Title: CPS: Breakthrough: Compositional Modeling of Cyberphysical Systems This project is devoted to the discovery of new mathematical modeling techniques for Cyber-Physical Systems. In particular, the research involves devising novel conceptual methods for assembling systems from subsystems, and for reasoning about the behavior of systems in terms of the behavior of their subsystems, which may be computational or physical. The results enable scientists and engineers to develop more realistic models of the systems they are designing, and to obtain greater insights into their eventual behavior, without having to build costly prototypes. The intellectual merits are the new notions of system behavior being developed that unify the computational and the physical, and the mathematical operators and laws governing the relationships between systems and subsystems. The project's broader significance and importance lie in the increased pace of innovation within Cyber-Physical System design that the new modeling techniques make possible, and the curricular enhancements that the novel conceptual frameworks under development support. The specific research program of this project involves the development of a novel modeling paradigm, Generalized Synchronization Trees (GSTs), into a rich framework for both describing Cyber-Physical Systems (CPSs) and studying their behavior under interconnection. GSTs are inspired by Milner's use of Synchronization Trees (STs) to model interconnected computing processes, but GSTs generalize the mathematical structure of their forebears in such a way as to encompass systems with discrete ("Cyber") as well as continuous ("Physical") dynamics. As Milner did with STs, the PIs are developing an algebraic theory of composition for GSTs. Such theories have a particular advantage over non-algebraic ones: because the composition of two (or more) objects results in an object of the same type, composition operators can be nested to build large structures out of smaller ones. Thus, the theory of GSTs is inherently compositional. The development of the theory involves five distinct but complementary endeavors. Standard models for cyber-physical systems are being encoded as GSTs in a semantically robust way; meaningful notions of composition and congruence for CPSs are being described and studied algebraically; the interplay between behavioral equivalence and the preservation of system properties is being investigated; a notion of real-time (or clock time) is under consideration for GSTs; and GSTs are being assessed as modeling tools for practical design scenarios.

Title: CPS: Breakthrough: A Mathematical Theory of Cyber-Physical Systems The fundamental challenge in cyber-physical systems is the confluence of distinct scientific and engineering models, methods, and tools for cyber and physical systems. Cyber systems are primarily about processing information. Physical systems are primarily about structure and dynamics, the evolution of state in time. This project develops a mathematical theory of cyber-physical systems that provides a formal interface between the cyber and the physical. The intellectual merits of the project are a solid basis for the modeling and design as well as the implementation and verification of cyber-physical systems, and a fruitful connection of the nascent discipline of cyber-physical-systems engineering with standard mathematical practice. The project's broader significance and importance are providing a sound foundation by which cyber-physical system technologies can be assessed, and enabling the discipline of cyber-physical-system engineering to evolve into a mature and durable field of study. The project builds on the theory of generalized ultrametric semilattices and the fixed-point theory of strictly contracting functions on generalized ultrametric semilattices to develop a cyber-physical domain theory, providing a firm mathematical footing for arbitrary composition and higher-order behavior, formulating the right notion of convergence and continuity for cyber-physical computation, and developing a notion of approximation and effectiveness that allows for a two-way connection between the abstractions of the theory and the realizations of practice. It further applies the theory to a wide range of classic problems of modeling and simulating mixed discrete and continuous phenomena, and extends it to embrace the discrete interventions of a cyber subsystem on its physical counterpart in a cyber-physical system. It also investigates the practical implications of the theory for the implementation and verification of cyber-physical systems by reexamining currently used timed models of computation through the prism of the theory, exploring the extension of programming languages with temporal constructs that are complete over the theoretical abstractions, and integrating the theory in automated and interactive theorem provers to supplement existing model-checking methods that might succumb to the scale of cyber-physical systems.

Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells into tissues and organs. While recent technologies such as tissue- and organoid on-a-chip have the potential to produce a paradigm shift in tissue engineering and drug development, the synthesis of user-specified, emergent behaviors in cell populations is a key step to unlock this potential and remains a challenging, unsolved problem. This project brings together synthetic biology and micron-scale mobile robotics to define the basis of a next-generation cyber-physical system (CPS) called biological CPS (bioCPS). Synthetic gene circuits for decision making and local communication among the cells are automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which is designed and built as part of this project, is used to generate desired patterns in networks of engineered cells. In RA, the engineered cells interact with a set of micro-robots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior. The micro-robots include both living and non-living matter (engineered cells attached to inorganic substrates that can be controlled using externally applied fields). This technology is applied to test the formation of various patterns in living cells. The project has a rich education and outreach plan, which includes nationwide activities for CPS education of high-school students, lab tours and competitions for high-school and undergraduate students, workshops, seminars, and courses for graduate students, as well as specific initiatives for under-represented groups. Central to the project is the development of theory and computational tools that will significantly advance that state of the art in CPS at large. A novel, formal methods approach is proposed for synthesis of emergent, global behaviors in large collections of locally interacting agents. In particular, a new logic whose formulas can be efficiently learned from quad-tree representations of partitioned images is developed. The quantitative semantics of the logic maps the synthesis of local control and communication protocols to an optimization problem. The project contributes to the nascent area of temporal logic inference by developing a machine learning method to learn temporal logic classifiers from large amounts of data. Novel abstraction and verification techniques for stochastic dynamical systems are defined and used to verify the correctness of the gene circuits in the BDA workflow.

Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells into tissues and organs. While recent technologies such as tissue- and organoid on-a-chip have the potential to produce a paradigm shift in tissue engineering and drug development, the synthesis of user-specified, emergent behaviors in cell populations is a key step to unlock this potential and remains a challenging, unsolved problem. This project brings together synthetic biology and micron-scale mobile robotics to define the basis of a next-generation cyber-physical system (CPS) called biological CPS (bioCPS). Synthetic gene circuits for decision making and local communication among the cells are automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which is designed and built as part of this project, is used to generate desired patterns in networks of engineered cells. In RA, the engineered cells interact with a set of micro-robots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior. The micro-robots include both living and non-living matter (engineered cells attached to inorganic substrates that can be controlled using externally applied fields). This technology is applied to test the formation of various patterns in living cells. The project has a rich education and outreach plan, which includes nationwide activities for CPS education of high-school students, lab tours and competitions for high-school and undergraduate students, workshops, seminars, and courses for graduate students, as well as specific initiatives for under-represented groups. Central to the project is the development of theory and computational tools that will significantly advance that state of the art in CPS at large. A novel, formal methods approach is proposed for synthesis of emergent, global behaviors in large collections of locally interacting agents. In particular, a new logic whose formulas can be efficiently learned from quad-tree representations of partitioned images is developed. The quantitative semantics of the logic maps the synthesis of local control and communication protocols to an optimization problem. The project contributes to the nascent area of temporal logic inference by developing a machine learning method to learn temporal logic classifiers from large amounts of data. Novel abstraction and verification techniques for stochastic dynamical systems are defined and used to verify the correctness of the gene circuits in the BDA workflow.

Computer systems are increasingly coming to be relied upon to augment or replace human operators in controlling mechanical devices in contexts such as transportation systems, chemical plants, and medical devices, where safety and correctness are critical. A central problem is how to verify that such partially automated or fully autonomous cyber-physical systems (CPS) are worthy of our trust. One promising approach involves synthesis of the computer implementation codes from formal specifications, by software tools. This project contributes to this "correct-by-construction" approach, by developing scalable, automated methods for the synthesis of control protocols with provable correctness guarantees, based on insights from models of human behavior. It targets: (i) the gap between the capabilities of today's hardly autonomous, unmanned systems and the levels of capability at which they can make an impact on our use of monetary, labor, and time resources; and (ii) the lack of computational, automated, scalable tools suitable for the specification, synthesis and verification of such autonomous systems. The research is based on study of modular reinforcement learning-based models of human behavior derived through experiments designed to elicit information on how humans control complex interactive systems in dynamic environments, including automobile driving. Architectural insights and stochastic models from this study are incorporated with a specification language based on linear temporal logic, to guide the synthesis of adaptive autonomous controllers. Motion planning and other dynamic decision-making are by algorithms based on computational engines that represent the underlying physics, with provision for run-time adaptation to account for changing operational and environmental conditions. Tools implementing this methodology are validated through experimentation in a virtual testing facility in the context of autonomous driving in urban environments and multi-vehicle autonomous navigation of micro-air vehicles in dynamic environments. Education and outreach activities include involvement of undergraduate and graduate students in the research, integration of the research into courses, demonstrations for K-12 students, and recruitment of research participants from under-represented demographic groups. Data, code, and teaching materials developed by the project are disseminated publicly on the Web.

Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells into tissues and organs. While recent technologies such as tissue- and organoid on-a-chip have the potential to produce a paradigm shift in tissue engineering and drug development, the synthesis of user-specified, emergent behaviors in cell populations is a key step to unlock this potential and remains a challenging, unsolved problem. This project brings together synthetic biology and micron-scale mobile robotics to define the basis of a next-generation cyber-physical system (CPS) called biological CPS (bioCPS). Synthetic gene circuits for decision making and local communication among the cells are automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which is designed and built as part of this project, is used to generate desired patterns in networks of engineered cells. In RA, the engineered cells interact with a set of micro-robots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior. The micro-robots include both living and non-living matter (engineered cells attached to inorganic substrates that can be controlled using externally applied fields). This technology is applied to test the formation of various patterns in living cells. The project has a rich education and outreach plan, which includes nationwide activities for CPS education of high-school students, lab tours and competitions for high-school and undergraduate students, workshops, seminars, and courses for graduate students, as well as specific initiatives for under-represented groups. Central to the project is the development of theory and computational tools that will significantly advance that state of the art in CPS at large. A novel, formal methods approach is proposed for synthesis of emergent, global behaviors in large collections of locally interacting agents. In particular, a new logic whose formulas can be efficiently learned from quad-tree representations of partitioned images is developed. The quantitative semantics of the logic maps the synthesis of local control and communication protocols to an optimization problem. The project contributes to the nascent area of temporal logic inference by developing a machine learning method to learn temporal logic classifiers from large amounts of data. Novel abstraction and verification techniques for stochastic dynamical systems are defined and used to verify the correctness of the gene circuits in the BDA workflow.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

Designing semi-autonomous networks of miniature robots for inspection of bridges and other large civil infrastructure According to the U.S. Department of Transportation, the United States has 605102 bridges of which 64% are 30 years or older and 11% are structurally deficient. Visual inspection is a standard procedure to identify structural flaws and possibly predict the imminent collapse of a bridge and determine effective precautionary measures and repairs. Experts who carry out this difficult task must travel to the location of the bridge and spend many hours assessing the integrity of the structure. The proposal is to establish (i) new design and performance analysis principles and (ii) technologies for creating a self-organizing network of small robots to aid visual inspection of bridges and other large civilian infrastructure. The main idea is to use such a network to aid the experts in remotely and routinely inspecting complex structures, such as the typical girder assemblage that supports the decks of a suspension bridge. The robots will use wireless information exchange to autonomously coordinate and cooperate in the inspection of pre-specified portions of a bridge. At the end of the task, or whenever possible, they will report images as well as other key measurements back to the experts for further evaluation. Common systems to aid visual inspection rely either on stationary cameras with restricted field of view, or tethered ground vehicles. Unmanned aerial vehicles cannot access constricted spaces and must be tethered due to power requirements and the need for uninterrupted communication to support the continual safety critical supervision by one or more operators. In contrast, the system proposed here would be able to access tight spaces, operate under any weather, and execute tasks autonomously over long periods of time. The fact that the proposed framework allows remote expert supervision will reduce cost and time between inspections. The added flexibility as well as the increased regularity and longevity of the deployments will improve the detection and diagnosis of problems, which will increase safety and support effective preventive maintenance. This project will be carried out by a multidisciplinary team specialized in diverse areas of cyber-physical systems and robotics, such as locomotion, network science, modeling, control systems, hardware sensor design and optimization. It involves collaboration between faculty from the University of Maryland (UMD) and Resensys, which specializes in remote bridge monitoring. The proposed system will be tested in collaboration with the Maryland State Highway Administration, which will also provide feedback and expertise throughout the project. This project includes concrete plans to involve undergraduate students throughout its duration. The investigators, who have an established record of STEM outreach and education, will also leverage on exiting programs and resources at the Maryland Robotics Center to support this initiative and carry out outreach activities. In order to make student participation more productive and educational, the structure of the proposed system conforms to a hardware architecture adopted at UMD and many other schools for the teaching of undergraduate courses relevant to cyber-physical systems and robotics. This grant will support research on fundamental principles and design of robotic and cyber-physical systems. It will focus on algorithm design for control and coordination, network science, performance evaluation, microfabrication and system integration to address the following challenges: (i) Devise new locomotion and adhesion principles to support mobility within steel and concrete girder structures. (ii) Investigate the design of location estimators, omniscience and coordination algorithms that are provably optimal, subject to power and computational constraints. (iii) Methods to design and analyze the performance of energy-efficient communication protocols to support robot coordination and localization in the presence of the severe propagation barriers caused by metal and concrete structures of a bridge.

