Projects

Enabling Trustworthy Upgrades of Machine-Learning Intensive Cyber-Physical Systems

NSF CAREER Award 2143351

Sole PI: Dr. Weiming Xiang, $498,985, June 2022 - May 2027

This project targets unique machine-learning-intensive CPS upgrade challenges by developing scalable verification and monitoring methods for upgrades as well as safe upgrade procedures to enable trustworthy upgrades and achieve lifetime safety assurance in machine-learning-intensive CPS.

Data-Driven Modeling and Control of Human-Cyber-Physical Systems with Extended-Reality-Assisted Interfaces

NSF CPS Award 2223035

Lead PI: Dr. Weiming Xiang, Co-PI: Dr. Jason Orlosky, $499,000, September 2022 - August 2025

This project will enable the synergistic integration of data-driven modeling and control methods such as neural networks, reinforcement learning, and model-based methods such as hybrid systems, and model predictive control. This project will also explore the benefits of extended reality (XR) in building human-machine interfaces for effective communication and interaction between human users, machines, and environments.

Vision-Based Navigation and Control of UAVs

This project aims to create navigation software for groups of autonomus flight drones using a camera vision based approach where localization of flight drones will primarily be handled by a classification neural network.
Github Repository

Gesture Control of Tello Drones

This project aims to extend an existing gesture drone control project to multiple drones to form a comprehensive machine-learning/neural network solution to swarm control.
Github Repository