Pub Date : 2024-11-12DOI: 10.1109/TMRB.2024.3487341
{"title":"IEEE Transactions on Medical Robotics and Bionics Publication Information","authors":"","doi":"10.1109/TMRB.2024.3487341","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3487341","url":null,"abstract":"","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"C2-C2"},"PeriodicalIF":3.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1109/TMRB.2024.3487343
{"title":"IEEE Transactions on Medical Robotics and Bionics Society Information","authors":"","doi":"10.1109/TMRB.2024.3487343","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3487343","url":null,"abstract":"","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"C3-C3"},"PeriodicalIF":3.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750863","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1109/TMRB.2024.3484068
Alan Kuntz;Blake Hannaford;Robert J. Webster
{"title":"Guest Editorial Special section on the Hamlyn Symposium 2023—Immersive Tech: The Future of Medicine","authors":"Alan Kuntz;Blake Hannaford;Robert J. Webster","doi":"10.1109/TMRB.2024.3484068","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3484068","url":null,"abstract":"","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1298-1299"},"PeriodicalIF":3.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1109/TMRB.2024.3487345
{"title":"IEEE Transactions on Medical Robotics and Bionics Information for Authors","authors":"","doi":"10.1109/TMRB.2024.3487345","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3487345","url":null,"abstract":"","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"C4-C4"},"PeriodicalIF":3.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TMRB.2024.3479878
Navid Masoumi;Andrés C. Ramos;Tannaz Torkaman;Liane S. Feldman;Jake Barralet;Javad Dargahi;Amir Hooshiar
A novel soft sensor calibration method is proposed for minimally invasive surgery, based on our developed gelatin-graphite sensor with high compliance and adaptability. This approach uses convolutional deep learning that accounts for a sensor’s non-linear behavior and reduces noise amplification. This technique offers a smaller minimum detectable force than other approaches and is particularly useful in sensitive surgical scenarios. The sensor’s performance is characterized by its fine resolution ( $leq 1$ mN) and accurate force estimation, especially for forces below 400 mN of amplitude. The best calibration (Morse) scheme provides high performance, with a Mean Absolute Error of $leq 7.9$ mN. This work was validated through comparison among other representative studies and offered a path toward future directions for optimizing and implementing soft robotic sensors in minimally invasive surgeries. The application of this sensor can revolutionize surgical procedures and capitalize on the benefits of soft robotics, potentially enhancing precision and reducing trauma in surgeries.
{"title":"Embedded Force Sensor for Soft Robots With Deep Transformation Calibration","authors":"Navid Masoumi;Andrés C. Ramos;Tannaz Torkaman;Liane S. Feldman;Jake Barralet;Javad Dargahi;Amir Hooshiar","doi":"10.1109/TMRB.2024.3479878","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3479878","url":null,"abstract":"A novel soft sensor calibration method is proposed for minimally invasive surgery, based on our developed gelatin-graphite sensor with high compliance and adaptability. This approach uses convolutional deep learning that accounts for a sensor’s non-linear behavior and reduces noise amplification. This technique offers a smaller minimum detectable force than other approaches and is particularly useful in sensitive surgical scenarios. The sensor’s performance is characterized by its fine resolution (\u0000<inline-formula> <tex-math>$leq 1$ </tex-math></inline-formula>\u0000mN) and accurate force estimation, especially for forces below 400 mN of amplitude. The best calibration (Morse) scheme provides high performance, with a Mean Absolute Error of \u0000<inline-formula> <tex-math>$leq 7.9$ </tex-math></inline-formula>\u0000 mN. This work was validated through comparison among other representative studies and offered a path toward future directions for optimizing and implementing soft robotic sensors in minimally invasive surgeries. The application of this sensor can revolutionize surgical procedures and capitalize on the benefits of soft robotics, potentially enhancing precision and reducing trauma in surgeries.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1363-1374"},"PeriodicalIF":3.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image-guided robotic interventions represent a transformative frontier in surgery, blending advanced imaging and robotics for improved precision and outcomes. This paper addresses the critical need for integrating open-source platforms to enhance situational awareness in image-guided robotic research. We present an open-source toolkit, named ENTRI, that seamlessly combines a physics-based constraint formulation framework, AMBF, with a state-of-the-art imaging platform application, 3D Slicer. ENTRI facilitates the creation of highly customizable interactive digital twins, that incorporate processing and visualization of medical imaging, robot kinematics, and scene dynamics for real-time robot control. Through a feasibility study, we showcase real-time synchronization of a physical robotic interventional environment in both 3D Slicer and AMBF, highlighting low-latency updates and improved visualization. The source code and supplementary materials for this study are available at https://github.com/LCSR-CIIS/ENTRI .