Enhanced Structural Health Monitoring of Civil Infrastructure Systems by Observing and Controlling Loads using a Cyber-Physical System Framework The economic prosperity of the nation is dependent on vast networks of civil infrastructure systems. Unfortunately, large fractions of these infrastructure systems are rapidly approaching the end of their intended design lives. The national network of highway bridges is especially vulnerable to age-based deterioration as revealed by recent catastrophic bridge collapses in the United States. Two major bottlenecks currently exist that severely limit the effectiveness of existing bridge health management methods. First, the causal relationship between repeated truck loading and long-term structural deterioration is not well understood. Second, current management methods are reliant on visual inspections which only provide qualitative information regarding bridge health and introduce subjectivity in post-inspection decision making. This project aims to resolve these major bottlenecks by advancing a cyber-physical system (CPS) designed to monitor the health of highway bridges, control the loads imposed on bridges by heavy trucks, and provide visual inspectors with quantitative information for data-driven bridge health assessments. The CPS framework created will have enormous impact on the national economy by enhancing public safety while dramatically improving the cost-effectiveness of infrastructure management methods. The project will also create publically available graduate-level course curricula focused on CPS technology and engages inner-city middle-school students from underrepresented groups to prepare them to pursue careers in the science, technology, engineering, and mathematics (STEM) fields. The overarching goal of the research project is to create a scalable and robust CPS framework for the observation and control of mobile agents that asynchronously and transiently interact with a stationary physical system. While this class of problem is found throughout many engineering disciplines, the project focuses on the health management of highway bridges. The mobile agents relevant to bridge health are the trucks that load and introduce long-term damage in the bridge and inspectors who visually inspect the bridge. The task of devising a robust CPS framework will be challenged by the highly transient nature of the agents involved. Specifically, the compressed time of interaction between the truck and bridge results in tight time constraints on observation, quantification and control of the truck's loading. The project will rely on ad-hoc wireless communications to seamlessly integrate sensors embedded in the mobile agents (trucks and inspectors) with wireless sensors installed on the bridge and with servers dedicated to cloud-based analytics located on the Internet. The project will design the CPS framework to quantify in real-time truck loads based on sensor data streaming into the CPS framework. A distributed computing architecture will be created for the CPS framework to automate the decomposition of computational tasks in order to dramatically improve the speed and efficiency of the framework's data processing capabilities. Finally, the CPS framework will establish ad-hoc feedback control of the mobile agents in order to control mobile agent-stationary system interactions. In particular, feedback control of an instrumented truck allows the CPS framework to control the loads imposed on the bridge for improved health assessments. The CPS framework will be further extended to control visual inspection processes by providing inspectors with recommend inspection actions based on rigorous analysis of collected sensor data. The intellectual significance of the CPS framework is that it observes and controls truck loads on highway bridges for the first time while creating an entirely new data-driven paradigm for more accurate health assessment of infrastructure systems.

Title: CPS: Breakthrough: Development of Novel Architectures for Control and Diagnosis of Safety-Critical Complex Cyber-Physical Systems This project is developing novel architectures for control and diagnosis of complex cyber-physical systems subject to stringent performance requirements in terms of safety, resilience, and adaptivity. These ever-increasing demands necessitate the use of formal model-based approaches to synthesize provably-correct feedback controllers. The intellectual merit of this research lies in a novel combination of techniques from the fields of dynamical systems, discrete event systems, reactive synthesis, and graph theory, together with new advancements in terms of abstraction techniques, computationally efficient synthesis of control and diagnosis strategies that support distributed implementations, and synthesis of acquisition of information and communication strategies. The project's broader significance and importance are demonstrated by the expected improvement of the safety, resilience, and performance of complex cyber-physical systems in critical infrastructures as well as the efficiency with which they are designed and certified. The original approach being developed is based on the combination of multi-resolution abstraction graphs for building discrete models of the underlying cyber-physical system with reactive synthesis techniques that exploit a representation of the solution space in terms of a finite structure called a decentralized bipartite transition system. The concepts of abstraction graph and decentralized bipartite transition system are novel and open new avenues of investigation with significant potential to the formal synthesis of safe, resilient, and adaptive controllers. This methodology naturally results in a set of decentralized and asynchronous controllers and diagnosers, which ensures greater resilience and adaptivity. Overall, this research will significantly impact the Science of Cyber-Physical Systems and the Engineering of Cyber-Physical Systems.