My research is at the intersection of control theory, networks, aerospace systems, robotics, optimization, and data science, with a multi-disciplineary twist to such areas as aerospace autonomy and dynamic networks.
The research contributions have been reflected in:
RAIN Lab is the main engine behind a lot of this research. The topics we consider in the group include:
- Dynamic Networks: dynamics, coordination, control of networks, aerospace networks, robotic networks, distributed space systems, distributed autonomy, distributed estimation, energy and transportation networks, network control and dynamics, state-dependent networks, random networks, fundamental theory, algorithms, applications
- Autonomy and semi-autonomy: real time motion planning, computational guidance and control, spacecraft autonomy, convex optimization, convexification for autonomy, parameterizations for autonomy (quaternions, dual quaternions, clifford algebras), autonomy in uncertain environments, energy-aware aerospace systems, fundamental theory, algorithms, implementation, and novel platforms
- Data to Decisions: data science for feedback control and autonomy, fundamental guarantees, dimension reduction, data to decisions, system identification, applications.
Controlling Robotic Swarms (Brian Douglas):
How to Land on a Planet (Brian Douglas):
Flying Smart with Autonomous Vehicles (UW TV):
If you are a perspective student interested in exploring joining the RAIN Lab, I highly recommend looking at some of the work that has been done by the group and see if there is synergy between your interest and the lab research. Generally, the students who come to our group enjoy math, coding, tinkering with hardware, and in addition to having a passion for aerospace systems and robotics, but also multidisciplinary applications of control and optimization.