The Robotic Exploration Lab in The Robotics Institute at Carnegie Mellon University conducts research in control, motion planning, and navigation for robotic systems that explore our planet and our universe.
Building new algorithms for learning dynamics models that are sample efficient and generalizable.
Directly optimizing robust policies for feedback motion planning.
Real-time simulation and trajectory optimization for soft robots.
Developing a fast and robust solver for constrained dynamic games aimed at identifying Nash equilibrium strategies.
The CR3BP is a useful model for designing and analyzing spacecraft trajectories that pass between multiple large bodies. We use optimization techniques to find trajectories that meet mission constraints while being dynamically feasible in the CR3BP.
Extending lifetimes of commercial microelectronic devices in harsh radiation environments without additional shielding or device alterations.
Building new solvers for trajectory optimization problems that are fast, accurate, and numerically robust.
Optimizing long duration spacecraft maneuvers for electric propulsion.
Developing algorithms and hardware for underactuated control of small satellites, mainly through trajectory optimization techniques of magnetorquer attitude manipulation.
Scalable Cooperative Transport of Cable-Suspended Loads with UAVs using Distributed Trajectory Optimization
Developing a fast, low memory footprint algorithm to solve minimum-fuel problems with possible implementation onboard a CubeSat for embedded trajectory optimization.
An open-source, radiation-tested reliable cubesat framework programmable entirely in python.
Making things get where they’re supposed to go when we don’t know exactly how they move and what disturbance forces might be pushing on them.
Tiny low-cost satellites made on printed circuit boards