NRI: Small: Understanding neuromuscular adaptations in human-robot physical interaction for adaptive robot co-workers

NRI：小：了解自适应机器人同事的人机物理交互中的神经肌肉适应性

• 批准号: 1317718
• 负责人: Jun Ueda
• 金额: $ 119.94万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1317718&HistoricalAwards=false
• 关键词: NRI; Small; Understanding; neuromuscular; adaptations

The goal of this award is to develop theories, methods, and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction. Human power-assisting systems, e.g., powered lifting devices that aid human operators in manipulating heavy or bulky loads, require physical contact between the operator and machine, creating a coupled dynamic system. This coupled dynamic has been shown to introduce inherent instabilities and performance degradation due to a change in human stiffness; when instability is encountered, a human operator often attempts to control the oscillation by stiffening their arm, which leads to a stiffer system with more instability. The project will establish control algorithms for robot co-workers that proactively adjust the contact impedance between the operator and robotic manipulator for achieving higher performance and stability. This research will 1) understand the association between neuromuscular adaptations and system performance limits, 2) develop probabilistic methods to classify and predict the transition of operator's cognitive and physical states from physiological measures, and 3) integrate this knowledge into a structure of shared human-robot and demonstrate the efficacy in a powered lifting device with real-world constraints at vehicle assembly facilities. If successful, the research will benefit the communities interested in the adaptive shared control approach for advanced manufacturing and process design, including automobile, aerospace, and military. Such next-generation manufacturing is expected to improve productivity and reduce assembly time as well as physical burden of assembly line workers. Research outcomes will be integrated into current courses at both graduate and undergraduate levels. Students will be recruited from interdisciplinary and multicultural groups including under-represented groups. K-12 outreach will be carried out in conjunction with Georgia Tech Student and Teacher Enhancement Partnership Program and a summer robot camp in a local non-profit association. An online portal is maintained for dissemination.

该奖项的目标是开发理论，方法和工具，以了解人机物理交互中神经运动适应的机制。 人力辅助系统，例如，帮助操作人员操纵重的或庞大的负载的动力提升装置需要操作人员和机器之间的物理接触，从而产生耦合的动态系统。这种耦合的动态已被证明是引入固有的不稳定性和性能退化，由于人类刚度的变化，当遇到不稳定性，人类操作员往往试图通过加强他们的手臂控制振荡，这导致一个更僵硬的系统与更多的不稳定性。 该项目将为机器人同事建立控制算法，主动调整操作员和机器人机械手之间的接触阻抗，以实现更高的性能和稳定性。本研究将1）了解神经肌肉适应性和系统性能限制之间的关联，2）开发概率方法来分类和预测操作员的认知和生理测量的物理状态的转变，以及3）将这些知识整合到共享的人机结构中，并在车辆装配设施中展示具有现实约束的动力提升设备的功效。如果成功，该研究将使对先进制造和工艺设计的自适应共享控制方法感兴趣的社区受益，包括汽车，航空航天和军事。这种下一代制造预计将提高生产力，减少装配时间以及装配线工人的体力负担。研究成果将被纳入目前的课程在研究生和本科生水平。学生将从跨学科和多元文化群体，包括代表性不足的群体招募。K-12外展将与格鲁吉亚技术学生和教师增强伙伴关系计划以及当地非营利协会的夏季机器人夏令营一起进行。维护了一个在线门户网站以供传播。