Emerging mobility systems, e.g., connected and automated vehicles and shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. However, different levels of vehicle automation in the transportation network can significantly alter transportation efficiency metrics (travel times, energy, environmental impact). Moreover, we anticipate that efficient transportation might alter human travel behavior causing rebound effects, e.g., by improving efficiency, travel cost is decreased, hence willingness-to-travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time. The project will consolidate emerging mobility systems and modes with real-world data and processed information leading to an equitable transportation system with broad economic, environmental, and societal benefits. We expect the outcome of this project to enhance our understanding of the rebound effects, changes in travel demand and capacity, human reception, adoption, and use of emerging mobility systems. 

The outcome of this research will deliver an online learning framework that will aim at distributing travel demand in a given transportation network resulting in a socially-optimal mobility system that travelers would be willing to accept. A ?socially-optimal mobility system? is defined as a mobility system that (1) is efficient (in terms of energy consumption and travel time), (2) does not cause rebound effects, and (3) ensures equity in transportation. The framework will establish new approaches in optimally controlling cyber-physical systems by merging learning and control approaches. It includes the development of new methods to enhance accessibility, safety, and equity in transportation and travelers? acceptance. In the context of the proposed framework, a ?social planner? faces the problem of aggregating the preferences of the travelers into a collective, system-wide decision when the private information of the travelers is not publicly known. Mechanism design theory will be used to derive the optimal routes and the selection of a transportation mode for all travelers so as to maximize accessibility, safety, and equity in transportation and travelers? acceptance. Online learning algorithms for contextual bandit problems will be developed to identify traveler preferences and to determine how they would respond to the social planner?s recommendations on routing and selection of a transportation mode.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The goal of this work is the realization of autonomous cyber-physical, microsystems for automated correction of blurred vision using flexible electronic contact lenses. The vision correction system integrates thin variable power lenses with object distance microsensors and computation and control software to continuously produce sharply-focused images in individuals suffering from presbyopia. Presbyopia, or loss of the eye's ability to change focus, is an inevitable and universal age-related condition that affects aging adults, causing blurred images and visual impairment. In 2018, two billion people worldwide were estimated to suffer from presbyopia. The proposed vision correction microsystem operates autonomously while collecting energy from its surrounding environment in order to provide continuous vision correction for an entire day. Realization of such microsystems advances our scientific knowledge of autonomous microsystem engineering for medical applications, ultimately improving the daily lives and well-being of billions of aging adults while reducing the cost of treatment.

The implementation of autonomous microsystems for vision correction requires deep integration of ultrathin state-of-the-art inhomogeneous microtechnologies including variable power liquid-crystal lenses, paper-thin embedded microprocessors and communications circuits with tens of millions of transistors, paper-thin microsensors to detect light level, user orientation and focal distance, and thin photovoltaic cells with power management circuits, all integrated onto a single flexible package that conforms to the surface of the human eye. The system must be able to scavenge power and manage its operation completely autonomously, in the best possible way, in a resource-limited biological environment. Advancements in systems with these characteristics are widely applicable to many future cyber physical systems (CPS) for medical and health monitoring applications. This extremely ambitious project pushes the frontiers of inhomogeneous microtechnology integration to a level that has been repeatedly dreamed of, but never realized before, to produce a highly integrated CPS that can benefit billions of people.

Complex cyber-physical systems (CPS) that operate in dynamic and uncertain environments will inevitably encounter unanticipated situations during their operation. Examples range from naturally occurring faults in both the cyber and physical components to attacks launched by malicious entities with the purpose of disrupting normal operations. As infrastructures, e.g. energy, transportation, industrial systems and built environments, are getting smarter, the chance of a fault or attack increases. When this happens, it is essential that system behavior remains viable, i.e., it does not violate pre-specified operating constraints on run-time behavior. Preserving safety, for instance, is of paramount importance to avoid damage and possible loss of life. This project will develop strategies for mitigating the effects of such unanticipated situations, that seek to optimize for performance (measured by multiple metrics such as cost, efficiency, accuracy, etc.) without compromising viability. The emphasis will be on the automotive application domain, given the upcoming revolution brought by innovations such as vehicle-to-vehicle (V2V), vehicle to infrastructure (V2I) communication and autonomous driving, and because of the safety-criticality of the transportation infrastructure. To ground our research on relevant problems, we will engage industrial partners. The outcomes of the project will be validated upon test scenarios drawn from the automotive industry. 