图像引导机器人介入是外科手术的变革前沿，它将先进的成像技术和机器人技术相结合，提高了手术的精确度和效果。本文探讨了在图像引导机器人研究中整合开源平台以提高态势感知能力的关键需求。我们提出了一个名为 ENTRI 的开源工具包，它将基于物理的约束制定框架 AMBF 与最先进的成像平台应用程序 3D Slicer 无缝地结合在一起。ENTRI 可帮助创建高度定制化的交互式数字双胞胎，将医学成像、机器人运动学和场景动力学的处理和可视化结合起来，实现实时机器人控制。通过可行性研究，我们在 3D Slicer 和 AMBF 中展示了物理机器人介入环境的实时同步，突出了低延迟更新和改进的可视化。本研究的源代码和补充材料可在 https://github.com/LCSR-CIIS/ENTRI 上获取。
{"title":"ENTRI: Enhanced Navigational Toolkit for Robotic Interventions","authors":"Manish Sahu;Hisashi Ishida;Laura Connolly;Hongyi Fan;Anton Deguet;Peter Kazanzides;Francis X. Creighton;Russell H. Taylor;Adnan Munawar","doi":"10.1109/TMRB.2024.3475827","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3475827","url":null,"abstract":"Image-guided robotic interventions represent a transformative frontier in surgery, blending advanced imaging and robotics for improved precision and outcomes. This paper addresses the critical need for integrating open-source platforms to enhance situational awareness in image-guided robotic research. We present an open-source toolkit, named ENTRI, that seamlessly combines a physics-based constraint formulation framework, AMBF, with a state-of-the-art imaging platform application, 3D Slicer. ENTRI facilitates the creation of highly customizable interactive digital twins, that incorporate processing and visualization of medical imaging, robot kinematics, and scene dynamics for real-time robot control. Through a feasibility study, we showcase real-time synchronization of a physical robotic interventional environment in both 3D Slicer and AMBF, highlighting low-latency updates and improved visualization. The source code and supplementary materials for this study are available at \u0000<uri>https://github.com/LCSR-CIIS/ENTRI</uri>\u0000.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1405-1408"},"PeriodicalIF":3.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soft crawling robots demonstrated high compliance and effectiveness in performing complex tasks in unstructured and harsh environments. They can navigate inside constrained spaces and provide superior adaptability. This paper presents a soft crawling robot with a modified Yoshimura origami-based central chamber (elongation/contraction actuator) and four electrostatic adhesion feet (anchoring elements). It was designed to perform linear and steering locomotion under specific actuation sequences to avoid obstacles autonomously; it features a height-adjustable ability to squeeze under low gaps. A dual-morphing mechanism, enabling the origami-based chamber to operate with two locomotion modalities, was investigated to provide a simple but effective actuation method. Tests were carried out to validate the dual-morphing mechanism and to characterise the crawling robot’s performance. Experimental tests successfully demonstrated the robot’s capabilities, e.g., locomotion under low gaps (i.e., 20 mm, 66% of the height of the robot), obstacle avoidance, climbing on a sloped surface (i.e., 15 deg), and lifting and carrying objects (i.e., 80 g, ten times its weight).
