Abstract: Overall Objective.The goal of this project is to facilitate the timely retrieval of dynamic situational awareness information from field deployed nodes by an operational center in disaster recovery or search and rescue missions, which are typically characterized by resource-constrained uncertain environments. Efficient situational awareness information retrieval under severe resource limitations is critical in applications like disaster response.

Abstract: A smart and connected community (S&CC) will utilize distributed sensors and embedded computing to seamlessly generate meaningful interpretations that would be of greater benefit to individuals, the community, and society, in general, through improved health and safety, efficient public infrastructure, and better access to needed services.

This project is developing theoretical foundations and computational algorithms for synthesizing higher-level supervisory and information-acquisition control logic in cyber-physical systems that expend or replenish their resources while interacting with the environment. On the one hand, qualitative requirements capture the safety requirements that are imposed on the system as it operates. On the other hand, quantitative requirements capture resource constraints in the context of energy-aware systems.

o reduce reliance on other countries for minerals (e.g., coal, rare-earth metals), the USA has seen an invigoration of mining activity in recent years. Unfortunately, miners often have to work in dangerous environments where there is risk of mine explosions, fires, poisonous gases, and flooding in tunnels. Mine accidents have killed over 500 US and 40,000 mine workers worldwide in the past decade.

Overview: Near-real-time water-quality monitoring in rivers, lakes, and water reservoirs of different physical variables is critical to prevent contaminated water from reaching the civilian population and to deploy timely solutions, e.g., to withdraw water from a treatment plant in the case of an emergency (caused by an accident or a terrorist attack) or at least to issue early warnings so to save damage to human and aquatic life. To make optimal decisions and “close the loop” promptly, it is necessary to collect, aggregate, and process real-time water data.

The goal of this proposed research project is to achieve true onboard autonomy in real time for small UAVs in the absence of remote control and external navigation aids. Three major areas have been explored. In the area of UAV flight control, an automatic trajectory generation framework is developed. It consists of waypoint planning at upper level and LQR based trajectory generation in the lower level. The Deep reinforcement learning based framework reduces the control trust by more than 15% with much less computing complexity compared to state-of-the-art approaches.

In pursuit of the long-term goal of enabling interpretable “white-box” autonomy in healthcare, the goal of this project is to investigate physiological modeling, coordinated and resilient multivariable closed-loop control, and regulatory science methodologies for white-box autonomy, as well as to illustrate its application to an important critical care scenario of circulatory resuscitation.  The idea of introducing autonomy to the healthcare domain is not new.  However, prior autonomy capabilities have not been suitably mature for real-world critical care, due to the limitations a

This project aims to create high-fidelity models, validated with real-world data, of mixed-mode travel decisions and emerging mobility markets. A growing subset of travelers make decisions informed by apps that optimize (mixed-mode) routes based on user-defined preferences. Locally optimized solutions tend to cause inefficiencies that are exacerbated by risk-sensitivity (arising from en-dogenous and exogenous uncertainties) in travelers.

Abstract: The surge of cyber-physical systems around us has led to the emergence of novel use-cases like smart surveillance and smart cars. These use-cases either involve high amounts of machine-to-machine communication or are highly interactive, making low-latency response to events critically important. Traditional cloud computing paradigm is unsuitable for such applications due to high network latency to datacenters, leading to the emergence of the edge computing paradigm.

This project is developing tools for traffic management and control using formal methods. By applying techniques such as model-checking and correct-by-construction synthesis, we ensure that traffic flow satisfies high-level objectives expressed using temporal logics that guarantee desirable behavior such as avoiding congestion, maintaining high throughput, ensuring fairness of ramp metering strategies, and reacting to incidents or unexpected conditions.