Saeidi H, Ge J, Kam M, Opfermann J D, Leonard S, Joshi A S, Krieger A
Mechanical Engineering Department, University of Maryland, College Park, MD 20742, USA., Fischell Institute for Biomedical Devices and the Marlene and Stewart Greenebaum Cancer Center.
Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Health System, 111 Michigan Ave. N.W., Washington, DC 20010.
IEEE Trans Med Robot Bionics. 2019 Nov;1(4):228-236. doi: 10.1109/tmrb.2019.2949870. Epub 2019 Oct 28.
Autonomous robotic surgery systems aim to improve patient outcomes by leveraging the repeatability and consistency of automation and also reducing human induced errors. However, intraoperative autonomous soft tissue tracking and robot control still remains a challenge due to the lack of structure, and high deformability of such tissues. In this paper, we take advantage of biocompatible Near-Infrared (NIR) marking methods and develop a supervised autonomous 3D path planning, filtering, and control strategy for our Smart Tissue Autonomous Robot (STAR) to enable precise and consistent incisions on complex 3D soft tissues. Our experimental results on cadaver porcine tongue samples indicate that the proposed strategy reduces surface incision error and depth incision error by 40.03% and 51.5%, respectively, compared to a teleoperation strategy via da Vinci. Furthermore, compared to an autonomous path planning method with linear interpolation between the NIR markers, the proposed strategy reduces the incision depth error by 48.58% by taking advantage of 3D tissue surface information.
自主机器人手术系统旨在通过利用自动化的可重复性和一致性以及减少人为失误来改善患者预后。然而，由于此类组织缺乏结构且具有高可变形性，术中自主软组织跟踪和机器人控制仍然是一项挑战。在本文中，我们利用生物相容性近红外（NIR）标记方法，为我们的智能组织自主机器人（STAR）开发了一种有监督的自主三维路径规划、滤波和控制策略，以实现对复杂三维软组织进行精确且一致的切割。我们在猪尸体舌头样本上的实验结果表明，与通过达芬奇手术系统进行的远程操作策略相比，所提出的策略分别将表面切割误差和深度切割误差降低了40.03%和51.5%。此外，与在近红外标记之间进行线性插值的自主路径规划方法相比，所提出的策略通过利用三维组织表面信息将切割深度误差降低了48.58%。
