Reliable operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical for the seamless functioning of a vibrant economy. Sustained power outages can lead to major disruptions over large areas costing millions of dollars. Efficient computational techniques and tools that curtail such systematic failures by performing fault diagnosis and prognostics are therefore necessary. The Smart Electric Grid is a CPS: it consists of networks of physical components (including generation, transmission, and distribution facilities) interfaced with cyber components (such as intelligent sensors, communication networks, and control software). This grant provides funding to develop new methods to build models for the smart grid representing the failure dependencies in the physical and cyber components. The models will be used to build an integrated system-wide solution for diagnosing faults and predicting future failure propagations that can account for existing protection mechanisms. The original contribution of this work will be in the integrated modeling of failures on multiple levels in a large distributed cyber-physical system and the development of novel, hierarchical, robust, online algorithms for diagnostics and prognostics. If successful, the model-based fault diagnostics and prognostics techniques will improve the effectiveness of isolating failures in large systems by identifying impending failure propagations and determining the time to critical failures that will increase system reliability and reduce the losses accrued due to failures. This work will bridge the gap between fault management approaches used in computer science and power engineering that are needed as the grid becomes smarter, more complex, and more data intensive. Outcomes of this project will include modeling and run-time software prototypes, research publications, and experimental results in collaborations with industry partners that will be made available to the scientific community.

The goal of the project is the development of the theory, hardware and computational infrastructure that will enable automatically transforming user-defined, high-level tasks such as inspection of hazardous environments and object retrieval, into provably-correct control for modular robots. Modular robots are composed of simple individual modules; while a single module has limited capabilities, connecting multiple modules in different configurations allows the system to perform complex actions such as climbing, manipulating objects, traveling in unstructured environments and self-reconfiguring (breaking into multiple independent robots and reassembling into larger structures). The project includes (i) defining and populating a large library of perception and actuation building blocks both manually through educational activities and automatically through novel algorithms, (ii) creating automated tools to assign values to probabilistic metrics associated with the performance of library components, (iii) developing a grammar and automated tools for control synthesis that sequence different components of the library to accomplish higher level tasks, if possible, or provide feedback to the user if the task cannot be accomplished and (iv) designing and building a novel modular robot platform capable of rapid and robust self-reconfiguration. This research will have several outcomes. First, it will lay the foundations for making modular robots easily controlled by anyone. This will enrich the robotic industry with new types of robots with unique capabilities. Second, the research will create novel algorithms that tightly combine perception, control and hardware capabilities. Finally, this project will create an open-source infrastructure that will allow the public to contribute basic controllers to the library thus promoting general research and social interest in robotics and engineering.

This project is a modular, computationally-distributed multi-robot cyberphysical system (CPS) for assisting young developmentally-delayed children in learning to walk. The multi-robot CPS is designed to function in the same way as an adult assisting a child in learning to walk (addressing the research target area of a science of CPS by introducing developmental rehabilitation robotics). It addresses the research target area of new CPS technology by introducing a multi-robot system: 1) a multi-cable scaffold robot that continuously modulates the stabilization of medio-lateral and anterior-posterior sway, and 2) a soft, wearable, exosuit robot with embedded sensing and actuation, which assists with stance push off and swing flexion. The objective is to build a prototype multi-robot CPS and perform tests with human subjects to evaluate the CPS functionality, safety, and interoperability (addressing the research target area of engineering CPS). Longitudinal tests of typically developing and developmentally delayed children learning to walk with or without assistance of the multi-robot CPS are conducted in a motion capture laboratory. Body center of mass behavior as well as gait parameters of walking are measured as the two robots work together to assist the child in maintaining balance and propelling the body forward with each step. This exosuit/scaffolding multi-robot technology will advance knowledge within engineering with bio-inspired soft components, including miniature pneumatic artificial muscle actuators with embedded sensors that enable the control of the muscles in real time. The bio-inspired architecture and material components of the exosuit will make possible a new generation of ?smart fabric? that acts in concert with the body for efficient energy use. The exosuit is part of a larger modular design that makes it possible to couple it to additional assistive robots via a modular communications network. Together, the exosuit, scaffold robot, and wireless communications network for modular CPS, will advance knowledge for the engineering of other CPS that require high levels of interoperability and safety, such as medical CPS. The multi-robot CPS is designed for children who are developmentally delayed as a result of early brain injury. The long term consequences of early brain injury, e.g., in children born prematurely, constitute a major health problem and a significant emotional and financial burden for families and society. The use of a multi-robot cyberphysical system as part of a rehabilitation program may be able to harness the potential of the nervous system for plasticity, the ability to re-organize its structure, function, and connections. The focus is on young children with a history of early brain injury due to prematurity. However, this new cyberphysical system will have a much broader impact in restoring function throughout the life span. Neuroplasticity is not just an immediate response to injury, but occurs throughout the developmental period, providing an opportunity to promote repair and re-education, and restore function. A key to this broad application is the developmentally-motivated, modular structure and interoperability of the exosuit.