Fundamental issues arising when safety-critical CPS operate in uncertain environments will be addressed, with the objective of obtaining a better understanding of, and developing optimal or near-optimal strategies for dealing with, emergent problems that arise from the interaction of resource-allocation and control strategies in such systems. One of the novelties of the technical approach adopted in this project is to closely integrate three different CPS perspectives ? control theory, automotive & aerospace application domain-knowledge, and real-time resource management & scheduling ? in order to develop run-time mitigation strategies for complex CPS's operating in dynamic and uncertain environments, and exposed to a variety of faults. Such an integrated approach will allow for the identification of emergent problems that arise from the interaction of resource-allocation and control algorithms, that may otherwise remain undiscovered if the control and resource-allocation aspects were considered separately.
The general design-time and run-time tools for creating resilient CPSs will be guided by the implementation and evaluation of the research in simulation and on laboratory test-beds upon three applications from the automotive domain: fault resilience for variable-valve internal combustion engines; fail-safe energy management for hybrid-electric vehicles; and robust sensor management for autonomous vehicles.

This Cyber-Physical Systems (CPS) grant will advance structural health monitoring of concrete structures by relying on data acquired by a novel sensing technology with unprecedented scalability and spatial resolution. Modern society depends critically on sound and steadfast functioning of a variety of engineering structures and infrastructures, such as bridges, buildings, pipelines, geotechnical structures, aircrafts, wind turbines, and industrial facilities. Due to aging, massive urbanization, and climate change, there is a growing need for accurate and reliable assessment of the health condition, performance, and operation of these structures in order to ensure their continuous functioning and safe use. The researched technology enables pervasive and scalable sensing of concrete structures with high resolution by transforming concrete into a smart self-sensing material, thereby enabling reliable long-term structural health monitoring. This in turn contributes to the nation?s sustainability and resilience and to advancing the nation?s prosperity, welfare, and security. The project advances multiple core research areas in structural health monitoring including CPS system architectures using embedded devices, multi-parameter sensing and networking based on radio frequency sensors, and machine learning for accurate and reliable data analytics. The research outcomes are highly translational to various other CPS domains. The project also contributes to secondary education and outreach activities in multiple ways as well as to undergraduate and graduate education. 

The aim of this project is to create a novel sensing system comprised of radio frequency sensors that are pervasively embedded in large volumes of concrete structures and that sense their localities using radio frequency properties. The objective is the assessment of key parameters that reflect the behavior of the monitored structure under operational conditions, such as deformation, temperature, and humidity, as well as detection and characterization of damages. The project has the following intellectual contributions: 1) Passive radio frequency-based sensing that operates over a wide range of frequencies; architectures of smart exciters and networked radio frequency sensors that communicate among themselves via backscatter modulation; solar-powered radio frequency exciter platform that powers the sensors. 2) Energy-based sensing and network optimization of the radio frequency sensor network in terms of its monitoring ability and network connectivity given the constraints on the available harvested power at the exciters. 3) Machine learning methods for function estimation based on the principle of ensemble modeling with Gaussian processes and applied to self-localization and to inference of three-dimensional distributions of material parameters within large volumes of concrete structures.

The reach of cyber-physical systems into space is growing exponentially, as launch services proliferate and satellites have become small, cheap, and capable. Unlike expensive satellites of the past, the near future promises constellations of thousands of inexpensive nanosatellites. Nanosatellites are becoming capable of supporting space-based cyber-physical applications, including defense, smart cities, agriculture, & infrastructure, climate science, and search & rescue. Unfortunately, nanosatellites today operate like monolithic satellites of the past, manually operated at high cost, impeding the realization of the potential of nanosatellites for these important cyber-physical systems applications. This project envisions a new operating model for space-based cyber-physical systems in nanosatellite constellations called a "software-defined nanosatellite constellation", which unleashes their potential. A software-defined constellation is a collection of nanosatellites capable of autonomously sensing the environment, processing data, and cooperatively planning and taking mechanical actions. The project realizes this vision through cross-cutting cyber-physical systems research spanning computer systems, control, planning, actuation, machine learning, and communications. This project will culminate in the launch of a software-defined constellation testbed that implements several space-based applications, demonstrating these societally important capabilities and applications, and functioning as a valuable resource for other cyber-physical systems researchers.

