Optimal mission planning with task clustering for intensive point targets observation of staring mode agile satellite

GENG Yuan-zhuo1 GUO Yan-ning1 LI Chuan-jiang1 MA Guang-fu1 LI Wen-bo2

(1.School of Astronautics, Harbin Institute of Technology, Harbin 150001)
(2.Beijing Institute of Control Engineering, Beijing 100190)

【Abstract】An efficient task clustering strategy and an improved ant colony optimization algorithm are proposed for the earth observation scheduling problem with a single agile satellite. Firstly, taking the field of view of the satellite into consideration, the task clustering strategy based on minimum clique partition in graph theory is presented and a series of cliques are built, which can enhance the observation efficiency effectively. Then, this paper presents an improved ant colony optimization algorithm to build the optimal observation path under a time window constraint and the constraint of attitude maneuverability of the satellite. A novel optimal index containing the priorities of targets and the energy consumption for attitude maneuver is proposed to improve the energy efficiency. Besides, in order to overcome the shortcomings of the basic ant colony algorithm which is easily trapped into the region of local minimum, this paper designs a heuristic ant colony algorithm that synthesizes the priorities, time windows of targets, and the transition time of the satellite between targets. Finally, a series of targets on the earth are selected and the effectiveness and efficiency of the algorithm proposed are demonstrated.

【Keywords】 agile staring satellite; intensive targets; staring-mode observation; mission planning; task clustering; ant colony algorithm;

【DOI】

【Funds】 National Natural Science Foundation of China (61403103, 61876050, 61273175)

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This Article

ISSN:1001-0920

CN: 21-1124/TP

Vol 35, No. 03, Pages 613-621

March 2020

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Article Outline

Abstract

  • 0 Introduction
  • 1 Problem formulation
  • 2 The task clustering based on the clique partition method
  • 3 Mission planning based on the heuristic ant colony optimization method
  • 4 Simulation analysis
  • 5 Conclusions
  • References