Due to their increasing use by civil and federal authorities and vast commercial and amateur applications, Unmanned Aerial Systems (UAS) will be introduced into the National Air Space (NAS); the question is only how this can be done safely. Today, NASA and the FAA are designing a new, (NextGen) automated air traffic control system for all aircraft, manned or unmanned. New algorithms and tools will need to be developed to enable computation of the complex questions inherent in designing such a system while proving adherence to rigorous safety standards. Researchers must develop the tools of formal analysis to be able to address the UAS in the NAS problem, reason about UAS integration during the design phase of NextGen, and tie this design to on-board capabilities to provide runtime System Health Management (SHM), ensuring the safety of people and property on the ground. This proposal takes a holistic view and integrates advances in the state of the art from three intertwined perspectives to address safe integration of unmanned systems into the national airspace: from on-board the vehicle, from the environment (NAS), and from the underlying theory enabling their formal analysis. There has been rapid development of new UAS technologies yet few of them are formally mathematically rigorous to the degree needed for FAA safety-critical system certification. This project bridges that gap, integrating new UAS and air traffic control designs with advances in formal analysis. Within the wealth of promising directions for autonomous UAS capabilities, this project fills a unique need, providing a direct synergy between on-board UAS SHM, the NAS environment in which they must operate, and the theoretical foundations common to both of these. This research will help to build a safer NAS with increased capacity for UAS and create broadly impactful capabilities for SHM on-board UAS. Advancements will require theoretical research into more scalable model checking and debugging of safety properties. Safety properties express the sentiment that "something bad does not happen" during any system execution; they represent the vast majority of the requirements for NextGen designs and all requirements researchers can monitor on-board a UAS for system heath management during runtime. This research will tackle new frontiers in embedding health management capabilities on-board UAS. Collaborations with aerospace system designers at the National Aeronautics and Space Administration and tool designers at the Bruno Kessler Foundation will aid real-life utility and technology transfer. Broader impact will be achieved by involving undergraduate students in the design of an open-source, affordable, all-COTS and 3D-printable UAS, which will facilitate flight testing of this project's research advances. An open-UAS design for academia will be useful both for classroom demonstrations and as a research platform. Further impact will be achieved by using this UAS and the research it enables in interactive teaching experiences for K-12, undergraduate, and graduate students and in mentoring outreach specifically targeted at girls achieving in Science, Technology, Engineering and Mathematics (STEM) subjects.

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production. The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production. The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .

The recent increase in the variety and usage of wearable sensing systems allows for the continuous monitoring of health and wellness of users. The output of these systems enable individuals to make changes to their personal routines in order to minimize exposures to pollutants and maintain healthy levels of exercise. Furthermore, medical practitioners are using these systems to monitor proper activity levels for rehabilitation purposes and to monitor threatening conditions such as heart arrhythmias. However, there is substantial work to be done to facilitate the processing and interpretation of such information in order to maximize impact. This proposal develops a computational framework that models the complex interactions between physiological and environmental factors contributing to an individual's health. The contributions of this award will facilitate the broad adoption of wearable sensing platforms and innovative analytical tools by individuals and medical practitioners. This award develops methodology for the estimation and prediction of physiological responses and environmental factors, with the objective of enabling users to efficiently change their behavior. To accomplish this objective, the framework will build on tools from statistical analysis, topological data analysis, optimization theory and human behavior analysis. This novel framework will not only develop new formal techniques, but it will also serve as a bridge between these cross-disciplinary fields. In particular, the proposed hierarchical computational framework has the potential of providing a trade-off between accuracy and computational flexibility based on the choice of granularity of the representation. This award will: (1) develop methodology for the concurrent representation of physiological, kinematic and environmental states for inference purposes; (2) develop techniques for mapping representations between different systems to enable information sharing; and (3) develop techniques to maximize the impact on the behavior of individuals by building on the proposed data representation. The algorithm development will be informed by integration of limitations on embedded platforms due to memory, computational and power capabilities, and transmission costs when off-board processing is required. The proposed techniques will empower users and medical practitioners to understand, analyze, and make decisions based on patterns in the data. The outcomes of this project will empower medical practitioners by providing innovative and effective tools for wearable sensing systems which enable efficient pattern identification, data representation and visualization. Besides training students directly working on this project, the data sets and algorithms developed will be incorporated into a new graduate course on computational techniques for physiological and environmental sensing. Undergraduate students will be engaged by participating in data collection experiments, REUs, and local demonstrations. Underrepresented undergraduate student communities will be exposed to the research at the national level by presenting demos at well-known diversity conferences in the STEM fields. Furthermore, K-12 local student communities will be engaged via summer workshops that will be prepared for students and educators.

Equipment operation represents one of the most dangerous tasks on a construction sites and accidents related to such operation often result in death and property damage on the construction site and the surrounding area. Such accidents can also cause considerable delays and disruption, and negatively impact the efficiency of operations. This award will conduct research to improve the safety and efficiency of cranes by integrating advances in robotics, computer vision, and construction management. It will create tools for quick and easy planning of crane operations and incorporate them into a safe and efficient system that can monitor a crane's environment and provide control feedback to the crane and the operator. Resulting gains in safety and efficiency wil reduce fatal and non-fatal crane accidents. Partnerships with industry will also ensure that these advances have a positive impact on construction practice, and can be extended broadly to smart infrastructure, intelligent manufacturing, surveillance, traffic monitoring, and other application areas. The research will involve undergraduates and includes outreach to K-12 students. The work is driven by the hypothesis that the monitoring and control of cranes can be performed autonomously using robotics and computer vision algorithms, and that detailed and continuous monitoring and control feedback can lead to improved planning and simulation of equipment operations. It will particularly focus on developing methods for (a) planning construction operations while accounting for safety hazards through simulation; (b) estimating and providing analytics on the state of the equipment; (c) monitoring equipment surrounding the crane operating environment, including detection of safety hazards, and proximity analysis to dynamic resources including materials, equipment, and workers; (d) controlling crane stability in real-time; and (e) providing feedback to the user and equipment operators in a "transparent cockpit" using visual and haptic cues. It will address the underlying research challenges by improving the efficiency and reliability of planning through failure effects analysis and creating methods for contact state estimation and equilibrium analysis; improving monitoring through model-driven and real-time 3D reconstruction techniques, context-driven object recognition, and forecasting motion trajectories of objects; enhancing reliability of control through dynamic crane models, measures of instability, and algorithms for finding optimal controls; and, finally, improving efficiency of feedback loops through methods for providing visual and haptic cues.