This project, investigating formal languages as a general methodology for task transfer between distinct cyber-physical systems such as humans and robots, aims to expand the science of cyber physical systems by developing Motion Grammars that will enable task transfer between distinct systems. Formal languages are tools for encoding, describing and transferring structured knowledge. In natural language, the latter process is called communication. Similarly, we will develop a formal language through which arbitrary cyber-physical systems communicate tasks via structured actions. This investigation of Motion Grammars will contribute to the science of human cognition and the engineering of cyber-physical algorithms. By observing human activities during manipulation we will develop a novel class of hybrid control algorithms based on linguistic representations of task execution. These algorithms will broaden the capabilities of man-made systems and provide the infrastructure for motion transfer between humans, robots and broader systems in a generic context. Furthermore, the representation in a rigorous grammatical context will enable formal verification and validation in future work. Broader Impacts: The proposed research has direct applications to new solutions for manufacturing, medical treatments such as surgery, logistics and food processing. In turn, each of these areas has a significant impact on the efficiency and convenience of our daily lives. The PIs serve as coordinators of graduate/undergraduate programs and mentors to community schools. In order to guarantee that women and minorities have a significant role in the research, the PIs will annually invite K-12 students from Atlanta schools with primarily African American populations to the laboratories. One-day robot classes will be conducted that engage students in the excitement of hands-on science by interactively using lab equipment to transfer their manipulation skills to a robot arm.

This grant supports the 2nd CPS National Principal Investigators Meeting, held August 1-2, 2011, at the Gaylord National Hotel, National Harbor, Maryland. The purpose of this meeting is to review research progress, enhance research interaction among projects funded under the NSF Cyber-Physical Systems program, and to provide a forum for stakeholders in academia, industry and federal agencies to review new developments in CPS foundations and domains. It provides an opportunity for the CPS community to promote new, emerging applications, and to collectively identify technology gaps and barriers.

This project, generalizing mean-field approaches from physics and chemistry for integrated design of scalable, network resource aware, distributed control strategies for multi-agent robotic systems, aims to develop macroscopic models that retain salient features of the underlying multi-agent robotic system and use these models in the design of distributed control strategies. For complex cyber physical systems, this promises to provide a novel design methodology that is potentially applicable to a large class of systems and, therefore, will result in foundational knowledge of use to the community at large. This high-risk, high-reward project integrates ideas from physics, chemistry, control theory, and robotics to develop new theoretical foundations for the design, validation, and improvement of coordination strategies for multi-agent robotic systems. The project's intellectual merit lies in the ensemble approach towards the design, validation, and improvement of cyber physical systems. Mean-field methods provide a system-level abstraction of the underlying distributed system while retaining the salient features of the various agent-level interactions. The generalization of these models to ensembles of interacting engineered systems provides new methods for designing distributed controllers that are sensitive to changing network resources and whose performance can be predicted and adjusted to achieve both the desired short-term and long-term performance specifications. Broader Impacts: The broader impacts of this project are twofold. First, the mean-field approach takes into account network resource usage and management, providing an integrated strategy for designing scalable decentralized control and coordination strategies. Second, different from biologically-inspired approaches, the mean-field approach enables the design of distributed coordination strategies whose performance can be systematically predicted and tuned to meet detailed performance specifications. This has the potential to unify various existing multi-agent coordination approaches. The research outcomes will be disseminated through publications in technical conferences and journals and incorporated into the PI's existing undergraduate and graduate curriculum and K-12 outreach efforts targeted at increasing female participation in STEM fields.

