This project will provide funding for students from U.S. institutions of higher learning to attend the 2023 Cyber-Physical Systems and Internet-of-Things Week (CPS-IoT Week 2023) in San Antonio, Texas during May 9-12, 2023. This is the first in-person CPS IoTT week since the pandemic in 2020. CPS-IoT Week (previously known as CPSWeek) is the premier conference for Cyber-Physical Systems research, and is comprised of five flagship conferences in CPS, namely ICCPS (International Conference on Cyber-Physical Systems), RTAS (Real-Time and Embedded Technology and Applications Symposium), IPSN (Information Processing in Sensor Networks), HSCC (Hybrid Systems: Computation and Control) and IoTDI (Internet of Things Design and Implementation). In addition to these 5 conferences, there are multiple workshops and demonstrations that permit papers to be submitted and presented by graduate students and postdoctoral researchers. 

Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability that will expand the horizons of these critical systems. CPS technologies are transforming the way people interact with engineered systems, just as the Internet has transformed the way people interact with information. Travel support to this conference for today's graduate students and postdoctoral researchers enables broad participation by tomorrow's researchers in this interdisciplinary field.

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 inthanovations in a pressure sensitive skin 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.

Propelled by the growth in demand for artificial intelligence-enabled applications, the past decade has witnessed the emergence of Collaborative Cyber-Physical Learning Systems (CCPLS). CCPLS carry out distributed, learning-based processing tasks through coordination among Cyber-Physical System (CPS) devices, and are envisioned to provide critical functionality across the commercial and defense sectors in the next several years. However, the data generated by CCPLS is often large-scale, high-dimensional, heterogeneous, and time-varying, which poses critical challenges for intelligence modeling. Concurrently, unmanned vehicles, in particular Unmanned Aerial Vehicles (UAVs), have shown promise of scaling up information-sharing in CCPLS, especially in under-served regions such as rural areas. The project's novelties are in establishing a concrete foundation for UAV-CCPLS integration that unifies the associated learning, networking, and communication design aspects around appropriate intelligence metrics. The project's impacts are the development of UAV-assisted CCPLS for smart agriculture tasks, as well as advancing the manufacturing of UAVs and other unmanned vehicles tailored for CCPLS. Project outcomes will be disseminated by releasing open-source software and research videos and tutorials. The investigators will further engage in Curriculum development, diversity, and outreach activities including mentoring undergraduate researchers. 

Research investigations center around three interconnected thrusts. Thrust 1 develops a novel UAV-assisted intelligence framework for CCPLS and introduces a precise, task-oriented notion of data dynamics and heterogeneity. Additionally, this thrust develops a `learning for learning? framework that aims to predictively estimate the rate of data dynamics. Thrust 2 investigates methodologies for jointly optimizing resource utilization and intelligence quality through co-design of UAV trajectories and UAV-to-CPS network establishment. The data dynamics framework from Thrust 1 is integrated into this design through an online, network-aware sequential decision-making framework. Finally, Thrust 3 develops CCPLS communication protocols based on learning-aware uplink and downlink wireless beamformers and over-the-air aggregation methods. These protocols are tailored to the specific needs of the UAV-assisted learning systems, e.g., the transmission of noisy information over UAV-to-UAV and UAV-to-access point communication links.

Many cyber-physical systems (CPS) have real-time (RT) requirements. For these RT-CPS, such as a network of unmanned aerial vehicles that deliver packages to customers? homes or a robot that performs/aides in cardiac surgery, deadline misses may result in economic losses or even fatal consequences. At the same time, as these RT-CPS interact with, and are depended on by, humans, they must also be trustworthy. The goal of this research is to design secure RT-CPS that are less complex, easier to analyze, and reliable for critical application domains such as defense, medicine, transportation, manufacturing, and agriculture, to name just a few. Since RT-CPS now permeate most aspects of our daily lives, especially in the smart city and internet-of-things (IoT) context, this research will improve confidence in automated systems by users. Research results will be disseminated to both academia and industry, and permit timely adoption since the hardware required in this research is already publicly available. This project will result in a pipeline of engineers and computer scientists who are well-versed in the interdisciplinary nature of securing RT-CPS, as well as course modules and red-teaming exercises for undergraduate students in all engineering disciplines and interactive learning modules and internship experience for K-12 students in D.C., Detroit, Dallas, and St. Louis.

