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Blake Hannaford - One of the best experts on this subject based on the ideXlab platform.

  • real time data driven precision estimator for Raven ii surgical robot end effector position
    International Conference on Robotics and Automation, 2020
    Co-Authors: Haonan Peng, Xingjian Yang, Blake Hannaford
    Abstract:

    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.

  • Raven s design and simulation of a robot for teleoperated microgravity rodent dissection under time delay
    International Conference on Robotics and Automation, 2020
    Co-Authors: Andrew Lewis, Jacob Rosen, David Drajeske, John Raiti, Angelique M Berens, Blake Hannaford
    Abstract:

    The International Space Station (ISS) serves as a research lab for a wide variety of experiments including some that study the biological effects of microgravity and spaceflight using the Rodent Habitat and Microgravity Science Glovebox (MSG). Astronauts train for onboard dissections of rodents following basic training. An alternative approach for conducting these experiments is teleoperation of a robot located on the ISS from earth by a scientist who is proficient in rodent dissection. This pilot study addresses (1) the effects of extreme time delay on skill degradation during Fundamentals of Laparoscopic Surgery (FLS) tasks and rodent dissections using Raven II; (2) derivation and testing of rudimentary interaction force estimation; (3) elicitation of design requirements for an onboard dissection robot, Raven-S; and (4) simulation of the Raven-S prototype design with dissection data. The results indicate that the tasks’ completion times increased by a factor of up to 9 for a 3 s time delay while performing manipulation and cutting tasks (FLS model) and by a factor of up to 3 for a 0.75 s time delay during mouse dissection tasks (animal model). Average robot forces/torques of 14N/0.1Nm (peak 90N/0.75Nm) were measured along with average linear/angular velocities of 0.02m/s / 4rad/s (peak 0.1m/s / 40rad/s) during dissection. A triangular configuration of three arms with respect to the operation site showed the best configuration given the MSG geometry and the dissection tasks. In conclusion, the results confirm the feasibility of utilizing a surgically-inspired Raven-S robot for teleoperated rodent dissection for successful completion of the predefined tasks in the presence of communications time delay between the ISS and ground control.

  • Raven open surgical robotic platforms
    arXiv: Robotics, 2019
    Co-Authors: Blake Hannaford, Jacob Rosen
    Abstract:

    The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery.

  • Improving position precision of a servo-controlled elastic cable driven surgical robot using Unscented Kalman Filter
    IEEE International Conference on Intelligent Robots and Systems, 2015
    Co-Authors: Mohammad Haghighipanah, Muneaki Miyasaka, Yangming Li, Blake Hannaford
    Abstract:

    Cable driven power transmission is popular in many manipulator applications including medical arms. In spite of advantages obtained by removing motors from the mechanism, cable transmission introduces higher non-linearity and more uncertainties such as cable stretch and cable coupling. In order to improve the control precision and robustness of the Raven-II surgical robot, particularly for automation applications, the Unscented Kalman Filter (UKF) was adopted for state estimation. The UKF estimated state variables of the Raven-II dynamic model from sensor data. The dual UKF was used offline to estimate cable coupling parameters. The experimental results showed that the proposed method improved joint position estimation precision and the estimation consistency, especially on the more elastic links. The improvements for links 2 and 3 of the Raven were 36.76%, and 62.99%, respectively. For link 1 the improvement was 1.43% because the transmission is very stiff.

  • Raven surgical robot training in preparation for da vinci
    Medicine Meets Virtual Reality, 2014
    Co-Authors: Deanna Glassman, Blake Hannaford, Lee W White, Andrew Lewis, Hawkeye H King, Alicia Clarke, Thomas S Glassman, Bryan A Comstock, Thomas S Lendvay
    Abstract:

    The rapid adoption of robotic assisted surgery challenges the pace at which adequate robotic training can occur due to access limitations to the da Vinci robot. Thirty medical students completed a randomized controlled trial evaluating whether the Raven robot could be used as an alternative training tool for the Fundamentals of Laparoscopic Surgery (FLS) block transfer task on the da Vinci robot. Two groups, one trained on the da Vinci and one trained on the Raven, were tested on a criterion FLS block transfer task on the da Vinci. After robotic FLS block transfer proficiency training there was no statistically significant difference between path length (p=0.39) and economy of motion scores (p=0.06) between the two groups, but those trained on the da Vinci did have faster task times (p=0.01). These results provide evidence for the value of using the Raven robot for training prior to using the da Vinci surgical system for similar tasks.

