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: 1830403
• 负责人: Michael Yip
• 金额: $ 38.92万
• 依托单位: University of California-San Diego
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830403&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 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.

这个国家机器人计划（NRI）项目将通过开发一种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的进步和国家的繁荣。仿生肌肉执行器，或机器人肌肉，是一种非常接近自然界中生物肌肉特性的执行器。在不同的肌肉驱动器中，驱动器的选择和设计以及建模和控制行为和特性方面存在一系列共同的挑战。该奖项支持识别和统一建模，控制和设计策略，以降低肌肉驱动机器人研究的障碍。 为设计师降低机器人肌肉风险的一项重大努力是创建一个机器人肌肉工具包，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推广民主化。设计挑战和竞赛将激发对机器人肌肉感兴趣的人的兴趣和社区建设，从K-12到大学生和研究人员。 在圣地亚哥和大波士顿地区社区的推广计划的本地安排，以激起对机器人的兴趣，为代表性不足的minority.A新颖的，数据驱动的过程和结构参数化的一组共同的仿生功能，定义肌肉驱动将被开发，以锚建模，控制和设计策略，在所谓的性能和配置空间。这些方法可以跨肌肉类型使用，并且可以推广到目前可用的肌肉致动器之外。有一个策略，肌肉选择的多维性能标准，结合了经验数据和物理模型。肌肉配置放大沿着这些性能尺寸的设计策略，概括在肌肉，将进行调查。新的技术将能够很容易地加入到这个框架中，以进行定量比较研究，而无需复制黄金标准的执行器。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.