The goal of this research is to design secure RT-CPS from the ground up while explicitly accounting for physical dynamics of said RT-CPS at runtime to achieve resilience via prevention and detection of, and recovery from, attacks. This will be accomplished by (i) securing the scheduling infrastructure from the ground up, (ii) using a formal framework for trading off security against timeliness while accounting for system dynamics, and for the cost of security to be explicitly quantified, and (iii) performing state- and function-dependent on-demand recovery. Said RT-CPS will be able to proactively prevent attacks using moving target defenses, as well as detect and recover from attacks that cannot be avoided. This research will pave the way for RT-CPS and internet-of-things (IoT) to be implemented with confidence: their timely and correct operation guaranteed. Specific contributions of this research are: (i) a trusted scheduling infrastructure that can protect the integrity of the real-time tasks, the scheduler, its task queues, and I/O, and which can recover from (intentional) errors, (ii) a probabilistic real-time/security co-design framework that exploits trusted execution to protect the security of the real-time tasks, (iii) novel schedulability analysis techniques, (iv) an incremental recovery mechanism for continuous operation, and (v) validation on automated ground vehicles, drones, and robot arms. Contributions expanding the knowledge base will be made to the fields of CPS, IoT, real-time systems, security, and control systems.

Systematic modeling, analysis and design of social, economic and technological systems requires addressing myriad tradeoffs that natural systems and human groups handle so effectively. A confluence of ideas from diverse disciplines, including arts, ecology, neuroscience, and control theory will further enhance our understanding of the underpinnings of such remarkable systems and enable us to facilitate such robustness and resilience in engineered systems. Examples of such systems range from electric networks to connected and autonomous vehicles to smart manufacturing to mixed human-automata systems. Broader issues that require such confluence include climate change, energy efficiency, and human-wildlife conflicts. To begin a dialogue aimed at improving synergistic approaches to these challenges, the proposed workshop on Convergence with Control: Bridging the Arts, Ecology, Neuroscience, and Engineering aims to bring a diverse scientific community together and foster fruitful cross-disciplinary collaborations. It will feature diverse and distinguished domestic and international speakers, including early career researchers. The participants will be from cross-cutting disciplines, including the arts, ecology, neuroscience, and engineering. In addition to the talks, the workshop will feature several activities with cross-disciplinary themes that will facilitate team-forming and facilitate deep scientific discussions. The workshop will consist of presentations by invited speakers interleaved with cross-disciplinary activities.

The purpose of the workshop is to facilitate and enhance research discussions and collaborations among experts in a broad range of disciplines, including biology, arts, and engineering. The invited speakers are leading experts in the areas of ecology, neuroscience, music and dance, dynamical systems, and control theory. The sub-themes, (i) collective behavior and control, (ii) robotics, learning and control, and (iii) neuroscience and control, will ensure that the broad agenda of the workshop is focused both on theory and application. The invited speakers have been selected to ensure a broad representation of research disciplines and such exposure will also benefit young researchers immensely.
 

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.
 

