My research interests lie in the intersection of control theory, robotics, and machine learning. More specifically, my Ph.D. research focuses on the provably safe collaboration of multiple robots (quadrotors and mobile robots), and safe learning based control techniques for robotics.

R1. Safe Certificate-Based Maneuvers for Teams of Quadrotors
* Developed safety control certificates to ensure safe aggressive maneuvers of multi-quadrotor and mobile robot swarms
* Implemented safety certificates on teams of quadrotors and robots with Robot Operating System (ROS) (C++, Python).
* Programmed stable hovering and trajectory tracking of quadrotors with Extended Kalman Filter and sensor fusion.

R2. Safe Learning of Quadrotor Dynamics with Barrier Certificates
* Developed online learning method for highly nonlinear quadrotor dynamics using recursive online Gaussian Process.
* Designed safe control certificates for the quadrotor to learn unknown wind field with safety guarantees.
* Implemented Extended Kalman Filter to estimate the states of quadrotors with motion tracking and IMU data.
* Implemented learning algorithms based on LWPR, Gaussian Process, Sparse Spectrum GP, and Eigen GP.

R3. Safe Swarm Robotic and Multi-objective Compositions
* Developed multi-objective composition strategy for provably safe and connected multi-robot collaboration.
* Designed Sum-of-Squares (SOS) programming based optimization algorithm to search for permissive barrier certificates.
* Implemented multi-objective composition algorithms on multiple Khepera III robots and Magellan Pro robots.
* Integrated real-time control and communication between different robots and devices on Robot Operating System (ROS).