The objective of this work is to generate new fundamental science for computer controlled complex physical systems, a broad class of cyber-physical systems (CPS) and demonstrate this science in aerial vehicles and walking robots. The new science enables autonomous planning and control in the presence of failures and abrupt changes in system variables. A framework for the design of algorithms that exploit awareness of the physical and design constraints to autonomously self-adapt their motion plan and control actions will be generated. The approach exploits elements from geometry, adaptive control, and hybrid control to advance the knowledge on modeling, planning, and design of CPS with constraints, non-smooth, and intertwined continuous and discrete dynamics. Unlike current approaches, which separate the task associated with planning the motion from the design of the algorithm used for control, the algorithms to emerge from this project self-learn and self-adapt in real time to cope with unexpected changes in motion and specification constraints so as to enable autonomous systems to perform robustly and safely, and degrade gracefully under failure conditions. Specifically, the new algorithms will learn and monitor the physical and design constraints in real time and adapt both planner and controller by selecting the appropriate constraints to enforce, with robustness and safety guarantees. The capabilities of the new tools will be demonstrated on multi-legged robots in harsh environments that make them prone to failures, and on aerial vehicles in contested/adversarial environments.

While substantial progress has been made in the control of electric grid considering the cyber and physical characteristics, there has been a gap in the integration of smart grid research as it integrates with human behavior -- especially in interactions with energy management systems. For example residential energy consumption has been rapidly increasing during the last decades, especially in the U.S. where 2.6 trillion kilowatt-hours were consumed during 2015, and an additional 13.5% increase is expected by 2040 . Research efforts such as demand response have been made to reduce this consumption especially in smart residential environments. Concepts such as demand response have largely overlooked the complexity of human behaviors and perceptions, and recent research in the social-science domain and recent experience has challenged the effectiveness of this approach and in some instances led to an abandonment and avoidance of such concepts. The objective of this proposal is to overcome the limitations associated with state-of-the-art energy management systems by designing novel algorithms, machine learning models, and optimization techniques that specifically consider user behaviors, perceptions, and psychological processes. This revolutionary approach will unleash the full potential of smart residential environments in reducing residential energy consumption and has the potential to transform the way in which energy management systems are designed, implemented, and used by people. This project also supports innovative educational activities such as classes, real time demonstrations, coding challenges, and research experiences for high school students. The PI will also lead a cohort of students to the diversity-oriented Grace Hopper conference and teach seminars for Hispanic elementary students. Finally, a new class on Cyber-Physical-Human System will be designed and several graduate and undergraduate students will participate in the research activities.

Collaborative robots have been widely employed to assist humans in an increasing number of areas. Just as human-human collaboration, the trust in human-robot teams has a property of bidirectional. However, few studies have been conducted on both human-trusting-robot issue and robot-trusting-human issue in a unified framework for human-robot collaboration. The project addresses this challenge by developing a new systematic Bi-Trust framework to integrate humans? trust in robots and robots? trust in humans into the human-robot collaboration process. With the established Bi-Trust framework, a trust-level-based computational collaboration model is created to optimize and plan robot actions. The proposed approaches will reduce uncertain failures and improve the collaboration-quality of human-robot shared tasks.

The goal of this proposal is to establish a framework for cascading failure abstraction and vulnerability analysis in Cyber-Physical Systems (CPSs), empowered by data. CPSs are critical to modern society, however, they are vulnerable to attacks and failures. The failures in CPSs are more destructive because of cascading failure, which means that the failure of a part of the system can cause failure in the rest of the system and result in more severe damage. However, analysis of CPS vulnerability involving cascading failure is extremely challenging, mainly because 1) it?s hard to theoretically analyze the various physical processes happen in a cascade and 2) local diffusion models applied to the CPS network cannot capture the global impact of cascades. Using simpler cascade models derived from data as media, it is possible to have a deeper understanding of how CPSs are vulnerable to cascading failure. CPSs are gaining popularity and there is an urgent need to enhance its security, hence the proposed work will greatly benefit the society and of national interest. The project will provide opportunities for undergraduate students, underrepresented minority groups and women to research in some of the society's most concerned fields like machine learning and security. Also, the outcomes of this work will be introduced in courses for undergraduate and graduate students and integrated into STEM outreach programs for K-12 students.

