Measurement of environment interaction forces during robotic minimally-invasive surgery would enable haptic feedback to the surgeon, thereby solving one long-standing limitation. Estimating this force from existing sensor data avoids the challenge of retrofitting systems with force sensors, but is difficult due to mechanical effects such as friction and compliance in the robot mechanism. We have previously shown that neural networks can be trained to estimate the internal robot joint torques, thereby enabling estimation of external forces. In this work, we extend the method to estimate external Cartesian forces and torques, and also present a two-step approach to adapt to the specific surgical setup by compensating for forces due to the interactions between the instrument shaft and cannula seal and between the trocar and patient body. Experiments show that this approach provides estimates of external forces and torques within a mean root-mean-square error (RMSE) of 2 N and 0.08 Nm, respectively. Furthermore, the two-step approach can add as little as 5 minutes to the surgery setup time, with about 4 minutes to collect intraoperative training data and 1 minute to train the second-step network.
The da Vinci Research Kit (dVRK) is a teleoperated surgical robotic system. For dynamic simulations and model-based control, the dynamic model of the dVRK is required. We present an open-source dynamic model identification package for the dVRK, capable of modeling the parallelograms, springs, counterweight, and tendon couplings, which are inherent to the dVRK. A convex optimization-based method is used to identify the dynamic parameters of the dVRK subject to physical consistency. Experimental results show the effectiveness of the modeling and the robustness of the package. Although this softw…
Imperial College London（伦敦帝国理工学院）；University of Leeds（利兹大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Intuitive Surgical Inc（直觉外科公司）
Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an acade…
Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning
Department of Electrical and Computer Engineering, University of Alberta（电气与计算机工程系，阿尔伯塔大学）；Department of Biomedical Engineering, University of Alberta（生物医学工程系，阿尔伯塔大学）
An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery
Department of Translational Surgical Oncology, NCT/UCC Dresden, a partnership between DKFZ, MF UKD, TUD, HZDR Dresden, Germany（转化外科肿瘤学系，NCT/UCC德累斯顿，DKFZ、MF UKD、TUD、HZDR德累斯顿的联合体）；Cluster of Excellence-CeTI, TUD, Germany（卓越中心- CeTI，TUD，德国）；Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD, Germany（visceral、胸腔和血管外科系，医学学院和卡尔·格奥尔格·卡尔医院，TUD，德国）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；Kyung Hee University（庆熙大学）
Innovations from surgical robotic research rarely translates to live surgery due to the significant difference between the lab and a live environment. Live environments require considerations that are often overlooked during early stages of research such as surgical staff, surgical procedure, and the challenges of working with live tissue. One such example is the da Vinci Research Kit (dVRK) which is used by over 40 robotics research groups and represents an open-sourced version of the da Vinci Surgical System. Despite dVRK being available for nearly a decade and the ideal candidate for transl…
University of California, Berkeley（加州大学伯克利分校）；SRI International（SRI国际）
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 …
Gravity Compensation of the dVRK-Si Patient Side Manipulator based on Dynamic Model Identification
Department of Robotics Engineering, Worcester Polytechnic Institute（机器人工程系，沃斯通理工大学）；Laboratory for Computational Sensing and Robotics, JHU（计算感知与机器人实验室，约翰·霍普金斯大学）；Department of Computer Science, Vanderbilt University（计算机科学系，范德比尔特大学）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰·霍普金斯大学）
Force estimation using neural networks is a promising approach to enable haptic feedback in minimally invasive surgical robots without end-effector force sensors. Various network architectures have been proposed, but none have been tested in real time with surgical-like manipulations. Thus, questions remain about the real-time transparency and stability of force feedback from neural network-based force estimates. We characterize the real-time impedance transparency and stability of force feedback rendered on a da Vinci Research Kit teleoperated surgical robot using neural networks with vision-…
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院威康/EPSRC介入与外科科学中心）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation. The problem is challenging because different illumination conditions, bleeding, smoke and occlusions can reduce algorithm robustness. At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper we show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a ne…
Haptic Intelligence Department（人机交互智能部门）；Optics and Sensing Laboratory, Max Planck Institute for Intelligent Systems（光学与感知实验室，智能系统马克斯·普朗克研究所）；Department of General, Visceral and Transplant Surgery, University Hospital Tübingen（普通外科、 visceral外科和移植外科部门，图宾根大学医院）；Laboratory for Computational Sensing and Robotics (LCSR) Johns Hopkins University（计算感知与机器人实验室（LCSR）约翰霍普金斯大学）
