Cluster intelligence refers to the emergence of collective behavior, such as collaborative detection, which compensates for individual limitations and accomplishes complex tasks through effective coordination among intelligent agents. The distributed strategy necessitates high autonomy for each spacecraft, with communication connections between adjacent spacecraft enabling state exchange. Firstly, the composition of the spacecraft cluster detection system described in this article is introduced. Then, a multi-agent reinforcement learning algorithm is introduced to address the aforementioned multivariable sequence decision-making problem. The entire sequence decision problem is divided into multiple time steps for multi-agent reinforcement learning modeling. Agents interact with the environment and receive reward feedback from it. After adopting the Actor Critic algorithm, each agent's optimization goal is to maximize their cumulative expected reward. The actor aims to learn the agent's strategy function and maximize expected cumulative rewards, while critics learn a value function to evaluate current state value and guide actor strategy optimization. Finally, scenario design rules and reward settings are based on collaborative target detection by search and tracking spacecrafts. This enables collaborative control of both types of spacecrafts, achieving 35 successful target tracks in line with task requirements.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Collaborative Control Method for Spacecraft Clusters Based on Multi Agent Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Deng, Yimin (Herausgeber:in) / Liang, Xi (Autor:in) / Wei, Cheng (Autor:in) / Zhao, Jianbo (Autor:in) / Wang, Peng (Autor:in) / Cheng, Zihao (Autor:in)

    Kongress:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Erscheinungsdatum :

    06.03.2025


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Resilient Multi-Agent Collaborative Spacecraft Inspection

    Choi, Changrak / Nakka, Yashwanth Kumar / Rahmani, Amir et al. | IEEE | 2023


    Multi-Vehicle Collaborative Lane Changing Based on Multi-Agent Reinforcement Learning

    Zhang, Xiang / Li, Shihao / Wang, Boyang et al. | IEEE | 2024


    Spacecraft cluster game hunting motion planning method based on multi-agent reinforcement learning

    CHEN GANG / CUI ZI'ANG / LIU YUQIANG et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Scaling Collaborative Space Networks with Deep Multi-Agent Reinforcement Learning

    Ma, Ricky / Hernandez, Gabe / Hernandez, Carrie | IEEE | 2023


    Adaptive Task Flow Management of Distributed Spacecraft Bus based on Multi Agent Reinforcement Learning

    Eishima, T. / Nakasuka, S. / European Space Agency | British Library Conference Proceedings | 2005