Deep reinforcement learning-based autonomous mission planning method for high and low orbit multiple agile Earth observing satellites-Reference-Cited by-同舟云学术

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Deep reinforcement learning-based autonomous mission planning method for high and low orbit multiple agile Earth observing satellites

Published:2022-12 Issue:11 Volume:70 Page:3478-3493
ISSN:0273-1177
Container-title:Advances in Space Research
language:en
Short-container-title:Advances in Space Research

Author:

Wang Xin,Wu Jian,Shi Zhong,Zhao Fanyu,Jin Zhonghe

Funder

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Space and Planetary Science,Aerospace Engineering,General Earth and Planetary Sciences,Atmospheric Science,Geophysics,Astronomy and Astrophysics

Reference35 articles.

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2. Multi-satellite Mission Planning Using a Self-Adaptive Multi-agent System;Bonnet,2015

3. Multi-agent collaboration: A satellite constellation case;Bonnet;Front. Artif. Intell. Appl.,2008

4. A prediction method for schedulability of satellite Earth observation task based on Bi-GRU;Chen;J. Hunan Univ. (Natural Sci.),2021

5. Space applications of artificial intelligence;Chien;AI Mag.,2014

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5. Deep Reinforcement Learning-Based Approach With Varying-Scale Generalization for the Earth Observation Satellite Scheduling Problem Considering Resource Consumptions and Supplements;IEEE Transactions on Aerospace and Electronic Systems;2024-06

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