Abstract Concerning the onboard autonomous mission planning problem of high and low orbiting agile Earth observation satellites (AEOSs), which requires high algorithm timeliness, a deep reinforcement learning (DRL) based algorithm, namely, the multi-satellite mission planning (MSMP) algorithm, is proposed. The algorithm uses neural networks with an ‘encoder-decoder’ structure and designs a mechanism that enables each satellite to select requests in turn. These two designs allow the algorithm to achieve simultaneous scheduling of multiple satellites. After that, the MSMP uses a REINFORCE with Critic Baseline algorithm to optimize its selection strategy. Computational experiments show that the proposed algorithm can reduce the reasoning time by a factor of tens compared to the adaptive task assignment large neighborhood search (A-ALNS) algorithm, while being able to keep the revenue rate difference to A-ALNS below 5%.


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    Titel :

    Deep reinforcement learning-based autonomous mission planning method for high and low orbit multiple agile Earth observing satellites


    Beteiligte:
    Wang, Xin (Autor:in) / Wu, Jian (Autor:in) / Shi, Zhong (Autor:in) / Zhao, Fanyu (Autor:in) / Jin, Zhonghe (Autor:in)

    Erschienen in:

    Advances in Space Research ; 70 , 11 ; 3478-3493


    Erscheinungsdatum :

    2022-08-05


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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