This proposal addresses the safety and security issues that arise when giving users remote-access to a multi-robot research test-bed, where mobile robots can coordinate their behaviors in a collaborative manner. Through a public interface, users are able to schedule, and subsequently upload, their own code and run their experiments, while being provided with the scientific data produced through the experiment. Such an open-access framework has the potential to significantly lowering the barriers to entry in robotics research and education, yet is inherently vulnerable from a safety and security point-of-view. This proposal aims at the development and definition of appropriate cyber-physical security notions, formal verification algorithms, and safety-critical, real-time control code for teams of mobile robots that will ultimately make such a system both useful and safe. On top of the research developments, this proposal contains a Transition to Practice component that will allow the system to become a highly usable, shared test-bed; one that can serve as a model for other open, remote-access test-beds. Safety is of central importance to the successful realization of any remote-access test-bed and failure to enforce safety could result in injury in local operators and damaged equipment. To guarantee safe operation, while allowing users to test algorithms remotely, new science is required in the domain of safety-critical control. To address this need, the proposed work follows a three-pronged approach, namely (1) development and use of novel types of barrier certificates in the context of minimally invasive, optimization-based controllers with provable safety properties, (2) formal methods for verification of safety-critical control code for networked cyber-physical systems, and (3) novel methods for protecting against machine-to-machine cyber attacks. By bringing together ideas from multi-agent robotics, safety-critical control, formal verification, and cyber-security, this project will result in a unified and coherent approach to security in networked cyber-physical systems. The potential impact of the resulting open-access multi-robot test-bed is significant along the research, education, and general outreach dimensions in that a future generation of roboticists at institutions across the country will have open and remote access to a world-class research facility, and educators at all levels will be able to run experiments on actual robots.

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.

Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area. The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.