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Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm

Yanbin Zhao1, *, Bin Du2, Shuang Li2

1 Shanghai Institute of Satellite Engineering, Shanghai, 201109, China.
2 Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.

* Corresponding Author: Yanbin Zhao. Email: email.

(This article belongs to this Special Issue: Nonlinear Computational and Control Methods in Aerospace Engineering)

Computer Modeling in Engineering & Sciences 2020, 122(1), 235-257.


Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First, for the densely distributed target points, a pre-processing scheme based on task clustering is proposed. The target points are clustered according to the distance condition. Second, the local observation path is generated by Tabu algorithm in the inner layer of cluster regions. Third, considering the scatter and cluster sets, the global observation path is obtained by adopting Tabu algorithm in the outer layer. Simulation results show that the algorithm can effectively reduce the task planning time of large-scale point targets while ensuring the optimal solution quality.


Cite This Article

Zhao, Y., Du, B., Li, S. (2020). Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm. CMES-Computer Modeling in Engineering & Sciences, 122(1), 235–257.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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