Knowing accurate joint positions is crucial for safe and precise control of laparoscopic surgical robots, especially for the automation of surgical sub-tasks. These robots have often been designed with cable-driven arms and tools because cables allow for larger motors to be placed at the base of the robot, further from the operating area where space is at a premium. However, by connecting the joint to its motor with a cable, any stretch in the cable can lead to errors in kinematic estimation from encoders at the motor, which can result in difficulties for accurate control of the surgical tool. In this work, we propose an efficient data-driven calibration of positioning joints of such robots, in this case the RAVEN-II surgical robotics research platform. While the calibration takes only 8-21 minutes, the accuracy of the calibrated joints remains high during a 6-hour heavily loaded operation, suggesting desirable feasibility in real practice. The calibration models take original robot states as input and are trained using zig-zag trajectories within a desired sparsity, requiring no additional sensors after training. Compared to fixed offset compensation, the Deep Neural Network calibration model can further reduce 76 percent of error and achieve accuracy of 0.104 deg, 0.120 deg, and 0.118 mm in joints 1, 2, and 3, respectively. In contrast to end-to-end models, experiments suggest that the DNN model achieves better accuracy and faster convergence when outputting the error to correct original inaccurate joint positions. Furthermore, a linear regression model is shown to have 160 times faster inference speed than DNN models for application within the 1000 Hz servo control loop, with slightly compromised accuracy.
Capstan-driven Continuum Surgical Robot: Design, Modeling, and Perception
The Chinese University of Hong Kong（香港中文大学）；School of Mechanical and Electronic Engineering, Wuhan University of Technology（武汉理工大学机电工程学院）；Huazhong University of Science and Technology（华中科技大学）；Shun Hing Institute of Advanced Engineering（信兴高等工程研究院）；Multi-Scale Medical Robotics Center（多尺度医疗机器人中心）
Stable Tracking-in-the-Loop Control of Cable-Driven Surgical Manipulators under Erroneous Kinematic Chains
Department of Electrical and Computer Engineering, University of California San Diego（电子工程与计算机科学系，加州大学圣地亚哥分校）；Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville（电子工程与计算机科学系，田纳西大学基洛纳分校）
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
University of California, Berkeley（加州大学伯克利分校）
由于电缆驱动系统的固有非线性，使用机器人手术助理（RSA）（如da Vinci Research Kit（dVRK））自动执行清创（去除死亡或患病组织碎片）等精确子任务具有挑战性。我们提出并评估了一种新的两阶段粗到精校准方法。在第一阶段（粗略），我们放置一个红色的校准标记的末端执行器，并让它随机移动通过一组开环轨迹，以获得一个大样本集的相机像素和内部机器人末端执行器配置。然后使用该粗略数据来训练深度神经网络（DNN）以学习粗略变换偏差。在阶段II（精细）中，施加来自阶段I的偏置以使末端执行器朝向印刷纸张上的一小组特定目标点移动。对于每个目标，人类操作员通过直接接触（而不是通过远程操作）手动调整末端执行器的位置，并记录剩余补偿偏差。然后使用该精细数据来训练随机森林（RF）以学习精细变换偏差。随后的实验表明，在没有校准的情况下，位置误差平均为4.55mm。阶段I可以将平均误差减小到2.14mm，并且阶段I和阶段II的组合可以将平均误差减小到1.08mm。我们将这些结果应用于葡萄干和南瓜籽作为碎片幻影的清创。使用具有标准边缘检测的内窥镜立体摄像机，120次试验的平均成功率为94.5%，超过了之前使用更大碎片的结果（89.4%），并实现了2.1倍的加速，将每个碎片的时间从15.8秒减少到7.3秒。源代码、数据和视频可在https://sites.google.com/view/calib-…
Learning Domain Adaptation with Model Calibration for Surgical Report Generation in Robotic Surgery
National University of Singapore（新加坡国立大学）；NUSRI Suzhou China（新加坡国立大学苏州研究院）；The Chinese University of Hong Kong（香港中文大学）；Imperial College London（伦敦帝国理工学院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
On-the-fly hand-eye calibration for the da Vinci surgical robot
Department of Mechanical Engineering, Imperial College London（帝国理工学院机械工程系）；Mechatronics in Medicine Laboratory（医学机电实验室）；Hamlyn Centre for Robotics Surgery（机器人外科哈姆林中心）
SurgCalib: Gaussian Splatting-Based Hand-Eye Calibration for Robot-Assisted Minimally Invasive Surgery
Robotics and Control Laboratory, University of British Columbia（机器人与控制实验室，不列颠哥伦比亚大学）；Department of Biomedical Engineering, National University of Singapore（生物医学工程系，新加坡国立大学）；Division of Otolaryngology – Head and Neck Surgery, University of British Columbia（耳鼻喉科–头颈外科部，不列颠哥伦比亚大学）
Quasi-Static Analysis on Transoral Surgical Tendon-Driven Articulated Robot Units
