Note: This post is adapted from my conference paper, that I presented at the Astrodynamics Specialists Conference in Summer 2020. You can read the full paper here.
Abstract – Using Reinforcement Learning to Design Missed Thrust Resilient Trajectories
From ion thrusters to solar sails, spacecraft continue to adopt new and more efficient forms of propulsion. As these low-thrust propulsion methods have become more prevalent, new challenges have arisen. Depending on the mission, low-thrust propulsion elements may need to thrust continuously for days/months. During these thrusting periods, external factors, such as a micro-meteoroid impact or a software glitch, may cause the spacecraft to prematurely cease its thrust stage. Half of all deep space missions enter a safe mode where they cannot thrust every four months. These missed thrust events can result in the complete loss of a spacecraft for time-dependent trajectories like planetary rendezvous. This paper demonstrates how neural networks, trained using reinforcement learning, can autonomously correct for missed thrust events during an interplanetary trajectory.
Key Takeaways
The full paper is online here, but here are, in my opinion, the most interesting aspects
- Reinforcement learning can be used to improve neural network resiliency to missed thrust events
- Global + Relative state information is better than Global or Relative state information alone
- Builds on past Neural Network Optimal Control work
Presentation
Due to COVID-19, this conference was held virtually, and I have uploaded a video of my presentation below.
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Kyra Bryan