This CPS Frontier project is establishing nanosatellite constellations as sophisticated, multi-tenant platforms for space-based cyber-physical systems applications. The work is interdisciplinary, spanning controls, ML, communications, systems, and hardware. The project makes constellations autonomous and equipped to compute efficiently on orbit. On-orbit computing treats constellation-level satellite control and actuation as resource management for unique nanosatellite resources: sensor data, bandwidth, energy, and computing. On-orbit machine learning techniques bring federated learning to the constellation, creating an autonomous orbital learning system. The project?s new communication techniques extract maximum information from each bit communicated, combining weak signals and often avoiding communication altogether. The project demonstrates the project?s value with on-orbit infrastructure and testbeds that form an open platform for future space-based cyber-physical systems research. The project will have a broad, transformative impact on society, industry, and education, within and beyond the cyber-physical and space systems communities. Software-defined nanosatellite constellations create an industry of cost-effective space-based applications. The project eliminates barriers to space, enabling industry to develop space-based applications. The project creates a new field of research around space-based cyber-physical systems fostering research and education. This project includes ambitious education activities from middle school to post-graduate levels. The project team will mentor middle schoolers and high schoolers from urban public schools through space systems research internships. The project team will involve undergraduates and graduate students in research mentoring and new curricula. The proposed outreach activities will engage society broadly via artist-in-residence programs, research community-building, and public/academic/private partnerships.

This project develops a cyber-physical-social system for communities to incentivize emerging ride-sourcing (such as Uber and Lyft) and sharing services to improve societal outcomes. The goal is to enable novel public-private partnerships that leverage these services for a win-win-win outcome for all parties involved: reducing travel times, energy use and emissions, while ensuring cost-effectiveness for public agencies; boosting mobility service providers' profitability; and improving the experience of all travelers. Research results will be disseminated through courses, open-source tools, journal publications, conferences, workshops, and an online short course. This project will provide interdisciplinary training to a diverse group of students, who will be part of the next generation of globally-engaged leaders. Learning sessions and hands-on activities will be designed and offered to general public under the collaboration with the Carnegie Museum of Natural History. 

This project develops a theoretical, modeling, and computational framework for communities to incentivize emerging mobility services to achieve system-wide goals on efficiency and reliability. This is done through optimally pricing a surcharge or credit to riders' fare with respect to departure times, routes, pooling and curbs (i.e., pick-up/drop-off locations), in conjunction with subsidies to mobility service providers in exchange for guaranteed system improvement. This project advances fundamental knowledge regarding how public right-of-way spaces (such as curbs and roads) and travel demand should be priced and balanced for social optimum. It develops an architecture that integrates travelers' seeking to maximize their utilities and service providers' goals for improving service efficiency and maximizing revenue, with novel optimization and controls of infrastructure and service pricing. In addition, it develops efficient and scalable algorithms to estimate and optimize mixed flows of shared and personal vehicles for large-scale networks. This project will assess multi-source high-resolution data, including vehicle trajectory data from mobility service providers to validate, test and demonstrate this cyber-physical-social system.

