NRI: FND: COLLAB: A Foundational Approach to Muscle Actuators that Lowers Barriers to Muscle-Powered Robotics Research

NRI：FND：COLLAB：肌肉驱动器的基础方法，降低肌肉动力机器人研究的障碍

• 批准号: 1830291
• 负责人: Robert Wood
• 金额: $ 36.08万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830291&HistoricalAwards=false
• 关键词: NRI; FND; COLLAB; Foundational; Approach

This National Robotics Initiative (NRI) project will promote the progress of science and advance the national prosperity by developing a foundational approach towards muscle-powered machines that move using biomimetic muscle actuators. Biomimetic muscle actuators, or robot muscles, are actuators that closely mimic the properties of biological muscles in nature. A common set of challenges in actuator selection and design, and modeling and controlling behaviors and properties emerge among different muscle actuators. This award supports identifying and unifying modeling, control, and design strategies that would lower the barrier to muscle-powered robotics research. A significant effort to de-risk robot muscles for designers is the creation of a robot muscles toolkit, an accessible software and hardware toolkit for designing muscle-powered machines and for democratizing the development and proliferation of these technologies at a broad scale. Design challenges and competitions will spur the interests and community building for those interested in robot muscles across all ages and experiences, from K-12 to college students and researchers. Local arrangements for outreach in the San Diego and Greater Boston Area communities are planned for stirring interest in robotics for underrepresented minorities.A novel, data-driven process and structure for parameterizing a common set of biomimetic features that define muscle actuation will be developed in order to anchor modeling, control, and design strategies across a so-called performance and configuration space. These methods can be employed across muscle types and generalize beyond currently available muscle actuators. There is a strategy for muscle selection over multi-dimensional performance criteria that incorporates both empirical data and physics models. Design strategies for muscle configurations for amplification along these performance dimensions, generalizable across muscles, will be investigated. New technologies will be able to be easily added into this framework to perform quantitative comparison studies without replication of gold standard actuators.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.

这一国家机器人倡议(NRI)项目将通过开发使用仿生肌肉执行器移动的肌肉动力机器的基础方法来促进科学进步和国家繁荣。仿生肌肉执行器，或称机器人肌肉，是一种接近模仿自然界中生物肌肉特性的执行器。在不同的肌肉执行器之间，在执行器的选择和设计、建模和控制行为和属性方面出现了一系列共同的挑战。该奖项支持确定和统一建模、控制和设计策略，以降低肌肉驱动机器人研究的门槛。为设计师降低机器人肌肉风险的一项重大努力是创建机器人肌肉工具包，这是一个可访问的软件和硬件工具包，用于设计肌肉驱动的机器，并使这些技术的开发和扩散在广泛的范围内民主化。设计挑战和比赛将刺激那些对机器人肌肉感兴趣的人的兴趣和社区建设，这些人来自各个年龄段和经历，从K-12到大学生和研究人员。计划在圣地亚哥和大波士顿地区社区进行当地的外联安排，以激发未被充分代表的少数民族对机器人技术的兴趣。将开发一种新颖的、数据驱动的过程和结构，用于将定义肌肉驱动的一组常见仿生特征参数化，以便在所谓的性能和配置空间中锚定建模、控制和设计策略。这些方法可以应用于肌肉类型，并推广到目前可用的肌肉执行器之外。有一种肌肉选择的策略，它结合了经验数据和物理模型，超过了多维性能标准。将研究肌肉结构的设计策略，以便沿着这些性能维度进行放大，并可在肌肉中推广。新技术将能够很容易地添加到这个框架中，以执行定量的比较研究，而不需要复制黄金标准执行器。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

Robotic Artificial Muscles: Current Progress and Future Perspectives

• DOI: 10.1109/tro.2019.2894371
• 发表时间: 2019-06-01
• 期刊: IEEE TRANSACTIONS ON ROBOTICS
• 影响因子: 7.8
• 作者: Zhang, Jun;Sheng, Jun;Yip, Michael C.
• 通讯作者: Yip, Michael C.

Controlled flight of a microrobot powered by soft artificial muscles

• DOI: 10.1038/s41586-019-1737-7
• 发表时间: 2019-11-14
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Chen, Yufeng;Zhao, Huichan;Wood, Robert J.
• 通讯作者: Wood, Robert J.

A dynamic electrically driven soft valve for control of soft hydraulic actuators

• DOI: 10.1073/pnas.2103198118
• 发表时间: 2021-08
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Siyi Xu;Yufeng Chen;N. P. Hyun;Kaitlyn P. Becker;R. Wood

Microrobotic laser steering for minimally invasive surgery

• DOI: 10.1126/scirobotics.abd5476
• 发表时间: 2021-01
• 期刊: Science Robotics
• 影响因子: 25
• 作者: Peter A. York;Rut Peña;D. Kent;R. Wood