As to support the mission of Mars exploration in China, automated mission planning is required to enhance security and robustness of deep space probe. Deep space mission planning requires modeling of complex operations constraints and focus on the temporal state transitions of involved subsystems. Also, state transitions are ubiquitous in physical systems, but have been elusive for knowledge description. We introduce a modeling approach to cope with these difficulties that takes state transitions into consideration. The key technique we build on is the notion of extended states and state transition graphs. Furthermore, a heuristics that based on state transition graphs is proposed to avoid redundant work. Finally, we run comprehensive experiments on selected domains and our techniques present an excellent performance.


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    Titel :

    Enriching mission planning approach with state transition graph heuristics for deep space exploration


    Beteiligte:
    Jin, Hao (Autor:in) / Xu, Rui (Autor:in) / Xu, Wenming (Autor:in) / Cui, Pingyuan (Autor:in) / Zhu, Shengying (Autor:in)

    Kongress:

    AOPC 2017: Space Optics and Earth Imaging and Space Navigation ; 2017 ; Beijing,China


    Erschienen in:

    Proc. SPIE ; 10463


    Erscheinungsdatum :

    24.10.2017





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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