Fishes are masters of locomotion in fluids owing to their highly integrated biological sensing, computing and motor systems. They are adept at collecting and exploiting rich information from the surrounding fluids for underwater sensing and locomotion control. Inspired and informed by fish swimming, this research aims to develop a novel bio-inspired cyber-physical system (CPS) that integrates the "physical" robot fish and fluid environment with the "cyber" robot control & machine learning algorithms. Specifically, this CPS system includes i) a pressure sensory skin with distributed sensing capability to collect flow information, ii) control and learning algorithms that compute robot motor signals, output by central pattern generators (CPGs) which receive pressure sensory feedback, iii) a robot fish platform to implement and validate the CPS framework for underwater sensing and control tasks, and iv) experimental and computational methods to investigate and model the underlying fluid physics. This CPS system will have immediate impacts on the core CPS research areas such as design, control, data analytics, autonomy, and real-time systems. It will also significantly impact a wide range of engineering applications which demand distributed sensing, control and adaptive actuation. Examples include human-machine interactions, medical robots, unmanned aerial/underwater vehicles, drug dosing, medical therapeutics, and space deployable structures among others. Leveraging the multidisciplinary nature of this research, this award will support a variety of educational and outreach activities. In particular, a list of activities in broadening participation in engineering will be carried out.

This research project integrates multiple CPS technologies to develop bio-inspired technologies for swarm control of fish. These include innovation in sensing modality via a stretchable, pressure sensitive skin, physics inspired learning and swarm control. The project will first develop a distributed pressure sensitive synthetic skin, which will be installed on robotic fishes to map the pressure distribution on their body and caudal-fin surfaces. The distributed pressure information will then be used in a feedback control policy that modulates CPGs to produce caudal-fin motion patterns of the robotic fishes. The control policy and the caudal-fin motion patterns will be optimized via reinforcement learning first in a surrogate fluid environment and then in the true fluid environment. The surrogate fluid environment will be developed using data-driven non-parametric models informed by physics-based hydrodynamic models of fish swimming, trained using combined experimental and Computational Fluid Dynamics (CFD) simulation data. The above control-learning methods will also be used to achieve efficient schooling in a group of robotic fishes, individually controlled by a CPG, which interacts with each other through surrounding fluids and pressure sensory feedback. The optimized swimming/schooling performance of robotic fishes and the underlying physics will be studied using CFD simulation. Together, this research will advance CPS knowledge on: 1) the design and creation of electronic and sensor materials and devices for robot skin applications; 2) the development of data-efficient, physics-informed learning methods for robotic systems that operate in complex environments, especially leveraging the recent progress on deep learning to exploit the spatial and temporal richness of the pressure data for underwater sensing and robot control; and 3) the flow physics and modeling of fish swimming.

The objective of this Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII) proposal is to develop a cognizant learning framework for cyber-physical systems (CPS) that incorporates risk-sensitive and irrational decision making. The necessity for such a framework is exemplified by two observations. First, CPS such as self-driving cars will share an environment with other CPS and human users. Human drivers demonstrate a heightened sensitivity to changes in speed and can easily adapt to changes in the environment and road conditions, which makes it essential for a CPS to have an ability to recognize non-rational behaviors. Second, large amounts of data generated during their operation and limited access to models of their environments can make a CPS reliant on machine learning algorithms for decision making to meet performance requirements such as reachability and safety. Our research will be grounded on improving behaviors of autonomous vehicles in realistic traffic situations. Outcomes from this effort will contribute to the development of a research paradigm unifying control, learning, and behavioral economics. Students at a Primarily Undergraduate Institution will benefit by being directly involved in all aspects of the research process. Research tasks will involve a team of undergraduate students in a vertically integrated manner where more experienced students will mentor newer team members. 

The proposed effort comprises two thrusts. Thrust 1 will construct utilities to encode CPS performance objectives consistent with practical models of risk-sensitive and irrational decision making. Strategies will be learned by formulating and solving a reinforcement learning problem to maximize this utility. Methods to enable learned strategies to adequately consider delays between evaluation and execution of actions arising from the physical components of the CPS will be developed. Thrust 2 will design algorithms to learn decentralized cognizant strategies when multiple CPS operate in the same environment. To improve reliability in uncertain environments, or when feedback is sparse, techniques to identify contributions of each CPS to a shared utility will be identified. Solution methodologies will be evaluated empirically through extensive experiments and theoretically by determining probabilistic performance guarantees. The PI will develop a research agenda and new undergraduate curriculum in CPS and machine learning at Western Washington University (WWU). Research and educational goals of the project will be integrated through the CARLA simulator for autonomous vehicle research and the F1/10 Autonomous Vehicle platform. The multidisciplinary scope of the project will be emphasized in outreach efforts through Student Outreach Services and STEM Clubs at WWU to encourage and broaden participation from traditionally underrepresented student groups.

