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.

Production as a service (PaaS) defines a new paradigm in manufacturing that will allow designers of new products to query existing manufacturing facilities and receive information about fabrication capabilities and production availability. The access to information such as part cost, part quality, and production time will help new products to be prototyped and scaled-up quickly, while also allowing existing manufacturing facilities to benefit from underutilized equipment and labor. The PaaS framework will include both a front-end query interface for the users and a back-end analysis component. The interface will be designed to connect users with small-, mid-, and large-sized manufacturing facilities, while the scheduling and routing algorithms will provide the flexibility and security protocols needed to guarantee operational and production safety across the range of facilities. Manufacturers that utilize the PaaS framework will reap the potential of meeting customer needs in terms of cost, quality, on-time delivery, while being reactive to changing market forces. With 12 percent of the GDP represented by the manufacturing industry, the manufacturing operational improvements that will result from this EArly-concept Grant for Exploratory Research (EAGER) project have the potential to make a significant impact in the national bottom line. The aim of the PaaS platform is to enable distributed manufacturing plant locations to efficiently coordinate both within one plant location as well as across plant locations to realize a flexible service interface for supporting production management. The intellectual merit of this research lies in the extensions that will be created to the existing science and technology in service-oriented architectures to enable distributed production, while preserving proprietary information of the manufacturing systems. The key software abstraction that enables this innovation comes from the extension to the well-known APIs to capture the sophisticated query logic and diverse production requirements to meet user needs. Routing and scheduling decisions will be optimized by leveraging a global view of the current state of all of the components in the manufacturing facilities. To demonstrate scalability and ensure privacy guarantees across multiple facilities, hierarchical abstraction will be used to hide low-level details and proprietary information. The PaaS framework will transform the way manufacturing companies interact with the emerging high-value market; providing the architecture to drive innovation and enable small-, mid-, and large-scale manufacturing companies across the U.S. to compete for new product business on an even playing field.