The Robotic Exploration Lab in the Department of Aeronautics and Astronautics at Stanford University conducts research in navigation, control, and motion planning for robotic systems that explore our planet and our universe.

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Research

Dynamic Games Solver

Dynamic Games Solver

Developing a fast and robust solver for constrained dynamic games aimed at identifying Nash equilibrium strategies.

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Trajectory Optimization in the Circular Restricted Three-Body Problem (CR3BP)

Trajectory Optimization in the Circular Restricted Three-Body Problem (CR3BP)

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.

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Intelligent Radiation Protection of Commercial Components in Space

Intelligent Radiation Protection of Commercial Components in Space

Extending lifetimes of commercial microelectronic devices in harsh radiation environments without additional shielding or device alterations.

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Fast Trajectory Optimization

Fast Trajectory Optimization

Building new solvers for trajectory optimization problems that are fast, accurate, and numerically robust.

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Design and Control of Agile Quadrupeds

Design and Control of Agile Quadrupeds

Designing a new, lost-cost quadrupeds with state-of-the-art control.

Low-Thrust Trajectory Optimization

Low-Thrust Trajectory Optimization

Optimizing long duration spacecraft maneuvers for electric propulsion.

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Underactuated Attitude Control of Small Satellites

Underactuated Attitude Control of Small Satellites

Developing algorithms and hardware for underactuated control of small satellites, mainly through trajectory optimization techniques of magnetorquer attitude manipulation.

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Distributed Trajectory Optimization

Distributed Trajectory Optimization

Scalable Cooperative Transport of Cable-Suspended Loads with UAVs using Distributed Trajectory Optimization

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Fast Solution of Optimal Control Problems With L1 Cost

Fast Solution of Optimal Control Problems With L1 Cost

Developing a fast, low memory footprint algorithm to solve minimum-fuel problems with possible implementation onboard a CubeSat for embedded trajectory optimization.

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PyCubed

PyCubed

An open-source, radiation-tested reliable cubesat framework programmable entirely in python.

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Control and Motion Planning with Contact Interactions

Control and Motion Planning with Contact Interactions

Controlling systems that make and break contact with objects and the environment. Applications to robotic locomotion and manipulation.

Robust Motion Planning

Robust Motion Planning

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.

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KickSat Project

Tiny low-cost satellites made on printed circuit boards

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People

Faculty

Zac Manchester
Assistant Professor

PhD Students

Taylor Howell
Optimization through contact
Brian Jackson
Real-time motion planning
Max Holliday
Space-resilient hardware
Simon Le Cleac'h
Optimizing game-theoretic interactions
Andrew Gatherer
Improving small-satellites
Kevin Tracy
Spacecraft dynamics

Masters Students

Jan Bruedigam
Modernizing physics engines
Laura Lee
Master's Student
Remy Derollez
Robust motion planning
Jared Blanchard
Optimizing Spacecraft Trajectories

Undergraduate Students

Nathan Kau
Undergraduate Student
Tarun Punnoose
Undergraduate Student
Aaron Schultz
Undergraduate Student