Peg transfer is a well-known surgical training task in the Fundamentals of Laparoscopic Surgery (FLS). While human sur-geons teleoperate robots such as the da Vinci to perform this task with high speed and accuracy, it is challenging to automate. This paper presents a novel system and control method using a da Vinci Research Kit (dVRK) surgical robot and a Zivid depth sensor, and a human subjects study comparing performance on three variants of the peg-transfer task: unilateral, bilateral without handovers, and bilateral with handovers. The system combines 3D printing, depth sensing, and deep learning for calibration with a new analytic inverse kinematics model and a time-minimized motion controller. In a controlled study of 3384 peg transfer trials performed by the system, an expert surgical resident, and 9 volunteers, results suggest that the system achieves accuracy on par with the experienced surgical resident and is significantly faster and more consistent than the surgical resident and volunteers. The system also exhibits the highest consistency and lowest collision rate. To our knowledge, this is the first autonomous system to achieve superhuman performance on a standardized surgical task.
翻译:Peg传输是拉帕罗斯科外科(FLS)基本知识中众所周知的外科培训任务。虽然人类超城远程操作机器人,如达芬奇能够以高速度和准确性完成这项任务,但这是自动化的挑战。本文介绍了使用达芬奇研究工具包(dVRK)外科机器人和Zivid深度传感器的新系统和控制方法,以及一项人体主题研究,比较了外科转移任务的三个变体的性能:单方面的、双边的、不交接的和双边的交接。该系统将3D打印、深度感应和深层校准学习与新的反向运动模型和时间最小运动控制器相结合。在对该系统进行的3384皮克传输试验的控制研究中,一位专家外科住院医生和9名志愿者,结果表明,该系统的准确性与有经验的外科住院病人相当,而且比外科住院者和志愿者要快得多,而且更加一致。该系统也显示了最高的一致性和最低的碰撞率。据我们所知,这是第一个实现超人外科手术性工作的自主系统。