Abstract This research looks towards improving the accuracy and efficiency of orbit transfer speed increment estimation and task assignment for space patrol missions involving multiple platforms. First, we set up a quick prediction model for the increment in orbit transfer velocity. Then, a two-layered hybrid optimization framework is developed, with a deep neural network sub-model as the inner layer to find the optimal speed increase and the consensus-based bundle algorithm as the outer layer to solve the multi-task patrol sequence problem. Finally, the simulation and analysis of the patrol task assignment problem are conducted under two types of scenarios. The results show that the hierarchical optimization method proposed in this paper is three orders of magnitude faster than the traditional optimization method. In addition, the suggested hierarchical optimization method has significant promise in practical applications, where it can produce solutions more quickly to adapt to more complex patrol scenarios.

    Highlights A multi-layered framework is established to handle the optimization of space patrol task assignment. The deep neural network-based orbit-transfer speed increment calculator is proposed. The consensus-based bundle algorithm-based task allocator achieves efficient assignments. Calculating performance of the proposed method is evaluated.


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    Titel :

    Hierarchical optimization method of space patrol task assignment based on deep neural network and consensus-based bundle algorithm


    Beteiligte:
    Wu, Weinan (Autor:in) / Lu, Huayue (Autor:in) / Xu, Jie (Autor:in) / Sun, Yiming (Autor:in)

    Erschienen in:

    Acta Astronautica ; 207 ; 295-306


    Erscheinungsdatum :

    2023-03-19


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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