This cooperative agreement with MetroLab Network aims to build capacity for the Civic Innovation Challenge (CIC), a research and action competition in the Smart & Connected Communities (S&CC) domain, as well as the broader S&CC research ecosystem. Building off of NSF's S&CC program, the CIC aims to flip the community-university dynamic, asking communities to identify civic priorities ripe for innovation and to partner with researchers to address those priorities. The CIC will help bridge the gap between research and deployment, ensuring that research is conducted in a context that allows for realistic testing and evaluation of impact. Features include engagement of other S&CC-focused funders as partners; shortening the typical timeline of civic research projects; and fostering cohorts organized around specific problem statements to encourage sharing of information across teams. MetroLab will work with Smart Cities Lab to support the CIC through outreach, capacity building, support and programming for finalists and winners, and joint-funder engagement. 

The CIC is an opportunity to transform civic research. It will enable data-driven, research-informed communities, engage residents in the process, and build a more cohesive research-to-innovation pipeline by finding synergies between funders of use-inspired research and funders of civic innovation. It will lay a foundation for a broader and more fluid exchange of research interests and civic priorities that will create new instances of collaboration and introduce new areas of technical and social scientific discovery. The CIC is designed to support transformative projects while fostering a collaborative spirit. 

The specific activities supporting the CIC include "boot camps" for teams of communities and universities to strengthen their partnerships and projects. It will also involve the cultivation of "communities-of-practice" oriented around specific domains, to facilitate knowledge-sharing and cross-site collaboration. The long-term impacts of this work will include deeper collaborations across sectors and regions; improved information sharing and best practices development; and greater impact from research outcomes in cities and communities around the United States.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Over the last three decades we have witnessed historic missions to Mars where unmanned space vehicles successfully landed on and explored the Martian surface in search of evidence of past life. Recently reusable rockets have captured the public's imagination by delivering payloads to orbit and then landing safely back on Earth. A common requirement for these space vehicles is that they must be operated autonomously during the atmospheric entry, descent, and landing (EDL). Furthermore, the first time they are ever tested as a fully integrated system is during the actual mission. This makes EDL extremely challenging and risky. A key technology that has enabled these recent successful space missions is the onboard software that controls the vehicle's motion during EDL, which must work properly under all expected variations in the mission conditions. Motivated by these effective point-design solutions from aerospace engineering, our research aims to develop a unified algorithmic framework for motion planning and control for a large class of Earth-based autonomous vehicles that operate in challenging environments with increasingly complex performance requirements. Applications include autonomous aerial, ground, and underwater vehicles serving many safety critical tasks in, for example, search and rescue, disaster relief, terrain mapping and monitoring, and toxic spill cleanup applications to name few.

Our main hypothesis is that optimization-based motion planning and control provides an effective and unifying mathematical framework that is able to handle the autonomy problems encountered in space applications and this framework can be generalized to a large variety of autonomous vehicles. Our project aims to build this optimization-based framework by leveraging invaluable insights and experiences from NASA's flagship missions to Mars. These missions had to succeed during their first attempt and any failure would have led to catastrophic results, i.e., there was no margin for error. Hence Mars landing can be considered a prototypical benchmark problem, as it encompasses complexities that one would also face with other (Earth-based) autonomous vehicles: switching between a variety of operational modes; limited fuel, power, and mission time; state and control constraints; and uncertainties in the situational awareness, sensing, actuation, vehicle dynamics, and environment. Our project aims to provide algorithmic foundations for optimization-based motion planning and control. It has both a theoretical component to produce fundamental results that can be used to build trustworthy algorithms and a comprehensive experimental component to produce the empirical evidence necessary to evaluate these algorithms on real-world examples, i.e., autonomous quad-rotors and underwater vehicles. Our research team is assembled to build on these lessons learned in space applications and to develop optimization-based planning and control methods that can seamlessly be transitioned to practice.