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.

The electric power grid is a national critical infrastructure that is increasing vulnerable to malicious physical and cyber attacks. As a result, detailed data describing grid topology and components is considered highly sensitive information that can be shared only under strict non-disclosure agreements. There is also increasing need to foster cooperation among the growing number of participants in microgrid-enabled electric marketplace. However, to maintain their economic competitiveness, the market participants are not inclined to share sensitive information about their grid with other participants. Motivated by this need for increased cyber-physical security and economic confidentiality, the project is developing techniques to obfuscate sensitive design information in power system models without jeopardizing the quality of the solutions obtained from such models. Specifically, solution approaches have been developed to hide sensitive structural information in Direct Current (DC) Optimal Power Flow models. These approaches are currently being extended to Alternating Current (AC) Optimal Power Flow models. The project is also developing secure multi-party methods where the market participants collectively optimize the grid operation while only sharing encrypted private sensitive information. Finally, the project is incorporating secure market operations in jointly solving the Optimal Power Dispatch problem without revealing sensitive private information from each participant to other participants. The techniques developed in this project have the potential to broadly impact areas beyond power systems. The general principles developed in the project can be used to mask sensitive information in many problems that can be formulated as a linear or non-linear programming optimization.

The objective of this project is to research tools to manage uncertainty in the design and certification process of safety-critical aviation systems. The research focuses on three innovative ideas to support this objective. First, probabilistic techniques will be introduced to specify system-level requirements and bound the performance of dynamical components. These will reduce the design costs associated with complex aviation systems consisting of tightly integrated components produced by many independent engineering organizations. Second, a framework will be created for developing software components that use probabilistic execution to model and manage the risk of software failure. These techniques will make software more robust, lower the cost of validating code changes, and allow software quality to be integrated smoothly into overall system-level analysis. Third, techniques from Extreme Value Theory will be applied to develop adaptive verification and validation procedures. This will enable early introduction of new and advanced aviation systems. These systems will initially have restricted capabilities, but these restrictions will be gradually relaxed as justified by continual logging of data from in-service products. The three main research aims will lead to a significant reduction in the costs and time required for fielding new aviation systems. This will enable, for example, the safe and rapid implementation of next generation air traffic control systems that have the potential of tripling airspace capacity with no reduction in safety. The proposed methods are also applicable to other complex systems including smart power grids and automated highways. Integrated into the research is an education plan for developing a highly skilled workforce capable of designing safety critical systems. This plan centers around two main activities: (a) creation of undergraduate labs focusing on safety-critical systems, and (b) integration of safety-critical concepts into a national robotic snowplow competition. These activities will provide inspirational, real-world applications to motivate student learning.

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 project designs algorithms for the integration of plug-in hybrid electric vehicles (PEVs) into the power grid. Specifically, the project will formulate and solve optimization problems critical to various entities in the PEV ecosystem -- PEV owners, commercial charging station owners, aggregators, and distribution companies -- at the distribution / retail level. Charging at both commercial charging stations and at residences will be considered, for both the case when PEVs only function as loads, and the case when they can also function as sources, equipped with vehicle-to-home (V2H) or vehicle-to-grid (V2G) energy reinjection capability. The focus of the project is on distributed decision making by various individual players to achieve analytical system-level performance guarantees. Electrification of the transportation market offers revenue growth for utility companies and automobile manufacturers, lower operational costs for consumers, and benefits to the environment. By addressing problems that will arise as PEVs impose extra load on the grid, and by solving challenges that currently impede the use of PEVs as distributed storage resources, this research will directly impact the society. The design principles gained will also be applicable to other cyber-physical infrastructural systems. A close collaboration with industrial partners will ground the research in real problems and ensure quick dissemination of results to the marketplace. A strong educational component will integrate the proposed research into the classroom to allow better training of both undergraduate and graduate students. The details of the project will be provided at http://ee.nd.edu/faculty/vgupta/research/funding/cps_pev.html

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.

This project addresses the impact of the integration of renewable intermittent generation in a power grid. This includes the consideration of sophisticated sensing, communication, and actuation capabilities on the system's reliability, price volatility, and economic and environmental efficiency. Without careful crafting of its architecture, the future smart grid may suffer from a decrease in reliability. Volatility of prices may increase, and the source of high prices may be more difficult to identify because of undetectable strategic policies. This project addresses these challenges by relying on the following components: (a) the development of tractable cross-layer models; physical, cyber, and economic, that capture the fundamental tradeoffs between reliability, price volatility, and economic and environmental efficiency, (b) the development of computational tools for quantifying the value of information on decision making at various levels, (c) the development of tools for performing distributed robust control design at the distribution level in the presence of information constraints, (d) the development of dynamic economic models that can address the real-time impact of consumer's feedback on future electricity markets, and finally (e) the development of cross-layer design principles and metrics that address critical architectural issues of the future grid. This project promotes modernization of the grid by reducing the system-level barriers for integration of new technologies, including the integration of new renewable energy resources. Understanding fundamental limits of performance is indispensable to policymakers that are currently engaged in revamping the infrastructure of our energy system. It is critical that we ensure that the transition to a smarter electricity infrastructure does not jeopardize the reliability of our electricity supply twenty years down the road. The educational efforts and outreach activities will provide multidisciplinary training for students in engineering, economics, and mathematics, and will raise awareness about the exciting research challenges required to create a sustainable energy future.

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 project develops the foundations of modeling, synthesis and development of verified medical device software and systems, from verified closed-loop models of the device and organ(s). The effort spans both implantable medical devices such as cardiac pacemakers and physiological control systems such as drug infusion pumps that have multiple networked medical systems. In both cases, the devices are physically connected to the body and exert direct control over the physiology and safety of the patient-in-the-loop. The goal is to ensure the device will never drive the patient into an unsafe state, while providing effective therapy. The contributions of are in three areas: closed-loop patient-device modeling; quantitative verification for optimized patient-specific devices; platforms for life-critical systems. Integrated modeling methodologies are developed to produce both the functional physiological signals, for clinically relevant testing with a medical device, and also generate the formal timing of device-patient interaction for formal verification. Starting with the problem of verifying the safety and correctness of medical device software, probabilistic patient models based on physiological data are then used to develop quantitative verification techniques to maintain the therapy?s efficacy on the patient and operational efficiency of the device. To facilitate participation of the CPS community, the Food and Drug Administration (FDA), physicians and manufacturers, open source libraries of device/patient models, software tools for verification and model translation and hardware platforms for testing with real medical devices are developed. The closed-loop design and verification techniques for medical device software and patients, developed here, have direct potential benefits on human health, and the quality and cost of medical care. Design of bug-free and safe medical device software is challenging, especially in complex implantable devices that control and actuate organs who's response is not fully understood. Safety recalls of pacemakers and implantable ?cardioverter? defibrillators between 1990 and 2000 affected over 600,000 devices. Of these, 200,000 or 41%, were due to firmware issues (i.e. software) that continue to increase in frequency. There is currently no formal methodology or open experimental platform to test and verify the correct operation of medical device software within the closed-loop context of the patient. If successful, this project has potential to not only increase the safety of such devices, but also to accelerate the development and certification process. The latter could reduce costs, and shorten the time to market for new devices. The project also has an extensive education and outreach component, including curriculum development in medical cyber-physical systems, involvement of undergraduate and graduate students in research, and cooperation with hospitals, makers of medical devices, and the FDA. The cross-cutting nature of the project brings together communities involving clinical physicians, electrical engineers, computer scientists and regulators of health care safety.