A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network

The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 11; no. 11; p. 1373
Main Authors Liu, Sikai, Yang, Jun
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2019
Subjects
Algorithms
Artificial intelligence
Communication
Earth observations (from space)
Ground stations
Heuristic
heuristic algorithm
in-orbit observation
Integer programming
Mixed integer
Neural networks
Planning
R&D
recurrent neural network
Recurrent neural networks
Research & development
Satellite observation
Satellites
Search algorithms
Software
task planning
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Summary:The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. In this paper, a mixed integer programming model for observation tasks is established, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed. The configurable probability of the observation task is obtained by constructing a structural symmetric recurrent neural network, and finally, the optimal task planning scheme is obtained. The experimental results are compared with several typical heuristic search algorithms, which have certain advantages, and the validity of the paper is verified. Finally, future application prospects of the method are discussed.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym11111373