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;


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

Download this article


    [1] Kim H, Chang Y K. Mission scheduling optimization of SAR satellite constellation for minimizing system response time [J]. Aerospace Science and Technology, 2015, 40: 17–32.

    [2] Wang P, Reinelt G, Gao P, et al. A model, a heuristic and a decision support system to solve the scheduling problem of an earth observing satellite constellation [J]. Computers & Industrial Engineering, 2011, 61 (2): 322–335.

    [2] Wang P, Reinelt G, Gao P, et al. A model, a heuristic and a decision support system to solve the scheduling problem of an earth observing satellite constellation [J]. Computers & Industrial Engineering, 2011, 61 (2): 322–335 (in Chinese).

    [4] Lemaitre M, Verfaillie G, Jouhaud F, et al. Selecting and scheduling observations of agile satellites [J]. Aerospace Science and Technology, 2002, 6 (5): 367–381.

    [5] Sarkheyli A, Bagheri A, Ghorbani-Vaghei B, et al.Using an effective tabu search in interactive resources scheduling problem for leo satellites missions [J]. Aerospace Science and Technology, 2013, 29 (1): 287–295.

    [6] Habet D, Vasquez M, Vimont Y. Bounding the optimum for the problem of scheduling the photographs of an agile earth observing satellite [J]. Computational Optimization and Applications, 2010, 47 (2): 307–333.

    [7] Wang X W, Chen Z, Han C. Scheduling for single agile satellite, redundant targets problem using complex networks theory [J]. Chaos, Solitons & Fractals, 2016, 83: 125–132.

    [8] Xu R, Chen H, Liang X, et al. Priority-based constructive algorithms for scheduling agile earth observation satellites with total priority maximization [J]. ExpertSystems with Applications, 2016, 51: 195–206.

    [9] Li Z L, Li X J, Wang Z H. Current status and prospect of agile satellite mission planning [J]. Journal of EquipmentAcademy, 2016, 27 (1): 69–75 (in Chinese).

    [10] Tang Z X, Han C. Agile satellite attitude maneuver strategy study based on directed acyclic graph [J]. Journal of Beijing University of Aeronautics and Astronautics, 2014 (5): 644–650 (in Chinese).

    [11] Zhao L, Wang S, Hao Y, et al. Energy-optimal in orbit mission planning for agile remote sensing satellites [J]. Acta Aeronautica et Astronautica Sinica, 2017, 38 (6): 202–220 (in Chinese).

    [12] CohenRH.Automated spacecraft scheduling-the ASTER example [R]. Ground System Architectures Workshop, 2002: 7–13.

    [13] Wu G, Liu J, Ma M, et al. A two-phase scheduling method with the consideration of task clustering for earth observing satellites [J]. Computers & OperationsResearch, 2013, 40 (7): 1884–1894.

    [14] Wu G, Wang H, Pedrycz W, et al. Satellite observation scheduling with a novel adaptive simulated annealing algorithm and a dynamic task clustering strategy [J]. Computers & Industrial Engineering, 2017, 113: 576–588.

    [15] Wu G H, Ma M H, Wang H L, et al. Multi-satellite observation scheduling based on task clustering [J]. Acta Aeronautica et Astronautica Sinica, 2011, 32 (7): 1275–1282 (in Chinese).

    [16] Li Z, Hao G, Xu P, et al. Agile imaging satellite intensive task clustering and scheduling for disaster monitoring [J]. Research Journal of Chemistry And Environment, 2013, 17: 170–179.

    [17] Bai B C, He R J, Li J F, et al. Imaging satellite observation scheduling with task merging [J]. Acta Aeronautica et Astronautica Sinica, 2009, 30 (11): 2165–2171 (in Chinese).

    [18] Guo L. Research on key problems of agile satellite imaging scheduling problem [D]. Wuhan: School of Computing, Wuhan University, 2015: 50–58 (in Chinese).

    [19] Roemer S, Renner U. Flight experiences with DLR-TUBSAT [J]. Acta Astronautica, 2003, 52 (9): 733–737.

    [20] Buhl M, Segert T, Danziger B. TUBSAT—A reliable and cost effective micro satellite platform [C]. Proceedings of the 61st International Astronautical Congress. Prague, 2010: 16.

    [21] Lian Y, Gao Y, Zeng G. Staring imaging attitude control of small satellites [J]. Journal of Guidance, Control, and Dynamics, 2017, 40 (5): 1–8.

    [22] Jia Z H, Li D, Li L S. Improved ant colony algorithm for solving batch scheduling problem with non-identical jobsizes [J]. Control and Decision, 2014, 29 (10): 1758–1764 (in Chinese).

    [23] Zhang Z J, Feng Z R, Chen Z Q. Simplified ant colony optimization algorithm [J]. Control and Decision, 2012, 27 (9): 1325–1330 (in Chinese).

This Article


CN: 21-1124/TP

Vol 35, No. 03, Pages 613-621

March 2020


Article Outline


  • 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