Volume 44 Issue 1
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LI Yingyu, SHI Haoying, ZHAO Tong. On-orbit Distributed Negotiation Intelligent Mission Planning for Instant Response (in Chinese). Chinese Journal of Space Science, 2024, 44(1): 159-168 doi: 10.11728/cjss2024.01.2022-0074
Citation: LI Yingyu, SHI Haoying, ZHAO Tong. On-orbit Distributed Negotiation Intelligent Mission Planning for Instant Response (in Chinese). Chinese Journal of Space Science, 2024, 44(1): 159-168 doi: 10.11728/cjss2024.01.2022-0074
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On-orbit Distributed Negotiation Intelligent Mission Planning for Instant Response

doi: 10.11728/cjss2024.01.2022-0074 cstr: 32142.14.cjss2024.01.2022-0074
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • 3.

    School of Computer Science, Peking University, Beijing 100871

  • Received Date: 2022-12-26
  • Rev Recd Date: 2023-02-23
    • Available Online: 2023-09-01
    Abstract
  • The mission planning of LEO remote sensing constellation is a complex multi-objective optimization problem. At present, there are some problems in satellite mission planning research based on deep reinforcement learning, such as small scale of test data constellation, single optimization objective, repeated task arrangement and poor model adaptability. To solve the above problems, the CON_DQN (Contract network and Deep Q Network, CON_DQN) algorithm is proposed in this paper, which adopts the master-slave on-orbit distributed negotiation mechanism, the slave satellite makes decisions based on the planning, and the master satellite makes multi-objective optimization decisions from the aspects of priority, resource cost and load balancing based on the deep reinforcement learning algorithm, and processes on-orbit distributed negotiation intelligent mission planning for instant response. Aiming at the scene where the user demand reaches the key observation area dynamically at high frequency, the simulation experiment of different scale task sets of 100-star constellation is carried out. The results show that the proposed algorithm has a fast response speed and can achieve higher task benefits.

     

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