Application of Reinforcement Learning: Trajectories Resilient to Missed Thrust Events (MTEs), Year 1

Goal: Develop methods for designing low thrust trajectories that are resilient to trajectory uncertainties and anomalies such as temporary thrust outages. Gap: Mission trajectory design is currently a deterministic process. Methods for dealing with uncertainties are coarse and labor intensive. Approach: Advance the state of the art in low-thrust mission design by developing innovative, artificial intelligence-based (reinforcement learning) techniques for designing trajectories resilient to missed thrust events (MTEs). Develop a prototype tool employing the advanced algorithms. Demonstrate increased resilience, coverage, and reduction in analysis time as compared to current techniques.

Methods can be applied in mission formulation and design phases to reduce risks while maximizing trajectory performance.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.