{"title":"Soft Crawling Robot With a Dual-Morphing Origami Configuration","authors":"Xuyang Ren;Yu Huan;Matteo Cianchetti;Shuxin Wang;Paolo Dario;Gastone Ciuti","doi":"10.1109/TMRB.2024.3472858","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3472858","url":null,"abstract":"Soft crawling robots demonstrated high compliance and effectiveness in performing complex tasks in unstructured and harsh environments. They can navigate inside constrained spaces and provide superior adaptability. This paper presents a soft crawling robot with a modified Yoshimura origami-based central chamber (elongation/contraction actuator) and four electrostatic adhesion feet (anchoring elements). It was designed to perform linear and steering locomotion under specific actuation sequences to avoid obstacles autonomously; it features a height-adjustable ability to squeeze under low gaps. A dual-morphing mechanism, enabling the origami-based chamber to operate with two locomotion modalities, was investigated to provide a simple but effective actuation method. Tests were carried out to validate the dual-morphing mechanism and to characterise the crawling robot’s performance. Experimental tests successfully demonstrated the robot’s capabilities, e.g., locomotion under low gaps (i.e., 20 mm, 66% of the height of the robot), obstacle avoidance, climbing on a sloped surface (i.e., 15 deg), and lifting and carrying objects (i.e., 80 g, ten times its weight).","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1771-1780"},"PeriodicalIF":3.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-07DOI: 10.1109/TMRB.2024.3475509
Mustafa Haiderbhai;Lueder A. Kahrs
Traditional methods for autonomous cutting in surgical robotics have relied on trajectory-based planning algorithms. These methods fail to compensate for dynamic changes in soft materials such as deformation and topological change. To apply recent advances such as reinforcement learning (RL), a simulation is needed that models the cutting of soft materials. In this work, we develop a surgical robotics simulation environment for cutting deformable meshes with the da Vinci Research Kit (dVRK). Our environment is built using a particle-based physics simulation to simulate a rope grid structure to create realistic physics behavior and visual rendering. Cutting is implemented with the EndoWrist Round Tip Scissors (RTS) through a system of collision checking and callbacks to detect and update cuts. To showcase the deformable mesh cutting simulation, we design a cutting task of cutting along a desired path that can be solved through manual control. The grid structure can be adapted to render different materials, and we highlight how it can be made to resemble deformable tissue or fabric while being stable with no visible artifacts. This environment is a stepping stone towards training autonomous agents for cutting 2D deformable materials and building towards cutting more complex deformable shapes.
{"title":"Simulating Surgical Robot Cutting of Thin Deformable Materials Using a Rope Grid Structure","authors":"Mustafa Haiderbhai;Lueder A. Kahrs","doi":"10.1109/TMRB.2024.3475509","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3475509","url":null,"abstract":"Traditional methods for autonomous cutting in surgical robotics have relied on trajectory-based planning algorithms. These methods fail to compensate for dynamic changes in soft materials such as deformation and topological change. To apply recent advances such as reinforcement learning (RL), a simulation is needed that models the cutting of soft materials. In this work, we develop a surgical robotics simulation environment for cutting deformable meshes with the da Vinci Research Kit (dVRK). Our environment is built using a particle-based physics simulation to simulate a rope grid structure to create realistic physics behavior and visual rendering. Cutting is implemented with the EndoWrist Round Tip Scissors (RTS) through a system of collision checking and callbacks to detect and update cuts. To showcase the deformable mesh cutting simulation, we design a cutting task of cutting along a desired path that can be solved through manual control. The grid structure can be adapted to render different materials, and we highlight how it can be made to resemble deformable tissue or fabric while being stable with no visible artifacts. This environment is a stepping stone towards training autonomous agents for cutting 2D deformable materials and building towards cutting more complex deformable shapes.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1401-1404"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1109/TMRB.2024.3474059
Korn Borvorntanajanya;Shen Treratanakulchai;Ferdinando Rodriguez y Rodriguez;Enrico Franco
This paper investigates the model based tracking control of soft growing robots with pneumatic actuation that extend according to the principle known as eversion. A model of the system which accounts for the pressure dynamics is presented. A new control law is constructed with a high-order sliding-mode approach and a nonlinear observer is employed to compensate for the effect of external forces. Numerical simulations and experiments demonstrate the effectiveness of the proposed controller compared to our former energy-shaping implementation and to a baseline sliding-mode controller. Experiments with a training phantom demonstrate that the new controller resulted in a reduced peak pressure, approximately 14.8% lower, a reduced tracking error, approximately 4.9% lower RMSE, and a reduced consumption of compressed air, approximately 3.9% lower, compared to a baseline sliding-mode algorithm.