The safety and performance of cyber-physical systems (CPS) depend crucially on control and scheduling decisions that often are fixed at design time, which significantly restricts the conditions under which a system can operate both safely and with suitable performance. Going beyond prior work that has explored different control and scheduling adaptations in individual system designs, this project will conduct more general and in-depth investigations, into how cyber-physical systems? control and scheduling can be co-designed to adapt jointly, automatically, dynamically, safely, and effectively even in response to rapid, large, and diverse changes in: (1) the system?s controlled behavior; (2) its environment; (3) its physical components; and (4) its platform software and hardware. Our project will immerse multiple graduate students in cross-disciplinary research, with extensive education, training, and mentoring spanning computer science, control theory, natural hazards engineering, structural engineering, mechanical engineering, and computer engineering. We will also involve undergraduate students via summer REU supplements and in-semester mentored independent study projects for academic credit, and will leverage our existing initiatives and relationships with partner organizations for K-12 outreach. As we have done in each of our previous collaborations, our multi-university team will recruit, mentor, and retain participants from groups traditionally under-represented in science and technology fields, leveraging effective and established outreach programs at our institutions.

In this cross-disciplinary research project we will develop new formal models, analyses, system infrastructure, and evaluation metrics, to explicitly represent, respect, and even exploit control and scheduling inter-dependencies, to ensure that systems? behaviors remain safe while enabling significant improvements in performance. The novel co-design approach we propose will enable radically improved cyber-physical system performance capabilities while respecting safety constraints that may cross-cut cyber and physical components and the system?s environment. For example, it will enable more extreme (but safely realizable) stress testing and adaptive management of mechanical systems and civil structures, to gauge and maintain resilience to significant (potentially adverse) changes to conditions in a system and its environment, and to enact adaptive mitigating responses accordingly.

Aging civil infrastructure is a critical worldwide problem that affects daily life, making it important to innovate more efficient and economical repair and construction methods for civil structures. Additive manufacturing, or 3D printing, offers a promising way to fulfill this compelling need. However, almost all current additive manufacturing methods rely on gantry-based systems that can only build structures within rigid frames, thereby restricting printing speed and scale, thus hindering their use in maintenance and construction. This award supports fundamental research to establish collective additive manufacturing, a novel robotics-based approach for large-scale 3D printing. Collective additive manufacturing uses a team of autonomous mobile robots to jointly print large-scale 3D structures. The results of the research will have a potentially wide range of applications in civil infrastructure maintenance and construction, to post-disaster response and extraterrestrial construction. The project is based on a convergent research approach involving robotics, artificial intelligence, control theory, and dynamical systems, which culminates in formal and informal learning activities to broaden participation of underrepresented groups in engineering.

Collective additive manufacturing envisions the use of teams of mobile robots to overcome key limitations of existing gantry-based additive manufacturing, including its small scale and slow printing speed. To unleash the full potential of collective additive manufacturing, several scientific boundaries must be pushed, ensuring optimal deployment of multiple mobile robots that print large structures according to an engineered, virtual design. This research will fill critical knowledge gaps in robotic localization, control, and coordination, to realize a robotic team that intentionally and actively modifies its surroundings to successfully complete its printing task. This interdisciplinary research program will unfold along three thrusts: artificial intelligence for planning and localization, model predictive control to adapt to printing disturbances and substrate variations, and distributed control to elicit stable collective dynamics. Theoretical advancements will proceed alongside with experimental research toward demonstrating the potential of collective additive manufacturing to accurately and efficiently print large structures in real-world settings.

Loss of walking function and leg sensation are devastating consequences of spinal cord injury (SCI). These deficits have a profoundly negative impact on independence and quality of life of those affected. Moreover, wheelchair reliance after SCI increases the risk of medical complications. The healthcare costs associated with SCI are ~$50 billion/year, presenting a significant public health concern. Currently, there are no biomedical solutions capable of restoring walking and leg sensation after SCI. Clinically practical and socially acceptable solutions to these important problems are desperately needed. Employing a cyber-physical system (CPS) to bypass the damaged spinal cord may be a novel way to restore walking and leg sensation to those with leg paralysis due to SCI. The proposed multi-disciplinary effort will inspire students from traditionally underprivileged and underrepresented groups to pursue college education in STEM fields by demonstrating how engineering and science can make a difference in the well-being of those with disabilities. In addition, it will engage individuals with disabilities, their family members, friends, and caregivers, in educational opportunities in order to increase their scientific and technical literacy. The outreach to these communities will be accomplished by leveraging diverse ethnic makeup of Orange and Los Angeles Counties, geographic proximity of the three study sites, which makes outreach activities amenable to integration, and the high societal significance and visibility of the project. 