The increasing use of Clinical Artificial Intelligence/Machine Learning (AI/ML)-enabled Software as a Medical Device (SaMD) for healthcare applications, including medical imaging, is posing significant challenges for regulatory bodies in ensuring that these devices are valid, robust, transparent, explainable, fair, safe, and accurate. One of the major challenges is the phenomenon of data shift, which refers to a mismatch between the distribution of the data that was used for model training/testing and the distribution of the data to which the model was applied. This makes it difficult to generalize AI/ML-enabled SaMD across different healthcare institutions, different medical devices, and disease patterns, resulting in AI model performance deterioration, erroneous outputs, and adverse patient outcomes.

Despite a long-standing effort on the automation in the care of critically ill patients, prior automation capabilities have not been suitably mature for real-world use due to a few limitations: (1) the decisions/actions of the automation could not be easily interpreted by clinicians, preventing clinicians' effective interaction with and supervision of the automation for safe patient care; (2) the automation was designed to perform a particular task of interest without accounting for the overall physiological state of the patient; (3) multiple automation functions were not often coordinated to avoid possible conflicts in patient treatment; (4) automation was prone to errors in the medical devices; and (5) regulatory science for evaluation and approval of safety-critical automation capabilities was lacking. This research program seeks to address these fundamental challenges by studying novel methodologies for (1) mathematically representing the patient physiology in a way to facilitate the interpretation of clinicians and (2) designing automation capabilities that can facilitate clinician interaction and supervision, coordination of multiple treatment goals and functions, and resilience to device errors and faults. In addition, this research program will benefit society and Science, Technology, Engineering and Mathematics (STEM) education by creating a wide range of automation systems that can improve the quality of care of critically ill patients, expediting the deployment of new medical devices with advanced automation capabilities, facilitating the evaluation and approval of emerging healthcare automation capabilities, and training STEM workforce especially from underrepresented minorities.

There is a growing trend toward human-cyber-physical systems (h-CPS), where systems collaborate or interact with human operators to harness complementary strengths of humans and autonomy. Examples include self-driving cars that need the occasional driver intervention, and industrial robots that work beside or cooperatively with people. The societal impact of h-CPS, however, is contingent on ensuring safety and reliability. Several high-profile incidents have shown that unsafe h-CPS can lead to catastrophic outcomes. Formal methods enable the model-based design of safety-critical systems with mathematically rigorous guarantees. However, the research area of formal methods for h-CPS is still in its infancy. The goal of this research is to expand formal methods toward the joint modeling and formal analysis of CPS and human-autonomy interactions, accounting for the uncertainty and variability of human behaviors, intentions, and preferences.

This Cyber Physical Systems (CPS) project will develop novel sensing, actuation, and embedded computing technologies that allow civil infrastructures to be responsive, resilient and adaptive in the face of dynamic loads. Such technologies require delivery of electrical power, typically either via an external power grid, or through the use of battery storage. However, grid power may be unreliable during extreme loading events, and batteries must be periodically recharged or replaced. The novelty of the technologies developed in this project is that they power themselves, by storing and reusing energy injected into the infrastructure by external loads. The project focuses on three applications: (1) urban stormwater networks that actively control water levels to prevent flooding, using power generated from the hydrologic flows, (2) buildings that actively control their deformations during earthquakes and high winds, using power generated from vibrations, and (3) ocean desalination systems that actively control pumping rates, using power generated from waves. The project contains an experimental campaign for each application. It also contains an analytical component, focused on the development of control algorithms to maximize the performance of the technologies. Educational outreach activities include class modules and research experiences for undergraduate and graduate students, as well as a workshop for high school students. Control algorithms for self-powered infrastructures must explicitly optimize the balance between power generation and performance objectives. This project will innovate new Model Predictive Control algorithms for self-powered infrastructure technologies, such that they achieve the best performance possible while not running out of energy. These algorithms will be validated experimentally, for all three applications. There is presently no existing theory for optimal control of self-powered systems that is scalable to large and complex systems such as the ones under consideration. The research to be conducted here will augment recent advances in Model Predictive Control theory, to result in a new body of knowledge in this area. Challenges include: (1) innovation of optimization algorithms that can contend with the inherent nonconvexity of optimal self-powered control problems; (2) development of effective techniques for handling the stochastic nature of the dynamics for the target applications; (3) synthesis of controllers that are computationally tractable, but which also optimally compensate for the complex transmission losses and constraints in the power trains; (4) the derivation of systematic techniques for ensuring the robustness of the controllers, to uncertainties in the system model and disturbances.

