CAREER: Advancing Autonomy for Soft Tissue Robotic Surgery and Interventions

职业：推进软组织机器人手术和干预的自主性

• 批准号: 2144348
• 负责人: Axel Krieger
• 金额: $ 59.99万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144348&HistoricalAwards=false
• 关键词: CAREER; Advancing; Autonomy; Soft; Tissue

The research supported by this Faculty Early Career Development (CAREER) grant will make contributions to our fundamental knowledge in robotics and autonomy related to healthcare. Autonomous robotic surgery systems have the potential to significantly improve efficiency, safety, and consistency over current tele-operated robotically assisted surgery. Complication rates of soft tissue surgery such as kidney tumor resections and bowel surgery reach up to 18 and 30 percent, respectively. There is a significant learning curve in robotic surgery, surgical outcomes vary greatly between hospitals, and postoperative complications differ significantly by surgeon. This project will enable the development of a new generation of surgical robots with increasing levels of autonomy that reduce complication rates and improve outcomes independent of surgeon’s experience. This research has the potential to democratize access to the highest level of healthcare by providing consistent expert-level results, mitigate the rising healthcare costs by increasing efficiency, and help in future pandemics by protecting healthcare workers. Thus, results from this research will benefit the US society welfare and economy. The multi-disciplinary research and education activities in robotics and autonomy and related diverse set of intellectual communities ranging from computer vision, sensing, artificial intelligence, to healthcare will help to inspire a diverse set of students from groups that have been traditionally underrepresented in science, technology, engineering, and mathematics (STEM) and to substantially increase participation in robotics research. Present day approaches to automated manipulation are unable to emulate highly trained humans in the performance of complex manipulation tasks in varying, unstructured, and deformable environments. These research and experiments on deformable tissue tracking will yield new techniques for identifying and tracking subtle tissue targets in unstructured environments. The research on deformation prediction will produce methodologies for understanding tissue behavior and how to compensate for deformations. The control-design activities of this project will address shortfalls in autonomous robot controllers by providing new strategies maximizing autonomy, while providing fail-safe operation. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项由学院早期职业发展(Career)基金资助的研究将对我们在机器人和与医疗保健相关的自主性方面的基础知识做出贡献。与目前的远程操作机器人辅助手术相比，自主机器人手术系统具有显著提高效率、安全性和一致性的潜力。软组织手术如肾肿瘤切除和肠道手术的并发症发生率分别高达18%和30%。机器人手术有一个重要的学习曲线，不同医院的手术结果差异很大，术后并发症因外科医生而异。该项目将使新一代手术机器人的开发具有越来越高的自主性，从而降低并发症发生率并改善结果，而不依赖于外科医生的经验。这项研究有可能通过提供一致的专家级结果来民主化获得最高水平的医疗保健，通过提高效率来缓解不断上升的医疗成本，并通过保护医护人员来帮助应对未来的流行病。因此，这项研究的结果将有利于美国的社会福利和经济。机器人学和自主性方面的多学科研究和教育活动以及相关的不同智力社区，从计算机视觉、传感、人工智能到医疗保健，将有助于激励来自传统上在科学、技术、工程和数学(STEM)领域代表性不足的群体的不同学生，并大幅增加对机器人研究的参与。目前的自动化操作方法不能在变化的、非结构化的和可变形的环境中模仿训练有素的人类执行复杂的操作任务。这些关于可变形组织跟踪的研究和实验将产生识别和跟踪非结构化环境中的微小组织目标的新技术。对变形预测的研究将为理解组织行为和如何补偿变形提供方法学。该项目的控制设计活动将通过提供最大限度地提高自主性的新策略来解决自主机器人控制器的不足，同时提供故障安全操作。该项目由跨部门机器人基础研究计划支持，该计划由工程学指导委员会(ENG)和计算机与信息科学与工程指导委员会(CEISE)共同管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Autonomous System for Tumor Resection (ASTR) - Dual-Arm Robotic Midline Partial Glossectomy

• DOI: 10.1109/lra.2023.3341773
• 发表时间: 2024-02-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Ge，Jiawei;Kam，Michael;Krieger，Axel
• 通讯作者: Krieger，Axel

Enhanced Accuracy in Magnetic Actuation: Closed-Loop Control of a Magnetic Agent With Low-Error Numerical Magnetic Model Estimation

• DOI: 10.1109/lra.2022.3191047
• 发表时间: 2022-10-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Erin，Onder;Raval，Suraj;Diaz-Mercado，Yancy
• 通讯作者: Diaz-Mercado，Yancy

Development and Evaluation of a Robotic Vessel Positioning System for Semi-Automatic Microvascular Anastomosis

半自动微血管吻合机器人血管定位系统的开发与评估

• DOI: 10.1109/icra48891.2023.10161296
• 发表时间: 2023
• 期刊: IEEE International Conference on Robotics and Automation (ICRA
• 作者: Haworth, Jesse;Opfermann, Justin;Kam, Michael;Wang, Yaning;Yang, Robin;Kang, Jin U.;Krieger, Axel
• 通讯作者: Krieger, Axel

Autonomous System for Vaginal Cuff Closure via Model-Based Planning and Markerless Tracking Techniques

• DOI: 10.1109/lra.2023.3273416
• 发表时间: 2023-07-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Kam，Michael;Wei，Shuwen;Krieger，Axel
• 通讯作者: Krieger，Axel