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A Multi-Satellite Multi-Target Observation Task Planning and Replanning Method Based on DQN.

Xiaoyu Xing1,2, Shuyi Wang1,2, Wenjing Liu1,2

  • 1Beijing Institute of Control Engineering, Beijing 100094, China.

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Summary

This study introduces a novel task planning method for satellite constellations using deep Q-learning network (DQN) and matrix sorting. It enables rapid, adaptive task allocation and replanning during satellite failures without retraining the AI model.

Keywords:
deep reinforcement learningsatellite observationtask planningtask re-planning

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Area of Science:

  • Artificial Intelligence
  • Aerospace Engineering
  • Operations Research

Background:

  • Satellite imaging constellations require robust task planning to handle complex, multi-target observation missions.
  • Emergent satellite failures necessitate adaptive planning capabilities to maintain operational efficiency and mission success.
  • Current methods often require extensive retraining of AI models when system failures occur.

Purpose of the Study:

  • To develop an adaptive task planning method for Earth-oriented cooperative observation tasks.
  • To address the challenge of emergent satellite failures in imaging constellations.
  • To enable real-time optimal task allocation and replanning without model retraining.

Main Methods:

  • Integration of deep Q-learning network (DQN) for sequence generation and matrix sorting for optimal task assignment.
  • Formulation of a mission scenario model with task constraints and optimization objectives.
  • Development of a replanning method for sudden satellite failures and urgent task insertions.

Main Results:

  • The proposed method demonstrates fast task planning speeds.
  • Achieves high task completion rates in cooperative observation scenarios.
  • Exhibits immediate task replanning capabilities in contingency situations.

Conclusions:

  • The integrated DQN and matrix sorting approach provides an efficient and adaptive solution for satellite task planning.
  • The method effectively handles emergent satellite failures, ensuring mission continuity.
  • This approach offers a significant advancement in real-time, resilient task allocation for satellite constellations.