Connected Automated Vehicle (CAV) applications are expected to transform the transportation landscape and address some of the pressing safety and efficiency issues. While advances in communication and computing technologies enable the concept of CAVs, the coupling of application, control and communication components of such systems and interference from human actors, pose significant challenges to designing systems that are safe and reliable beyond prototype environments. Realizing CAV applications, in particular in situations where humans may partly remain in the loop, requires addressing uncertainties that arise from human input. Large scale deployment of CAVs will also require addressing challenges in coordination of actions among CAVs and with human operated systems. To address these challenges, this project develops a novel model-based stochastic hybrid systems (SHS)-theoretic approach that relies on describing and communicating behaviors of actors in the system in the form of evolving SHS using Bayesian learning. The models are then utilized in a stochastic model predictive control (SMPC) framework for optimal coordination of actions. The proposed research will provide wide-ranging societal benefits through three major impact areas: first, by advancing research in stochastic communication-aware control design for hybrid systems; second, through the development of new models and advanced controllers to address the emerging challenges of coordinating mixed systems of automated and manned vehicles, hence opening new vistas in other areas involving general multi-agent systems; and third, through educational and outreach activities that are natural extensions of this multidisciplinary research. This project is also the first fruits of a recent National Science Foundation/Deutsche Forschungs Gesellschaft (NSF/DFG) collaboration on cyber-physical systems (CPS). Through this collaboration, NSF funds the US component (University of Central Florida and University of Georgia) while the German partners (University of Technology and University of Koblenz-Landau) are funded by DFG. The overarching goal of this collaborative research is to introduce SHS-based modeling and control concepts to allow the design of highly efficient CAV systems capable of large scale coordination (mass platooning). Such designs are currently challenging due to the uncertainties that stem from human input and communication of actors. The key objectives of the project are to: (1) develop methods for capturing the human, sensors and communication induced uncertainties of mixed automated and manned systems in a stochastic hybrid system form (perception maps) and communicating them in a control-aware fashion, (2) employ the models in an SMPC framework to produce multi-modal decisions and lower level longitudinal motion control in a single unified framework, and (3) validate the analytical outcomes through both extensive data-driven co-simulation using industry utilized models, and a fleet of realistic small CAVs and a full scale prototype CAV.

Increasing wildfire costs---a reflection of climate variability and development within wildlands---drive calls for new national capabilities to manage wildfires. The great potential of unmanned aerial systems (UAS) has not yet been fully utilized in this domain due to the lack of holistic, resilient, flexible, and cost-effective monitoring protocols. This project will develop UAS-based fire management strategies to use autonomous unmanned aerial vehicles (UAVs) in an optimal, efficient, and safe way to assist the first responders during the fire detection, management, and evacuation stages. The project is a collaborative effort between Northern Arizona University (NAU), Georgia Institute of Technology (GaTech), Desert Research Institute (DRI), and the National Center for Atmospheric Research (NCAR). The team has established ongoing collaborations with the U.S. Forest Service (USFS) in Pacific Northwest Research Station, Kaibab National Forest (NF), and Arizona Department of Forestry and Fire Management to perform multiple field tests during the prescribed and managed fires. This proposal's objective is to develop an integrated framework satisfying unmet wildland fire management needs, with key advances in scientific and engineering methods by using a network of low-cost and small autonomous UAVs along with ground vehicles during different stages of fire management operations including: (i) early detection in remote and forest areas using autonomous UAVs; (ii) fast active geo-mapping of the fire heat map on flying drones; (iii) real-time video streaming of the fire spread; and (iv) finding optimal evacuation paths using autonomous UAVs to guide the ground vehicles and firefighters for fast and safe evacuation. This project will advance the frontier of disaster management by developing: (i) an innovative drone-based forest fire detection and monitoring technology for rapid intervention in hard-to-access areas with minimal human intervention to protect firefighter lives; (ii) multi-level fire modeling to offer strategic, event-scale, and new on-board, low-computation tactics using fast fire mapping from UAVs; and (iii) a bounded reasoning-based planning mechanism where the UAVs identify the fastest and safest evacuation roads for firefighters and fire-trucks in highly dynamic and uncertain dangerous zones. The developed technologies will be translational to a broad range of applications such as disaster (flooding, fire, mud slides, terrorism) management, where quick search, surveillance, and responses are required with limited human interventions. This project will also contribute to future engineering curricula and pursue a substantial integration of research and education while also engaging female and underrepresented minority students, developing hands-on research experiments for K-12 students. This project is in response to the NSF Cyber-Physical Systems 20-563 solicitation.