Hao Wang - One of the best experts on this subject based on the ideXlab platform.

  • Raven 2 0 a versatile toolbox for metabolic network reconstruction and a case study on streptomyces coelicolor
    PLOS Computational Biology, 2018
    Co-Authors: Hao Wang, Simonas Marcisauskas, Benjamin J Sanchez, Ivan Domenzain, Daniel Hermansson, Rasmus Agren, Jens Nielsen, Eduard J Kerkhoven
    Abstract:

    Raven is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present Raven Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of Raven 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that Raven 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that Raven 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both Raven 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/Raven and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

  • Raven 2 0 a versatile platform for metabolic network reconstruction and a case study on streptomyces coelicolor
    bioRxiv, 2018
    Co-Authors: Hao Wang, Simonas Marcisauskas, Benjamin J Sanchez, Ivan Domenzain, Daniel Hermansson, Rasmus Agren, Jens Nielsen, Eduard J Kerkhoven
    Abstract:

    Raven is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present Raven Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of Raven 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor . Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that Raven 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor , with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that Raven 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both Raven 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community.

Eduard J Kerkhoven - One of the best experts on this subject based on the ideXlab platform.

  • Raven 2 0 a versatile toolbox for metabolic network reconstruction and a case study on streptomyces coelicolor
    PLOS Computational Biology, 2018
    Co-Authors: Hao Wang, Simonas Marcisauskas, Benjamin J Sanchez, Ivan Domenzain, Daniel Hermansson, Rasmus Agren, Jens Nielsen, Eduard J Kerkhoven
    Abstract:

    Raven is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present Raven Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of Raven 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that Raven 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that Raven 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both Raven 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/Raven and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

  • Raven 2 0 a versatile platform for metabolic network reconstruction and a case study on streptomyces coelicolor
    bioRxiv, 2018
    Co-Authors: Hao Wang, Simonas Marcisauskas, Benjamin J Sanchez, Ivan Domenzain, Daniel Hermansson, Rasmus Agren, Jens Nielsen, Eduard J Kerkhoven
    Abstract:

    Raven is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present Raven Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of Raven 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor . Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that Raven 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor , with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that Raven 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both Raven 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community.

Jacob Rosen - One of the best experts on this subject based on the ideXlab platform.

  • Raven s design and simulation of a robot for teleoperated microgravity rodent dissection under time delay
    International Conference on Robotics and Automation, 2020
    Co-Authors: Andrew Lewis, Jacob Rosen, David Drajeske, John Raiti, Angelique M Berens, Blake Hannaford
    Abstract:

    The International Space Station (ISS) serves as a research lab for a wide variety of experiments including some that study the biological effects of microgravity and spaceflight using the Rodent Habitat and Microgravity Science Glovebox (MSG). Astronauts train for onboard dissections of rodents following basic training. An alternative approach for conducting these experiments is teleoperation of a robot located on the ISS from earth by a scientist who is proficient in rodent dissection. This pilot study addresses (1) the effects of extreme time delay on skill degradation during Fundamentals of Laparoscopic Surgery (FLS) tasks and rodent dissections using Raven II; (2) derivation and testing of rudimentary interaction force estimation; (3) elicitation of design requirements for an onboard dissection robot, Raven-S; and (4) simulation of the Raven-S prototype design with dissection data. The results indicate that the tasks’ completion times increased by a factor of up to 9 for a 3 s time delay while performing manipulation and cutting tasks (FLS model) and by a factor of up to 3 for a 0.75 s time delay during mouse dissection tasks (animal model). Average robot forces/torques of 14N/0.1Nm (peak 90N/0.75Nm) were measured along with average linear/angular velocities of 0.02m/s / 4rad/s (peak 0.1m/s / 40rad/s) during dissection. A triangular configuration of three arms with respect to the operation site showed the best configuration given the MSG geometry and the dissection tasks. In conclusion, the results confirm the feasibility of utilizing a surgically-inspired Raven-S robot for teleoperated rodent dissection for successful completion of the predefined tasks in the presence of communications time delay between the ISS and ground control.

