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1. Securing Robot-assisted Minimally Invasive Surgery through Perception Complementarities

   https://doi.org/10.1109/IRC.2020.00013  

   Su, Yun-Hsuan ; Sosnovskaya, Yana ; Hannaford, Blake ; Huang, Kevin ( November 2020 , Fourth IEEE International Conference on Robotic Computing (IRC))

   null (Ed.)

   Surgical robots have been introduced to operating rooms over the past few decades due to their high sensitivity, small size, and remote controllability. The cable-driven nature of many surgical robots allows the systems to be dexterous and lightweight, with diameters as low as 5mm. However, due to the slack and stretch of the cables and the backlash of the gears, inevitable uncertainties are brought into the kinematics calcu- lation [1]. Since the reported end effector position of surgical robots like RAVEN-II [2] is directly calculated using the motor encoder measurements and forward kinematics, it may contain relatively large error up to 10mm, whereas semi-autonomous functions being introduced into abdominal surgeries require position inaccuracy of at most 1mm. To resolve the problem, a cost-effective, real-time and data-driven pipeline for robot end effector position precision estimation is proposed and tested on RAVEN-II. Analysis shows an improved end effector position error of around 1mm RMS traversing through the entire robot workspace without high-resolution motion tracker. The open source code, data sets, videos, and user guide can be found at //github.com/HaonanPeng/RAVEN Neural Network Estimator. 

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2. Context-aware Monitoring in Robotic Surgery

   Yasar, Mohammad Samin ; Evans, David ; Alemzadeh, Homa ( January 2019 , International Symposium on Medical Robotics)

   Robotic-assisted minimally invasive surgery (MIS) has enabled procedures with increased precision and dexterity, but surgical robots are still open loop and require surgeons to work with a tele-operation console providing only limited visual feedback. In this setting, mechanical failures, software faults, or human errors might lead to adverse events resulting in patient complications or fatalities. We argue that impending adverse events could be detected and mitigated by applying context-specific safety constraints on the motions of the robot. We present a context-aware safety monitoring system which segments a surgical task into subtasks using kinematics data and monitors safety constraints specific to each subtask. To test our hypothesis about context specificity of safety constraints, we analyze recorded demonstrations of dry-lab surgical tasks collected from the JIGSAWS database as well as from experiments we conducted on a Raven II surgical robot. Analysis of the trajectory data shows that each subtask of a given surgical procedure has consistent safety constraints across multiple demonstrations by different subjects. Our preliminary results show that violations of these safety constraints lead to unsafe events, and there is often sufficient time between the constraint violation and the safety-critical event to allow for a corrective action. 

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3. Collaborative Robotics Toolkit (CRTK): Open Software Framework for Surgical Robotics Research

   https://doi.org/10.1109/IRC.2020.00014  

   Su, Yun-Hsuan ; Munawar, Adnan ; Deguet, Anton ; Lewis, Andrew ; Lindgren, Kyle ; Li, Yangming ; Taylor, Russell H. ; Fischer, Gregory S. ; Hannaford, Blake ; Kazanzides, Peter ( November 2020 , 2020 Fourth IEEE International Conference on Robotic Computing (IRC))

   null (Ed.)

   Robot-assisted minimally invasive surgery has made a substantial impact in operating rooms over the past few decades with their high dexterity, small tool size, and impact on adoption of minimally invasive techniques. In recent years, intelligence and different levels of surgical robot autonomy have emerged thanks to the medical robotics endeavors at numerous academic institutions and leading surgical robot companies. To accelerate interaction within the research community and prevent repeated development, we propose the Collaborative Robotics Toolkit (CRTK), a common API for the RAVEN-II and da Vinci Research Kit (dVRK) - two open surgical robot platforms installed at more than 40 institutions worldwide. CRTK has broadened to include other robots and devices, including simulated robotic systems and industrial robots. This common API is a community software infrastructure for research and education in cutting edge human-robot collaborative areas such as semi-autonomous teleoperation and medical robotics. This paper presents the concepts, design details and the integration of CRTK with physical robot systems and simulation platforms. 

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4. Collaborative Robotics Toolkit (CRTK): Open Software Framework for Surgical Robotics Research

   Su, Yun-Hsuan ; Munawar, Adnan ; Deguet, Anton ; Lewis, Andrew ; Lindgren, Kyle ; Li, Yangming ; Taylor, Russell H. ; Fischer, Gregory S. ; Hannaford, Blake ; Kazanzides, Peter ( November 2020 , IEEE International Conference on Robotic Computing (IRC))

   null (Ed.)

   Robot-assisted minimally invasive surgery has made a substantial impact in operating rooms over the past few decades with their high dexterity, small tool size, and impact on adoption of minimally invasive techniques. In recent years, intelligence and different levels of surgical robot autonomy have emerged thanks to the medical robotics endeavors at numerous academic institutions and leading surgical robot companies. To accelerate interaction within the research community and prevent repeated development, we propose the Collaborative Robotics Toolkit (CRTK), a common API for the RAVEN-II and da Vinci Research Kit (dVRK) - two open surgical robot platforms installed at more than 40 institutions worldwide. CRTK has broadened to include other robots and devices, including simulated robotic systems and industrial robots. This common API is a community software infrastructure for research and education in cutting edge human-robot collaborative areas such as semi-autonomous teleoperation and medical robotics. This paper presents the concepts, design details and the integration of CRTK with physical robot systems and simulation platforms. 

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5. Real-time vision-based surgical tool segmentation with robot kinematics prior

   https://doi.org/10.1109/ISMR.2018.8333305  

   Su, Yun-Hsuan ; Huang, Kevin ; Hannaford, Blake ( March 2018 , 2018 International Symposium on Medical Robotics (ISMR), Atlanta, GA, 2018)

   null (Ed.)

   Robot-assisted minimally invasive surgery com- bines the skills and techniques of highly-trained surgeons with the robustness and precision of machines. Several advantages include precision beyond human dexterity alone, greater kinematic degrees of freedom at the surgical tool tip, and possibilities in remote surgical practices through teleoperation. Nevertheless, obtaining accurate force feedback during surgical operations remains a challenging hurdle. Though direct force sensing using tool tip mounted sensors is theoretically possible, it is not amenable to required sterilization procedures. Vision-based force estimation according to real-time analysis of tissue deformation serves as a promising alternative. In this application, along with numerous related research in robot- assisted minimally invasive surgery, segmentation of surgical instruments in endoscopic images is a prerequisite. Thus, a surgical tool segmentation algorithm robust to partial occlusion is proposed using DFT shape matching of robot kinematics shape prior (u) fused with log likelihood mask (Q) in the Opponent color space to generate final mask (U). Implemented on the Raven II surgical robot system, a real-time performance robust to tool tip orientation and up to 6 fps without GPU acceleration is achieved. 

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