Cyber-physical systems (CPS) are becoming the key enabler in many engineering domains from traffic management to autonomous vehicles. Concurrency, failures, and their interactions with the physical environment make it challenging to wrestle a high level of confidence from such systems. This project develops a reusable middleware service which enables the creation of verified and hence reliable distributed CPS by pushing the state-of-the-art in two directions: (1) Existing distributed services cannot be practically implemented because of high communication costs incurred in the face of dynamic failures and changes. This project develops a Group Communication Service (GCS) which can be implemented with reasonable resources and which guarantees automatic recovery after failures (stabilization). (2) Existing verification techniques focus on non-distributed CPS, and in general systems with failures, message delays, etc., are unlikely to be amenable to automated analysis. For applications built with the GCS, the project develops a suite of verification tools that exploit stabilization, compositionality, abstraction-refinement, and delay insensitivity of applications. These core research tasks will lead to fundamental advances in design and verification of hybrid and distributed systems. The outcomes of this project are expected to bolster the dependability of emerging applications in autonomous vehicles and factories, and intelligent surveillance systems, while keeping the development costs acceptable through automation. Through industry collaborations, the research outcomes will be translated into engineering practices. The educational component will provide course and lab modules for graduate, undergraduate, and high-school students with the aim of unifying the physical and the computational viewpoints in the systems curriculum. Through active recruitment and mentoring, women and minority students will be prepared for careers in scientific research.

The Cyber-Physical Systems Virtual Organization (CPS-VO) was founded by NSF in 2010 to: (i) facilitate and foster interaction and exchanges among CPS PIs and their teams; (ii) enable sharing of artifacts and knowledge generated by the projects with the broader engineering and scientific communities; and (iii) facilitate and foster collaboration and information exchange between CPS researchers and industry. During the last five years, the CPS-VO has become the focal point of the CPS community in the US and it has played a significant role in catalyzing CPS research world-wide. The CPS-VO Portal serves as a central information repository and as a collaboration platform for the rapidly growing research community. It is the home to ~200 special interest groups, reaches ~10,000 members, and includes ~20,000 webpages and ~24,000 files capturing the first 8 years of CPS history (as of 1-Oct-2015). This proposal looks to envision how the CPS-VO will be transformed over the next few years into a resource which becomes a "destination for doing" rather than a repository and collaboration capability. In this proposal we address the next phase of development of the CPS-VO: (1) changing the Portal from being a passive information repository and collaboration platform to becoming an active resource as research tool for CPS, (2) serving as an integration platform for open source CPS tools and models emerging from the research community and (3) making the Portal an active resource for CPS education. Active Resources encapsulate the new capabilities of the CPS-VO. Research teams may contribute to Active Resources on three different levels: (a) end-to-end design and simulation tool chains and test beds including model repositories, tools and web-based user interfaces to access resources, (b) individual tools that can be integrated into design flows, and (c) models and code integrated into open repositories. The proposal will spur CPS community growth through conducting series of student competitions to be held in the first two years building on the unmanned air vehicle design studio from UPenn that will allow students and researchers to study the physical design, dynamics and control of quad rotors, a multi-model simulation system from Vanderbilt facilitating the virtual integration of embedded software for control, estimation, planning, and coordinated, dynamic flight of multiple micro air vehicles. In addition, the CPS VO will extend outreach to the community to identify new and emerging VO needs and provide enhanced user experience through redesign of the user facing portal and integration of new information management technologies.

As self-driving cars are introduced into road networks, the overall safety and efficiency of the resulting traffic system must be established and guaranteed. Numerous critical software-related recalls of existing automotive systems indicate that current design practices are not yet up to this challenge. This project seeks to address this problem, by developing methods to analyze and coordinate networks of fully and partially self-driving vehicles that interact with conventional human-driven vehicles on roads. The outcomes of the research are expected to also contribute to the safety of other cyber-physical systems with scalable configurable hierarchical structures, by developing a mathematical framework and corresponding software tools that analyze the safety and reliability of a class of systems that combine physical, mechanical and biological components with purely computational ones. The project research spans four technical areas: autonomous and human-controlled collaborative driving; scheduling computations over heterogeneous distributed computing systems; security and trust in V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) networks; and Verification & Validation of V2X systems through semi-virtual environments and scenarios. The integrating aspect of this research is the development of a distributed system calculus for Cyber-Physical Systems (CPS) that enables modeling, simulation and analysis of collaborative vehicular systems. The development of a comprehensive framework to model, analyze and test reconfiguration, hierarchical control, security and trust differentiates this research from previous attempts to address the same problem. Educational and outreach activities include integration of project research in undergraduate and graduate courses, and a summer camp at Ohio State University for high-school students through the Women in Engineering program.