The objective of this research is to develop a comprehensive theoretical and experimental cyber-physical framework to enable intelligent human-environment interaction capabilities by a synergistic combination of computer vision and robotics. Specifically, the approach is applied to examine individualized remote rehabilitation with an intelligent, articulated, and adjustable lower limb orthotic brace to manage Knee Osteoarthritis, where a visual-sensing/dynamical-systems perspective is adopted to: (1) track and record patient/device interactions with internet-enabled commercial-off-the-shelf computer-vision-devices; (2) abstract the interactions into parametric and composable low-dimensional manifold representations; (3) link to quantitative biomechanical assessment of the individual patients; (4) facilitate development of individualized user models and exercise regimen; and (5) aid the progressive parametric refinement of exercises and adjustment of bracing devices. This research and its results will enable us to understand underlying human neuro-musculo-skeletal and locomotion principles by merging notions of quantitative data acquisition, and lower-order modeling coupled with individualized feedback. Beyond efficient representation, the quantitative visual models offer the potential to capture fundamental underlying physical, physiological, and behavioral mechanisms grounded on biomechanical assessments, and thereby afford insights into the generative hypotheses of human actions. Knee osteoarthritis is an important public health issue, because of high costs associated with treatments. The ability to leverage a quantitative paradigm, both in terms of diagnosis and prescription, to improve mobility and reduce pain in patients would be a significant benefit. Moreover, the home-based rehabilitation setting offers not only immense flexibility, but also access to a significantly greater portion of the patient population. The project is also integrated with extensive educational and outreach activities to serve a variety of communities.

This proposal is to collect perishable data on the physical response of the transportation infrastructure in New York City following Hurricane Sandy. It makes use of a new human-in-the-loop smartphone-based crowd-sourcing sensing technology, called TrafficTurk. TrafficTurk is a smartphone application which enables intelligent, human?centric sensing of traffic flows during extreme events. The aftermath of Hurricane Sandy represents a rare opportunity to observe transient behavior of a transportation network in response to a significant loss of physical infrastructure (due to flooding and gas shortages) and cyber infrastructure (due to loss of power for traffic control devices). The data gathered by this project, which will be shared with researchers across the country, will enable study of how traffic dynamics evolve after a major disruption to the cyber and physical components of a transportation infrastructure system. Potential benefits include improved preparedness and response to future disasters.

Intellectual Merit: Recent developments in nanostructures manufacturing, sensing and wireless networking, will soon enable us to deploy Flow-based Cyber-Physical Systems equipped with sensing and actuation capabilities for a broad range of applications. Some of these applications will be safety critical, including water distribution monitoring (i.e., critical national infrastructure systems particularly vulnerable to a variety of attacks, including contamination with deadly agents) and interventional medicine (i.e., a medical branch that makes use of tiny devices introduced in a living body through small incisions, to detect and treat diseases). The goal of this project is to advance our fundamental understanding, through a robust mathematical framework, of emerging field of Flow-based Cyber-Physical System. The project develops new architectures, models, metrics, algorithms and protocols for optimal sensing, communication and actuation in Flow-based Cyber-Physical System deployed on-demand (i.e., reactively, when sensing and actuation is needed) or proactively. Flow-based Cyber Physical Systems consist of mobile sensor nodes and static nodes, aware of their location. For stringent requirements (e.g., form factor, cost, energy budget) nodes may or may not possess node-to-node communication capabilities. Due to the lack of localization infrastructure, mobile sensor nodes infer their location only by proximity to static nodes. Sensor nodes are moved by the flow in the network, detect events of interest and proximity to static nodes, communicate and actuate. This research will enable, for example, water distribution monitoring systems to accurately and timely detect events of interest in the infrastructure and to react to these events. It may enable doctors to detect diseases and deliver medication with microscopic precision. Broader Impacts: Ultimately, the outcomes of this research will have impact on CPS that operate in critical modes and environments and control critical infrastructures and medical applications. The results from this research may also foster new research directions in CPS applications. The PI will integrate the research results in newly approved courses on CPS at Texas A&M and disseminate course materials online through the project website and Rice University Connections Consortium. This project will also offer research opportunities to undergraduate students, underrepresented groups, and high school students participating in the Texas Science Olympiad and National Science Olympiad.