  • Raven open surgical robotic platforms
    arXiv: Robotics, 2019
    Co-Authors: Blake Hannaford, Jacob Rosen
    Abstract:

    The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery.

  • Raven-II: An open platform for surgical robotics research
    IEEE Transactions on Biomedical Engineering, 2013
    Co-Authors: Blake Hannaford, Hawkeye King, Diana W. Friedman, Phillip Roan, Sina Nia Kosari, D. Glozman, Jacob Rosen, Ji Ma, Lei Cheng, Lee White
    Abstract:

    The Raven-II is a platform for collaborative research on advances in surgical robotics. Seven universities have begun research using this platform. The Raven-II system has two 3-DOF spherical positioning mechanisms capable of attaching interchangeable four DOF instruments. The Raven-II software is based on open standards such as Linux and ROS to maximally facilitate software development. The mechanism is robust enough for repeated experiments and animal surgery experiments, but is not engineered to sufficient safety standards for human use. Mechanisms in place for interaction among the user community and dissemination of results include an electronic forum, an online software SVN repository, and meetings and workshops at major robotics conferences.

  • maximizing dexterous workspace and optimal port placement of a multi arm surgical robot
    International Conference on Robotics and Automation, 2011
    Co-Authors: Daniel Glozman, Dejan Milutinovic, Jacob Rosen
    Abstract:

    Surgical procedures are traditionally performed by two or more surgeons along with staff nurses. One surgeon serves as the primary surgeon and the other serves as his/her assistant. Surgical robotics have redefined the dynamics in which the two surgeons interact with each other and with the surgical site. Raven IV is a new generation of the surgical robot system having four articulated robotic arms in a spherical configuration, each holding an articulated surgical tool. The system allows two surgeons to teleoperate the Raven IV collaboratively from two remote sites. The current research effort aims to configure the link architecture of each robotic arm, along with the position (port placement) and orientation of the Raven IV with respect to the patient, in order to optimize the common workspace reachable by all four robotic arms. The simulation results indicate that tilting the base of the robotic arms in the range of −20 to 20 deg while moving the ports closer together up to 50 mm apart leads to a preferred circular shape of the common workspace with an isotropy value of 0.5. A carefully configured system with multiple surgical robotic arms will enhance the interactive performance of the two surgeons.

Jens Nielsen - One of the best experts on this subject based on the ideXlab platform.

  • Raven 2 0 a versatile toolbox for metabolic network reconstruction and a case study on streptomyces coelicolor
    PLOS Computational Biology, 2018
    Co-Authors: Hao Wang, Simonas Marcisauskas, Benjamin J Sanchez, Ivan Domenzain, Daniel Hermansson, Rasmus Agren, Jens Nielsen, Eduard J Kerkhoven
    Abstract:

    Raven is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present Raven Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of Raven 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that Raven 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that Raven 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both Raven 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/Raven and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

  • Raven 2 0 a versatile platform for metabolic network reconstruction and a case study on streptomyces coelicolor
    bioRxiv, 2018
    Co-Authors: Hao Wang, Simonas Marcisauskas, Benjamin J Sanchez, Ivan Domenzain, Daniel Hermansson, Rasmus Agren, Jens Nielsen, Eduard J Kerkhoven
    Abstract:

    Raven is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present Raven Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of Raven 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor . Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that Raven 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor , with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that Raven 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both Raven 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community.