Impairment or complete loss of gait function and lower extremity sensation are common after spinal cord injury (SCI). A new cyberphysical system, CPS, can be realized as a permanently implantable bi-directional (BD) brain-computer interface (BCI), which translates walking intentions from brain signals into commands for a leg prosthesis, and converts prosthesis sensor signals into electrical stimulation of the brain for artificial leg sensation. This closed-loop operation would come close to restoring able-body-like walking and leg sensation after SCI. Before such an implantable CPS is deployed in humans, its feasibility and safety must be established. The main objective of this Frontier project is to design, develop, and test a wearable analogue of a fully implantable electrocorticogram (ECoG)-based BD-BCI for walking and leg sensation. The BD-BCI CPS will be designed as an ultra-low power modular system with revolutionary techniques for interference mitigation to enable simultaneous electrical stimulation and recording. The first module will consist of a custom brain signal acquisition system that exploits ECoG signal attributes to significantly reduce power consumption. The second module will consist of a low-power processing unit, brain stimulator, and wireless communication transceiver. This module will internally execute optimized BCI algorithms and wirelessly transmit commands to a robotic gait exoskeleton for walking. Comprehensive benchtop and bedside tests will be conducted to assess proper system function. Finally, subjects with paraplegia due to SCI will be recruited to undergo a 30-day ECoG implantation to test the BD-BCI's ability to restore brain-controlled walking and leg sensation. The goals of transition to practice (TTP) are to: (1) develop a fully implantable version of the BD-BCI, (2) perform a series of industrial-standard medical device benchtop tests, and (3) test the implants safety.

The adoption of cyber-physical systems (CPS) is growing at an accelerated rate, giving rise to large-scale CPS??for example, comprised of large numbers of robots in a warehouse, turbines on a wind farm, or vehicles and traffic signals in a city. If intelligently coordinated, these systems will unlock transformative societal benefits across broad economic sectors. They also promise to contribute to the most pressing challenge of the century??climate change??by substantially utilizing resources more effectively, such as from supply chains, energy systems, and urban systems. Unfortunately, effective coordination schemes have been elusive due to the sheer scale and diversity of scenarios that these systems encounter. To advance robust coordination in large-scale CPS, this project investigates the generalization of learning-enabled methods as a key solution concept, in light of their potential to translate coordination schemes across disparate scenarios. The project's impact will be enhanced through the dissemination of open-source research and teaching material, and via experiments derived from large-scale CPS applications in collaboration with public sector, industry, and academic partners. The project also boosts K-12, undergraduate, graduate, and professional education, by supporting and actively engaging students in research activities, promoting the translation of research to practice, and through outreach efforts targeting middle school students from underrepresented and underserved communities.

This NSF CAREER project focuses on an enabling methodology for large-scale CPS: understanding generalization of learning-enabled methods, and further applying it to reduce the complexity of system design and analysis. Recent evidence shows that controllers trained using machine learning sometimes have the remarkable ability to generalize to other scenarios, such as to different problem sizes or between simulated and physical robotic systems. However, generalization is currently more of an art than a science; the conditions under which generalization is successful are not well understood. At the same time, large-scale CPS often induce parameterized families of scenarios; for example, traffic control must consider different weather conditions, sensing modalities, and numbers and types of agents. This family of related CPS scenarios thus provides a platform for carefully examining generalization across scenarios. The project will: 1) advance learning algorithms for large-scale CPS by designing coordination-aware model-based reinforcement learning methods for multi-agent systems; 2) leverage the algorithms to understand generalization by formalizing, measuring, and characterizing generalization with respect to deviations in CPS scenarios; and then 3) harness generalization for robust coordination, by efficiently solving large families of scenarios necessary to provide high performance and assurances.