Autonomous Spacecraft Decision Making with Reinforcement Learning

As deep space missions continue to grow in complexity, scope, and number, the need for autonomy is paramount. When these satellite are operating millions of miles away from Earth, with minutes of light-time delay, these systems need to react dynamically to their environment while maintaining safety and maximizing science. Reinforcement learning provides a framework for achieving this autonomy. Explicitly, reinforcement learning is a framework by which satellite and spacecraft (agents) are trained to make complex decisions in uncertain environments.

This project specifically focuses on how to train spacecraft to safely and efficiently navigate in small-body environments. Given the increasing focus on exploration of these solar system targets, it is important that spacecraft are able to react dynamically to the chaotic environment of these systems — avoiding collisions and preserving fuel while maximizing science. This project focuses on developing high-fidelity simulations of these environments, and designing control algorithms with deep reinforcement learning to navigate this environment in a manner that acquires as much dynamical insight about the environment in as little time as possible, while maintaining provable safety guarantees.

John Martin
John Martin
Assistant Professor of Aerospace Engineering

Director of the MLDS Lab