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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Main Authors: Sikai Liu, Jun Yang
Format: Article
Language:English
Published: MDPI AG 2019-11-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/11/11/1373
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author Sikai Liu
Jun Yang
author_facet Sikai Liu
Jun Yang
author_sort Sikai Liu
collection DOAJ
description 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.
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spelling doaj.art-b751ed5a16184df0a1479563b28a5fd42022-12-22T02:07:20ZengMDPI AGSymmetry2073-89942019-11-011111137310.3390/sym11111373sym11111373A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural NetworkSikai Liu0Jun Yang1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaThe 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.https://www.mdpi.com/2073-8994/11/11/1373in-orbit observationtask planningrecurrent neural networkheuristic algorithm
spellingShingle Sikai Liu
Jun Yang
A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
Symmetry
in-orbit observation
task planning
recurrent neural network
heuristic algorithm
title A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
title_full A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
title_fullStr A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
title_full_unstemmed A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
title_short A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
title_sort satellite task planning algorithm based on a symmetric recurrent neural network
topic in-orbit observation
task planning
recurrent neural network
heuristic algorithm
url https://www.mdpi.com/2073-8994/11/11/1373
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AT junyang asatellitetaskplanningalgorithmbasedonasymmetricrecurrentneuralnetwork
AT sikailiu satellitetaskplanningalgorithmbasedonasymmetricrecurrentneuralnetwork
AT junyang satellitetaskplanningalgorithmbasedonasymmetricrecurrentneuralnetwork