CAREER: Human-Centric Control for Teleoperated Surgical Robots

职业：以人为中心的遥控手术机器人控制

• 批准号: 1846726
• 负责人: Ann Majewicz Fey
• 金额: $ 50万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846726&HistoricalAwards=false
• 关键词: CAREER; Human; Centric; Control; Teleoperated

Human-in-the-loop control strategies in which the user performs a task better, and feels more confident to do so, is an important area of research in teleoperated robotics. However, human behavior can often change as a result of environmental, physical, emotional, and social factors. The goal of this Faculty Early Career Development Program (CAREER) project is to design adaptive control systems that can interpret and react to the dynamic human user. This research will be impactful in the field of surgical robotics where interaction with the patient demands safety and effectiveness from both the human operator (surgeon) and the robotic system itself. Robotic systems that are aware and responsive to user skill and performance style could be more able to avoid user errors and respond to adverse events in unpredictable environments. By integrating real-time models of user intent, movement style, and expertise level with a surgical robotic platform, this project will advance the NSF mission to promote the progress of science and advance national health by exploring fundamental relationships human behavior, motor control, and machine manipulation within the context of surgical robotics. The project supports education and broadening participation in engineering by promoting innovation activities related to healthcare and technology development for medical simulation and training. The goal of this CAREER project is to develop adaptive control algorithms for teleoperated robotic surgical systems that can respond to, ignore, and/or augment human motor control inputs depending on the output of user-centric models of behavior and task difficulty. Model output will be based in real-time, data-driven predictions and interpretations of human intent, surgical style, and level of expertise. Research objectives include: developing methods to model and control human behavior (e.g., user behavior and expertise) during unstructured teleoperation tasks; designing and analyzing adaptive control laws to enhance performance through visual and haptic guidance; and evaluating the effectiveness of these algorithms on clinically-relevant outcomes in training and intervention using a surgical robotic platform. A key innovation of this work is designing control methods to be agnostic of the specific task performed by the human operator: only user-centric metrics and movement data will serve as inputs to novel difficulty and stylistic prediction models that will then be used to create adaptive control algorithms. This work could lead to significant improvements in the adaptability, capability, and usability of teleoperated surgical systems when collaborating with a human user.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.

用户可以更好地执行任务并更自信地执行的人类控制策略是远程手工机器人技术的重要领域。但是，人类行为通常会因环境​​，身体，情感和社会因素而改变。这个教师早期职业发展计划（职业）项目的目标是设计自适应控制系统，以解释和反应动态的人类用户。这项研究将在手术机器人技术领域产生影响，在手术机器人技术领域，与患者的相互作用要求人类操作员（外科医生）和机器人系统本身的安全性和有效性。意识到并响应用户技能和性能样式的机器人系统可以更避免用户错误并在无法预测的环境中响应不良事件。 通过将用户意图，运动风格和专业知识水平的实时模型与外科机器人平台相结合，该项目将通过探索在杂技机器人技术的背景下探索基本关系人类的行为，运动控制和机器操纵来促进NSF的使命，以促进科学的进步并提高国家健康。该项目通过促进与医疗模拟和培训有关的医疗保健和技术开发有关的创新活动来支持教育和扩大工程的参与。该职业项目的目的是为远程手工的机器人手术系统开发自适应控制算法，这些算法可以根据以用户为中心的行为和任务难度的模型来应对，忽略和/或增强人体运动控制输入的输入。模型输出将基于实时，数据驱动的预测和人类意图，手术风格和专业水平的解释。研究目标包括：开发在非结构化远程操作任务中建模和控制人类行为（例如用户行为和专业知识）的方法；设计和分析适应性控制法，以通过视觉和触觉指导提高性能；并评估这些算法对使用手术机器人平台在培训和干预方面与临床相关结果的有效性。这项工作的一个关键创新是设计控制方法是人类操作员执行的特定任务的不可知论：只有以用户为中心的指标和移动数据将作为新型难度和风格预测模型的输入，然后将其用于创建自适应控制算法。这项工作可能会导致与人类用户合作时，索取手术系统的适应性，能力和可用性的显着提高。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子的智力优点和更广泛的影响来通过评估来支持。