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Application of Reinforcement Learning: Trajectories Resilient to Missed Thrust Events (MTEs), Year 2

Completed TRL 4 (started at 3, targeting 4)

Description

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.

Benefits

Develop methods for designing low thrust trajectories that are resilient to trajectory uncertainties and anomalies such as temporary thrust outages. Mission trajectory design is currently a deterministic process. Methods for dealing with uncertainties are coarse and labor intensive.

Details

Technology areaGN&C > GN&C Systems Engineering Technologies > GN&C Fault Management, Fault Tolerance, and Autonomy
ProgramCenter Innovation Fund: JPL CIF (JPL CIF)
Lead organizationJet Propulsion Laboratory, Pasadena, CA
Start date2020-10-01
End date2021-09-30

Project contacts

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How to get involved

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