Georgia Institute of Technology（佐治亚理工学院）；Asan Medical Center（峨山医院）；Asan Institute for Life Sciences（峨山生命科学研究所）；University of Ulsan College of Medicine（蔚山大学医学院）
Shaft-integrated Force Sensing with Transformer-based Dynamics Compensation for Telesurgery
Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University（电气、计算机与系统工程系，凯斯西储大学）；Department of Mechanical and Aerospace Engineering, Case Western Reserve University（机械与航空航天工程系，凯斯西储大学）；Resense GmbH；Semantic Information Systems Group, Osnabrück University（语义信息系统组，奥斯纳布吕克大学）；Justus Liebig University（吉森大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
High-Precision Surgical Robotic System for Intraocular Procedures
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（机械与航空航天工程系，加州大学洛杉矶分校）；Stein Eye Institute, University of California, Los Angeles（斯坦眼科研究所，加州大学洛杉矶分校）
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
The Chinese University of Hong Kong (CUHK)（香港中文大学）；Beijing Institute of Technology（北京理工大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Tongji University（同济大学）；University College London（伦敦大学学院）
Dept. of Automation and Systems Engineering（自动化与系统工程系）；Universidade de Vigo（维戈大学）；Dept. of Communication and Information Technologies（通信与信息科技系）；Technical University of Cartagena（卡塔赫纳技术大学）
Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning
Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London（伦敦帝国理工学院全球健康创新研究所哈姆林机器人手术中心）；Department of Surgery and Cancer, Imperial College London（伦敦帝国理工学院外科与癌症系）；Department of Mechanical Engineering, Imperial College London（伦敦帝国理工学院机械工程系）
LLM-based ambiguity detection in natural language instructions for collaborative surgical robots
Institutes of Innovation for Future Society, Nagoya University（未来社会创新研究所，名古屋大学）；Department of Micro-Nano Mechanical Science and Engineering, Nagoya University（微纳米机械科学与工程系，名古屋大学）
A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion
Institute for Robotics and Intelligent Machines, Georgia Tech（佐治亚理工学院机器人与智能机器研究所）；CNRS IRL 2958, Georgia Tech Lorraine（法国国家科学研究中心2958国际联合实验室，佐治亚理工洛林分校）；School of Civil and Environmental Engineering, Georgia Tech（佐治亚理工学院土木与环境工程学院）
Mixed-Reality-Guided Teleoperation of a Collaborative Robot for Surgical Procedures
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；Technical Sciences Academy of Romania（罗马尼亚技术科学院）；”Iuliu Hatieganu” University of Medicine and Pharmacy（“尤利乌·哈蒂埃加努”医药大学）；École Centrale Nantes（南特中央理工学校）；Nantes Université（南特大学）；CNRS（法国国家科学研究中心）
本文提出一种结合 Kuka IIWA LBR 协作机器人与 Hololens 2 的混合现实遥操作系统，允许外科医生通过手势直观控制机器人定位，有效提升手术工作流程并缩短学习曲线。
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）
A Surgical Platform for Intracerebral Hemorrhage Robotic Evacuation (ASPIHRE): A Non-metallic MR-guided Concentric Tube Robot
Georgia Institute of Technology（佐治亚理工学院）；Vanderbilt University Medical Center（范德堡大学医学中心）；George Washington University（乔治华盛顿大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；DePaul University（德保罗大学）；Children’s National Hospital（全国儿童医院）
RCM-ACT: Imitation Learning with Dynamic RCM Calibration for Autonomous Intraocular Foreign Body Removal
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院血管植入设备国家重点实验室）；Dessight Biomedical（Dessight生物医学公司）；Center for Rehabilitation Medicine, Department of Ophthalmology, Zhejiang Provincial People’s Hospital（浙江省人民医院康复医学中心、眼科部门）；School of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；School of Public Health and Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院公共卫生学院及第二附属医院）；State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine（浙江大学医学院第二附属医院及公共卫生学院血管植入设备国家重点实验室）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）
Collaborative Human-Robot Surgery for Mandibular Angle Split Osteotomy: Optical Tracking based Approach
organization= School of Medical Technology；the Key Laboratory of Biomimetic Robots；Systems, Beijing Institute of Technology , city= Beijng , postcode= 100081 , country= China；organization= School of Mechatronical Engineering；Systems , Beijing Institute of Technology , city= Beijng , postcode= 100081 , country= China；organization= School of Biomedical Engineering \& Imaging Sciences, King's College London , city= London , postcode= SE1 7EU , country= UK