The goal of this research is to develop fundamental theory, efficient algorithms, and realistic experiments for the analysis and design of safety-critical cyber-physical transportation systems with human operators. The research focuses on preventing crashes between automobiles at road intersections, since these account for about 40% of overall vehicle crashes. Specifically, the main objective of this work is to design provably safe driver-assist systems that understand driver?s intentions and provide warnings/overrides to prevent collisions. In order to pursue this goal, hybrid automata models for the driver-vehicles-intersection system, incorporating driver behavior and performance as an integral part, are derived from human-factors experiments. A partial order of these hybrid automata models is constructed, according to confidence levels on the model parameters. The driver-assist design problem is then formulated as a set of partially ordered hybrid differential games with imperfect information, in which games are ordered according to parameter confidence levels. The resulting designs are validated experimentally in a driving simulator and in large-scale computer simulations. This research leverages the potential of embedded control and communication technologies to prevent crashes at traffic intersections, by enabling networks of smart vehicles to cooperate with each other, with the surrounding infrastructure, and with their drivers to make transportation safer, more enjoyable, and more efficient. The work is based on a collaboration among researchers in formal methods, autonomous control, and human factors who are studying realistic and provably correct warning/override algorithms that can be readily transitioned to production vehicles.

Until now, the cyber component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved. These factors can be determined from: longer-term traces of in-built sensor data that can be abstracted as triplines, socialized versions of these that are shared amongst vehicle users, and online databases. These three sources of information collectively constitute the automotive infoverse. This project harnesses this automotive infoverse to achieve these goals through high-confidence vehicle tuning and driver feedback decisions. Specifically, the project develops software called Headlight that permits the rapid development of apps that use the infoverse to achieve one or more goals. Advisory apps can provide feedback to the driver in order to ensure better fuel efficiency, while auto-tuning goals can set car parameters to promote safety. Allowing vehicles and such apps to share vehicle data with others and to use extrinsic information results in novel information processing, assurance, and privacy challenges. The project develops methods, algorithms and models to address these challenges. Broader Impact - This project can have significant societal impact by reducing carbon emissions and improving vehicular safety, can spur innovation in tuning methods and encourage researchers to experiment with this class of cyber-physical systems. The active participation of General Motors will strongly facilitate technology transfer. The program has outreach through internships, course material, high school and undergraduate involvement, and through creating an open infrastructure usable by diverse developers.

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 focuses on the formal design of semi-autonomous automotive Cyber Physical Systems (CPS). Rather than disconnecting the driver from the vehicle, the goal is to obtain a vehicle where the degree of autonomy is continuously changed in real-time as a function of certified uncertainty ranges for driver behavior and environment reconstruction. The highly integrated research plan will advance the science and engineering for CPS by developing methods for (1) reconstructing 3D scenes which incorporate high-level topological and low-level metric information, (2) extracting driver behavioral models from large datasets using geometry, reasoning and inferences, (3) designing provably-safe control schemes which trade-off real-time feasibility and conservatism by using the evidence collected during actual driving. Assisting humans in controlling complex and safety-critical systems is a global challenge. In order to improve the safety of human-operated CPS we need to provide guarantees in the reconstruction of the environment where the humans and the CPS operate, and to develop control systems that use predictive cognitive models of the human when interacting with the CPS. A successful and integrated research in both areas will impact not only the automotive sector but many other human-operated systems. These include telesurgery, homeland security, assisted rehabilitation, power networks, environmental monitoring, and all transportation CPS. Graduate, undergraduate and underrepresented engineering students will benefit through classroom instruction, involvement in the research and a continuous interaction with industrial partners who are leaders in the field of assisted driving.