Needle shape, diameter, and path are critical parameters that directly affect suture depth and tissue trauma in autonomous suturing. This paper presents an optimization-based approach to specify these parameters. Given clinical suturing guidelines, a kinematic model of needle-tissue interaction was developed to quantify suture parameters and constraints. The model was further used to formulate constant curvature needle path planning as a nonlinear optimization problem. The optimization results were confirmed experimentally with the Raven II surgical system. The proposed needle path planning algorithm guarantees minimal tissue trauma and complies with a wide range of suturing requirements.
PinPoint: Monocular Needle Pose Estimation for Robotic Suturing via Stein Variational Newton and Geometric Residuals
Department of Mechanical Engineering, Vanderbilt University（范德比尔特大学机械工程系）；Department of Electrical and Computer Engineering and Department of Computer Science, Vanderbilt University（范德比尔特大学电气与计算机工程系和计算机科学系）；The Robotics Center and the Kahlert School of Computing, University of Utah（犹他大学机器人中心和Kahlert计算学院）
Reeling It In: Flexible Needle Pick Up via Thread Manipulation for Autonomous Suturing
University of California San Diego（加利福尼亚大学圣迭戈分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；Carnegie Mellon University（卡内基梅隆大学）；UC San Diego Health（加州大学圣迭戈分校健康中心）
Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland, College Park（马里兰大学帕克分校）；Weinberg Medical Physics, Inc.（温伯格医学物理公司）
针的实时视觉定位对于各种手术应用是必要的，包括手术自动化和视觉反馈。在这项研究中，我们调查的背景下，我们的磁共振系统的针的定位和自主机器人控制。我们的系统具有手术操作的潜力，具有微创和减少患者副作用的优点。然而，非线性磁场会产生非直观的力，并需要精细的基于位置的控制，这超出了人类直接操纵的能力。这使得自动针定位成为必要。我们的定位方法结合了基于神经网络的分割和经典技术，并且我们能够在清洁环境中以0.73 mm RMS误差和在具有血液和闭塞的挑战性环境中以2.72 mm RMS误差一致地定位我们的针。对于我们在实验中使用的所有环境，平均定位RMS误差为2.16 mm。我们结合我们的闭环反馈控制系统的本地化方法，以证明本地化自主控制的进一步适用性。我们的缝针能够在（1）无血液，无组织;（2）大量血液，无组织;（3）无血液，有组织;和（4）大量血液，有组织环境中遵循连续缝合路径。尖端位置跟踪误差范围为2.6 mm至3.7 mm RMS，为自主导航任务打开了大门。
