2-DOF转向,遍历外镜折叠的能力,以及内窥镜检查工具的功能。该平台将在非传统中验证反馈控制,本地化和映射算法的未来验证在vivo 中> 环境。

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