CPS: TTP Option: Synergy: Collaborative Research: Nested Control of Assistive Robots through Human Intent Inference

CPS：TTP 选项：协同：协作研究：通过人类意图推理对辅助机器人进行嵌套控制

• 批准号: 1544895
• 负责人: Deniz Erdogmus
• 金额: $ 60.3万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544895&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Synergy; Collaborative

Part 1: Upper-limb motor impairments arise from a wide range of clinical conditions including amputations, spinal cord injury, or stroke. Addressing lost hand function, therefore, is a major focus of rehabilitation interventions; and research in robotic hands and hand exoskeletons aimed at restoring fine motor control functions gained significant speed recently. Integration of these robots with neural control mechanisms is also an ongoing research direction. We will develop prosthetic and wearable hands controlled via nested control that seamlessly blends neural control based on human brain activity and dynamic control based on sensors on robots. These Hand Augmentation using Nested Decision (HAND) systems will also provide rudimentary tactile feedback to the user. The HAND design framework will contribute to the assistive and augmentative robotics field. The resulting technology will improve the quality of life for individuals with lost limb function. The project will help train engineers skilled in addressing multidisciplinary challenges. Through outreach activities, STEM careers will be promoted at the K-12 level, individuals from underrepresented groups in engineering will be recruited to engage in this research project, which will contribute to the diversity of the STEM workforce.Part 2: The team previously introduced the concept of human-in-the-loop cyber-physical systems (HILCPS). Using the HILCPS hardware-software co-design and automatic synthesis infrastructure, we will develop prosthetic and wearable HAND systems that are robust to uncertainty in human intent inference from physiological signals. One challenge arises from the fact that the human and the cyber system jointly operate on the same physical element. Synthesis of networked real-time applications from algorithm design environments poses a framework challenge. These will be addressed by a tightly coupled optimal nested control strategy that relies on EEG-EMG-context fusion for human intent inference. Custom distributed embedded computational and robotic platforms will be built and iteratively refined. This work will enhance the HILCPS design framework, while simultaneously making novel contributions to body/brain interface technology and assistive/augmentative robot technology. Specifically we will (1) develop a theoretical EEG-EMG-context fusion framework for agile HILCPS application domains; (2) develop theory for and design novel control theoretic solutions to handle uncertainty, blend motion/force planning with high-level human intent and ambient intelligence to robustly execute daily manipulation activities; (3) further develop and refine the HILCPS domain-specific design framework to enable rapid deployment of HILCPS algorithms onto distributed embedded systems, empowering a new class of real-time algorithms that achieve distributed embedded sensing, analysis, and decision making; (4) develop new paradigms to replace, retrain or augment hand function via the prosthetic/wearable HAND by optimizing performance on a subject-by-subject basis.

第一部分：上肢运动障碍是由多种临床情况引起的，包括截肢、脊髓损伤或中风。因此，解决手部功能丧失问题是康复干预的一个主要重点；最近，旨在恢复精细运动控制功能的机械手和外骨骼的研究取得了显著进展。将这些机器人与神经控制机制相结合也是一个正在进行的研究方向。我们将开发通过嵌套控制来控制的假肢和可穿戴的手，这种控制无缝地融合了基于人类大脑活动的神经控制和基于机器人传感器的动态控制。这些使用嵌套决策（Hand）系统的手部增强功能还将为用户提供基本的触觉反馈。HAND设计框架将为辅助和增强机器人领域做出贡献。由此产生的技术将改善失去肢体功能的人的生活质量。该项目将帮助培训工程师熟练应对多学科挑战。通过外展活动，STEM职业将在K-12级别得到提升，来自工程领域代表性不足群体的个人将被招募参与该研究项目，这将有助于STEM劳动力的多样性。第2部分：该团队之前介绍了人在环网络物理系统（HILCPS）的概念。利用HILCPS硬件软件协同设计和自动合成基础设施，我们将开发假肢和可穿戴HAND系统，这些系统对来自生理信号的人类意图推断的不确定性具有鲁棒性。一个挑战来自于人类和网络系统在同一物理元素上共同运作的事实。从算法设计环境中合成网络实时应用对框架提出了挑战。这些将通过紧耦合的最优嵌套控制策略来解决，该策略依赖于脑电图-肌电图-上下文融合来进行人类意图推断。定制的分布式嵌入式计算和机器人平台将被构建和迭代改进。这项工作将增强HILCPS的设计框架，同时为身体/大脑接口技术和辅助/增强机器人技术做出新的贡献。具体而言，我们将(1)为敏捷HILCPS应用领域开发一个理论脑电图-肌电图-上下文融合框架；(2)发展理论和设计新的控制理论解决方案，以处理不确定性，将运动/力规划与高层次的人类意图和环境智能相结合，以稳健地执行日常操作活动；(3)进一步发展和完善HILCPS领域特定设计框架，使HILCPS算法能够快速部署到分布式嵌入式系统上，为实现分布式嵌入式传感、分析和决策的新型实时算法提供支持；(4)通过优化每个受试者的性能，开发新的范例，通过假肢/可穿戴手来替代、再训练或增强手部功能。