The development of remote sensing satellite constellations has created an increasing need for periodic observations of ground targets. At present, the research on periodic observation is not sufficient, and the multisatellite scheduling algorithm based on deep reinforcement learning (DRL) is still relatively few and is trained inefficiently. In this paper, a periodic observation mission planning (POMP) algorithm is proposed for the periodic Earth observation scheduling problem. The POMP is based on the encoder/decoder architecture. First, static and dynamic attributes of the observation task and satellite are encoded using three convolutional networks in the encoder and decoder. Second, an additive attention mechanism is employed to calculate the probability of each observation task being selected. Third, the model is trained with the REINFORCE with rollout baseline algorithm. Experimental results in various scenarios show that both training with the REINFORCE with rollout baseline algorithm and encoding dynamic attributes of the satellite as part of the step context vector are effective. The proposed POMP can maintain the gap with the ant colony optimization (ACO) algorithm in weight degree of timeout (WDT) and total timeout within 0.7% and 1.5 h, respectively, while having a tens of seconds advantage over ACO in terms of computational time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning-Based Periodic Earth Observation Scheduling for Agile Satellite Constellation


    Contributors:
    Wang, Xin (author) / Zhao, Fanyu (author) / Shi, Zhong (author) / Jin, Zhonghe (author)

    Published in:

    Publication date :

    2023-05-09


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Optimization-Based Scheduling Method for Agile Earth-Observing Satellite Constellation

    Cho, Doo-Hyun / Kim, Jun-Hong / Choi, Han-Lim et al. | AIAA | 2018



    Agile Earth Observation Satellite Scheduling With a Quantum Annealer

    Stollenwerk, Tobias / Michaud, Vincent / Lobe, Elisabeth et al. | IEEE | 2021



    Single-Agent Reinforcement Learning for Scalable Earth-Observing Satellite Constellation Operations

    Herrmann, Adam / Stephenson, Mark A. / Schaub, Hanspeter | AIAA | 2023