This project is a collaborative grant involving the University of Michigan (Award number CNS-1646392) and the University of Illinois at Urbana-Champaign (Award number CNS-1646305).

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 this project, we have developed an integrated set of cyber-physical sensing, modeling, and control methods and tools to support large-scale intelligent Wastewater Reuse and Algal Biomass (iWRAB) production.

Cyber-physical Systems (CPS) integrate computing, network communication, and control to facilitate smart-world systems. Nonetheless, the interconnection of sensing and actuating devices in the Internet of Things (IoT) creates new uncertainties and countermeasures must be developed. Deep Learning is an emerging tool with the power to conduct data analysis to address uncertainties.

According to the fire fighter fatalities report issued by the U.S. Fire Administration, ninety-one firefighters including 56 volunteer, 30 career and five wildland agencies died while on duty in the year of 2014. Respecting the fact that the total number of fire incidents across the country is actually decreasing, being a fire fighter becomes more and more dangerous disregarding other significant advances in various areas of science and technology. In the proposed research project, we investigate a new connected and smart infrastructure

Abstract: Cyber-Physical Systems (CPS) promise to change how we study and interact with physical world. CPS use sensors and actuators connected to an autonomic cyberinfrastructure. A key requirement for having dependable CPS is the correct calibration of their sensors. Unfortunately, the current state of the art is to calibrate sensors in laboratories, often manually. Moreover, commodity sensor precision degrades over time.  Consequently, sensors must be periodically recalibrated.