This Cyber-Physical Systems project designs and evaluates a foundational information substrate for efficient, agile, model-driven, human-centered building systems. The approach is to develop software-defined buildings, to shatter existing stovepipe architectures, dramatically reduce the effort to add new functions and applications without forklift upgrades, and expand communications and control capabilities beyond a single stand-alone building to enable groups of buildings to behave cooperatively and in cooperation with the energy grid. We investigate how such Software-Defined Buildings can be founded on a flexible, multi-service and open Building Integrated Operating System (BIOS) that allows applications to run reliably in safe, sandboxed environments. It supports sensor and actuator access, access management, metadata, archiving, and discovery, as well as multiple simultaneously executing programs. Building operators retain supervisory management, controlling application separation physically (access different controls), temporally (change controls at different times), informationally (what information leaves the building), and logically (what actions or sequences thereof are allowable). We construct, deploy, and demonstrate the capabilities of a prototype BIOS in the context of university, residential buildings and closely related industrial processes. Making buildings more efficient, while keeping occupants comfortable, productive, and healthy, is critical to our economy and health. Transforming buildings into agile, human centered cyber-physical systems eliminates waste, while allowing them to be a proactive resource on the electric grid with zero emission renewable supplies. And by providing greater value from the same physical plant, the SDB approach can move beyond cost-to-build and cost-to-operate metrics to broader return-on-investment for new extendable future-proof technologies.

Cyber physical systems (CPSs) are merging into major mobile systems of our society, such as public transportation, supply chains, and taxi networks. Past researchers have accumulated significant knowledge for designing cyber physical systems, such as for military surveillance, infrastructure protection, scientific exploration, and smart environments, but primarily in relatively stationary settings, i.e., where spatial and mobility diversity is limited. Differently, mobile CPSs interact with phenomena of interest at different locations and environments, and where the context information (e.g., network availability and connectivity) about these physical locations might not be available. This unique feature calls for new solutions to seamlessly integrate mobile computing with the physical world, including dynamic access to multiple wireless technologies. The required solutions are addressed by (i) creating a network control architecture based on novel predictive hierarchical control and that accounts for characteristics of wireless communication, (ii) developing formal network control models based on in-situ network system identification and cross-layer optimization, and (iii) designing and implementing a reference implementation on a small scale wireless and vehicular test-bed based on law enforcement vehicles. The results can improve all mobile transportation systems such as future taxi control and dispatch systems. In this application advantages are: (i) reducing time for drivers to find customers; (ii) reducing time for passengers to wait; (iii) avoiding and preventing traffic congestion; (iv) reducing gas consumption and operating cost; (v) improving driver and vehicle safety, and (vi) enforcing municipal regulation. Class modules developed on mobile computing and CPS will be used at the four participating Universities and then be made available via the Web.

This project demonstrates the synergistic use of a cyber-physical infrastructure consisting of smart-phone devices; cloud computing, wireless communication, and intelligent transportation systems to manage vehicles in the complex urban network -- through the use of traffic controls, route advisories and road pricing -- to jointly optimize drivers' mobility and the sustainability goals of reducing energy usage and improving air quality. The system developed, MIDAS-CPS, proactively manages the interacting traffic demand and the available transportation supply. A key element of MIDAS-CPS is the data collection and display device PICT that collects each participating driver's vehicle position, forward images from the vehicle's dashboard, and communication time stamps, and then displays visualizations of predicted queues ahead, relevant road prices, and route advisories. Given the increasing congestion in most of the urban areas, and the rising costs of constructing traffic control facilities and implementing highway hardware, MIDAS-CPS could revolutionize the way traffic is managed on the urban network since all computing is done via clouds and the drivers instantly get in-vehicle advisories with graphical visualizations of predicted conditions. It is anticipated this would lead to improved road safety and lesser drive stress, besides the designed benefits on the environment, energy consumption, congestion mitigation, and driver mobility. This multidisciplinary project is at the cutting edge in several areas: real-time image processing, real-time traffic prediction and supply/demand management, and cloud computing. Its educational impacts include enhancements of curricula and laboratory experiences at participating universities, workforce development, and student diversity.

This project demonstrates the synergistic use of a cyber-physical infrastructure consisting of smart-phone devices; cloud computing, wireless communication, and intelligent transportation systems to manage vehicles in the complex urban network ? through the use of traffic controls, route advisories and road pricing ? to jointly optimize drivers? mobility and the sustainability goals of reducing energy usage and improving air quality. The system developed, MIDAS-CPS, proactively manages the interacting traffic demand and the available transportation supply. A key element of MIDAS-CPS is the data collection and display device PICT that collects each participating driver?s vehicle position, forward images from the vehicle?s dashboard, and communication time stamps, and then displays visualizations of predicted queues ahead, relevant road prices, and route advisories. Given the increasing congestion in most of the urban areas, and the rising costs of constructing traffic control facilities and implementing highway hardware, MIDAS-CPS could revolutionize the way traffic is managed on the urban network since all computing is done via clouds and the drivers instantly get in-vehicle advisories with graphical visualizations of predicted conditions. It is anticipated this would lead to improved road safety and lesser drive stress, besides the designed benefits on the environment, energy consumption, congestion mitigation, and driver mobility. This multidisciplinary project is at the cutting edge in several areas: real-time image processing, real-time traffic prediction and supply/demand management, and cloud computing. Its educational impacts include enhancements of curricula and laboratory experiences at participating universities, workforce development, and student diversity. Additional information on the project is available at http://midas-cps.mobicloud.asu.edu/.