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1部分：上LIMB运动障碍源于多种临床状况，包括截肢，脊髓损伤或中风。因此，解决损失的手功能是康复干预措施的主要重点。旨在恢复精细运动控制功能的机器人手和手部外骨骼进行的研究速度很高。这些机器人与神经控制机制的整合也是一个持续的研究方向。我们将通过嵌套控制来开发假肢和可穿戴手控制的手，该控制者基于人脑活动和基于机器人传感器的动态控制，无缝地融合神经控制。这些使用嵌套决策（Hand）系统的手动增强也将为用户提供基本的触觉反馈。手工设计框架将有助于辅助和增强机器人技术领域。最终的技术将改善肢体功能失去的个体的生活质量。该项目将帮助培训熟练应对多学科挑战的工程师。通过宣传活动，将在K-12级促进STEM职业，将招募来自代表性不足的工程团体的个人参与该研究项目，该项目将有助于STEM Workforce的多样性：该团队先前介绍了人类在卢比的网络网络物理系统（HILCP）的概念。使用HILCPS硬件软件共同设计和自动合成基础架构，我们将开发假肢和可穿戴的手动系统，这些手动系统对生理信号的人类意图不确定性是牢固的。一个挑战是由人与网络系统共同在同一物理元素上运作的事实。来自算法设计环境的联网实时应用程序的合成带来了框架挑战。这些将通过紧密耦合的最佳嵌套控制策略来解决，该策略依赖于EEG-EMG-Context融合来进行人类意图推断。定制分布式嵌入式计算和机器人平台将建立并迭代精制。这项工作将增强HILCPS设计框架，同时为身体/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应用域开发理论EEG-EMG-CONTEXT融合框架； （2）开发和设计新颖的控制理论解决方案，以高级人类意图和环境智能来处理不确定性，混合运动/力量计划，以鲁棒性执行日常操纵活动； （3）进一步开发和完善Hilcps域特异性设计框架，以使HILCPS算法快速部署到分布式嵌入式系统上，从而赋予实现分布式嵌入式感应，分析和决策的新型实时算法； （4）通过假肢/可穿戴手来开发新的范式来替换，再训练或增强手部功能，以逐拟主体优化性能。