手術用ペグ移しの自動化：深層学習によるキャリブレーションが人間の速度・精度・一貫性を超える
Automating Surgical Peg Transfer: Calibration with Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans
da Vinci研究用ロボットと深度センサを用い、深層学習によるキャリブレーションと時間最小化制御でペグ移しタスクを自律化し、熟練外科医や被験者より高速・高精度・高一貫性を達成した。
著者: Minho Hwang, Jeffrey Ichnowski, Brijen Thananjeyan, Daniel Seita, Samuel Paradis, Danyal Fer, Thomas Low, Ken Goldberg
分類: cs.RO
原文アブストラクト
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.
