Generation of Spacecraft Operations Procedures Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Generation of Spacecraft Operations Procedures Using Deep Reinforcement Learning

Published:2021-12-13 Issue: Volume: Page:1-16
ISSN:0022-4650
Container-title:Journal of Spacecraft and Rockets
language:en
Short-container-title:Journal of Spacecraft and Rockets

Author:

Harris Andrew¹,Valade Trace¹,Teil Thibaud¹,Schaub Hanspeter¹^ORCID

Affiliation:

1. University of Colorado Boulder, Boulder, Colorado 80309

Funder

U.S. Department of Defense

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Space and Planetary Science,Aerospace Engineering

Link

https://arc.aiaa.org/doi/pdf/10.2514/1.A35169

Reference17 articles.

1. “NASA Technology Taxonomy 2020,” NASA TR, July 2015, 2020.

2. “DARPA 2019 Strategic Framework,” Defense Advanced Research Project Agency TR, Aug. 2019, https://www.darpa.mil/about-us/about-darpa [accessed 17 Nov. 2021].

3. Demonstration of autonomous rendezvous technology (DART) project summary

4. Using Autonomy Flight Software to Improve Science Return on Earth Observing One

5. Optimization-based scheduling for the single-satellite, multi-ground station communication problem

Cited by 11 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. A mission planning method for deep space detectors using deep reinforcement learning;Aerospace Science and Technology;2024-10

2. 3- and 6-degree-of-freedom analysis of the Magellan aerobraking experiment and planar aerobraking at Venus;Aerospace Science and Technology;2024-09

3. Simulation-Based Results of the Trusted Distributed Autonomy Demonstration Experiment;2024 IEEE Aerospace Conference;2024-03-02

4. Deep Reinforcement Learning for Autonomous Satellite Responsiveness to Observed Events;2024 IEEE Aerospace Conference;2024-03-02

5. Using Enhanced Simulation Environments to Accelerate Reinforcement Learning for Long-Duration Satellite Autonomy;AIAA SCITECH 2024 Forum;2024-01-04