{"title":"Model-Based Tracking Control of a Soft Growing Robot for Colonoscopy","authors":"Korn Borvorntanajanya;Shen Treratanakulchai;Ferdinando Rodriguez y Rodriguez;Enrico Franco","doi":"10.1109/TMRB.2024.3474059","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3474059","url":null,"abstract":"This paper investigates the model based tracking control of soft growing robots with pneumatic actuation that extend according to the principle known as eversion. A model of the system which accounts for the pressure dynamics is presented. A new control law is constructed with a high-order sliding-mode approach and a nonlinear observer is employed to compensate for the effect of external forces. Numerical simulations and experiments demonstrate the effectiveness of the proposed controller compared to our former energy-shaping implementation and to a baseline sliding-mode controller. Experiments with a training phantom demonstrate that the new controller resulted in a reduced peak pressure, approximately 14.8% lower, a reduced tracking error, approximately 4.9% lower RMSE, and a reduced consumption of compressed air, approximately 3.9% lower, compared to a baseline sliding-mode algorithm.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1354-1362"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/TMRB.2024.3472844
Ang Zhang;Zhe Min;Zhengyan Zhang;Yingying Wang;Max Q.-H. Meng
This paper presents a novel augmented reality (AR)-assisted orthopedic surgical robotic system based on Head-Mounted Display (HMD) devices. The proposed system can overlay the preoperative plans over the patient’s anatomy and provide useful guidance for surgeons during interventions, with integrated calibration and registration components. A novel bi-directional generalised point set registration algorithm that utilises robust features is developed to accurately align the pre-operative CT and intra-operative patient spaces, which has been demonstrated to outperform existing registration methods. The efficacy of the system is both qualitatively and quantitatively assessed with an in vitro study representing a total knee arthroplasty (TKA) procedure. The experimental results showed that 1) the system can successfully align the preoperative and intraoperative spaces, with the mean target registration error (TRE) being $2.78 ; pm ; 2.51$ mm; 2) the models can be properly overlaid to the physical scenarios with the mean AR visualization accuracy being $6.97 ; pm ; 1.57$ mm.
{"title":"A Novel Augmented Reality Assisted Orthopedic Surgical Robotic System With Bidirectional Surface Registration Algorithms","authors":"Ang Zhang;Zhe Min;Zhengyan Zhang;Yingying Wang;Max Q.-H. Meng","doi":"10.1109/TMRB.2024.3472844","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3472844","url":null,"abstract":"This paper presents a novel augmented reality (AR)-assisted orthopedic surgical robotic system based on Head-Mounted Display (HMD) devices. The proposed system can overlay the preoperative plans over the patient’s anatomy and provide useful guidance for surgeons during interventions, with integrated calibration and registration components. A novel bi-directional generalised point set registration algorithm that utilises robust features is developed to accurately align the pre-operative CT and intra-operative patient spaces, which has been demonstrated to outperform existing registration methods. The efficacy of the system is both qualitatively and quantitatively assessed with an in vitro study representing a total knee arthroplasty (TKA) procedure. The experimental results showed that 1) the system can successfully align the preoperative and intraoperative spaces, with the mean target registration error (TRE) being \u0000<inline-formula> <tex-math>$2.78 ; pm ; 2.51$ </tex-math></inline-formula>\u0000 mm; 2) the models can be properly overlaid to the physical scenarios with the mean AR visualization accuracy being \u0000<inline-formula> <tex-math>$6.97 ; pm ; 1.57$ </tex-math></inline-formula>\u0000 mm.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1555-1566"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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