Strategic decision-making for physical-world infrastructures is rapidly transitioning toward a pervasively cyber-enabled paradigm, in which human stakeholders and automation leverage the cyber-infrastructure at large (including on-line data sources, cloud computing, and handheld devices). This changing paradigm is leading to tight coupling of the cyber- infrastructure with multiple physical- world infrastructures, including air transportation and electric power systems. These management-coupled cyber- and physical- infrastructures (MCCPIs) are subject to complex threats from natural and sentient adversaries, which can enact complex propagative impacts across networked physical-, cyber-, and human elements. We propose here to develop a modeling framework and tool suite for threat assessment for MCCPIs. The proposed modeling framework for MCCPIs has three aspects: 1) a tractable moment-linear modeling paradigm for the hybrid, stochastic, and multi-layer dynamics of MCCPIs; 2) models for sentient and natural adversaries, that capture their measurement and actuation capabilities in the cyber- and physical- worlds, intelligence, and trust-level; and 3) formal definitions for information security and vulnerability. The attendant tool suite will provide situational awareness of the propagative impacts of threats. Specifically, three functionalities termed Target, Feature, and Defend will be developed, which exploit topological characteristics of an MCCPI to evaluate and mitigate threat impacts. We will then pursue analyses that tie special infrastructure-network features to security/vulnerability. As a central case study, the framework and tools will be used for threat assessment and risk analysis of strategic air traffic management. Three canonical types of threats will be addressed: environmental-to-physical threats, cyber-physical co-threats, and human-in-the-loop threats. This case study will include development and deployment of software decision aids for managing man-made disturbances to the air traffic system.

Legged robots have captured the imagination of society at large, through entertainment and through the dissemination of research findings. Yet, today's reality of what (bipedal) legged robots can do falls short of society's vision. A big part of the reason is that legged robots are viewed as surrogates for humans, able to go wherever humans can as aids or as assistants where it might also be too dangerous or risky. It is in the expectation of robustness and walking facility that today's research hits its limits, especially when the terrain has granular properties. Impeding progress is the lack of a holistic approach to the cyber-physical modeling and control of legged robots. The vision of this work is to unite experts in granular mechanics, optimal control, and learning theory in order to define a methodology for advancing cyber-physical systems (CPS) involving a tight coupling of the physical with the cyber through dynamic interactions that must be learned online. The proposed work will advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain. Achieving reliable, adaptive legged locomotion over terrain with arbitrary granular properties would transform several application domain areas of robotics; e.g., disaster response, agricultural and industrial robotics, and planetary robotics. More broadly, the same tools would apply to related CPS with regards to terrain aware exoskeleton and rehabilitation prosthetics for persons with missing, non-functional, or injured legs, as well as to energy networks with time-varying, nonlinear dynamics models. The CPS platform to be studied is that of a bipedal robot locomoting over granular ground material with uncertain physical properties (sand, gravel, dirt, etc.). The proposed work seeks to overcome current impediments to reliable legged locomotion over uncertain terrain type, which fundamentally relies on the controlled interaction of the robot's feet with the physical environment. The research goal is to improve the perception and control of legged locomotion over granular media for the express purpose of achieving robust, adaptive, terrain-aware locomotion. It revolves around the hypothesis that simple models with decent predictive performance and low computational overhead are sufficient for the optimal control formulations as the compute-constrained adaptive subsystem will both learn and classify the peculiarities of the terrain online. The main research objectives will involve: [1] a validated co-simulation platform for legged robot movement over granular media; [2] terrain-dependent, stable gait generation and gait transition strategies via optimal control; [3] online, compute-constrained learning of granular interactions for adaptation and terrain classification; and [4] validated contributions using experimental testbeds involving variable and unknown (to the robot) granular media. Given the high value of the robotic platforms and the research with regards to outreach and participation, they will be used as outreach tools and to create new educational modules for promotion of STEM fields. Further, the multi-disciplinary nature of the work will be highlighted in order to emphasize its importance.

The objective of this work is to generate new fundamental science that enables the operation of cyber-physical systems through complex environments. Predicting how a system will behave in the future requires more computing power if that system is complex. Navigating through environments with many obstacles could require significant computing time, which may delay the issue of decisions that have to be made by the on-board algorithms. Fortunately, systems do not always need the most accurate model to predict their behavior. This project develops new theory for deciding between the best model to use when making a decision in real time. The approach involves switching between different predictive models of the system, depending on the computational burden of the associated controller, and the accuracy that the predictive model provides. These tools will pave the way for more kinds of aircraft to navigate closely and safely with one another through the National Air Space (NAS), including Unmanned Air Systems (UAS). The results from this project will enable more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach utilizes hybrid control to switch between models whose accuracy is normalized by their computational burden of predictive control methods. This synergistic approach enables computationally-aware cyber-physical systems (CPSs), in which model accuracy can be jointly considered with computational requirements. The project advances the knowledge on modeling, analysis, and design of CPSs that utilize predictive methods for trajectory synthesis under constraints in real-time cyber-physical systems. The results will include methods for the design of algorithms that adapt to the computational limitations of autonomous and semi-autonomous systems while satisfying stringent timing and safety requirements. With these methods come new tools to account for computational capabilities in real-time, and new hybrid feedback algorithms and prediction schemes that exploit computational capabilities to arrive at more accurate predictions within the time constraints. The algorithms will be modeled in terms of hybrid dynamical systems, to guarantee dynamical properties of interest. The problem space will draw from models of UAS in the NAS.

The objective of this work is to generate new fundamental science that enables the operation of cyber-physical systems through complex environments. Predicting how a system will behave in the future requires more computing power if that system is complex. Navigating through environments with many obstacles could require significant computing time, which may delay the issue of decisions that have to be made by the on-board algorithms. Fortunately, systems do not always need the most accurate model to predict their behavior. This project develops new theory for deciding between the best model to use when making a decision in real time. The approach involves switching between different predictive models of the system, depending on the computational burden of the associated controller, and the accuracy that the predictive model provides. These tools will pave the way for more kinds of aircraft to navigate closely and safely with one another through the National Air Space (NAS), including Unmanned Air Systems (UAS). The results from this project will enable more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach utilizes hybrid control to switch between models whose accuracy is normalized by their computational burden of predictive control methods. This synergistic approach enables computationally-aware cyber-physical systems (CPSs), in which model accuracy can be jointly considered with computational requirements. The project advances the knowledge on modeling, analysis, and design of CPSs that utilize predictive methods for trajectory synthesis under constraints in real-time cyber-physical systems. 
 The results will include methods for the design of algorithms that adapt to the computational limitations of autonomous and semi-autonomous systems while satisfying stringent timing and safety requirements. With these methods come new tools to account for computational capabilities in real-time, and new hybrid feedback algorithms and prediction schemes that exploit computational capabilities to arrive at more accurate predictions within the time constraints. The algorithms will be modeled in terms of hybrid dynamical systems, to guarantee dynamical properties of interest. The problem space will draw from models of UAS in the NAS.