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%.
Deep reinforcement learning-based autonomous mission planning method for high and low orbit multiple agile Earth observing satellites
Advances in Space Research ; 70 , 11 ; 3478-3493
2022-08-05
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multi-Objective Mission Planning Problem Of Agile Earth Observing Satellites
British Library Conference Proceedings | 2012
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