Using Reinforcement Learning to Design a Low-Thrust Approach into a Periodic Orbit in a Multi-Body System-Reference-Cited by-同舟云学术

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Using Reinforcement Learning to Design a Low-Thrust Approach into a Periodic Orbit in a Multi-Body System

Published:2020-01-05 Issue: Volume: Page:
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Container-title:AIAA Scitech 2020 Forum
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Author:

Sullivan Christopher J.¹,Bosanac Natasha¹

Affiliation:

1. University of Colorado, Boulder

Publisher

American Institute of Aeronautics and Astronautics

Reference29 articles.

1. Trajectory design for a cislunar CubeSat leveraging dynamical systems techniques: The Lunar IceCube mission

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards fully autonomous orbit management for low-earth orbit satellites based on neuro-evolutionary algorithms and deep reinforcement learning;European Journal of Control;2024-06

2. Autonomous Orbital Slot Maintenance with Impulsive Maneuvers and Reinforcement Learning;AIAA SCITECH 2024 Forum;2024-01-04

3. Fast Estimation of Initial Costate for Time-Optimal Trajectory Based on Surrogate Model;Journal of Aerospace Engineering;2023-11

4. Autonomous Guidance Between Quasiperiodic Orbits in Cislunar Space via Deep Reinforcement Learning;Journal of Spacecraft and Rockets;2023-08-25

5. Densely rewarded reinforcement learning for robust low-thrust trajectory optimization;Advances in Space Research;2023-08

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