@inproceedings{a27645096c9343e284b38326f85ab94e,
title = "TRM-A2C Planning Method for Mega-Constellation Region Observation Mission",
abstract = "The efficient management of mega-constellation satellite resources and the rapid planning of observation missions are critical driving force for the advancement of space technology. To address the dimensionality explosion problem in the solution space for regional observation mission planning of mega-constellations and to satisfy timely demands, a task planning method based on an A2C (Advantage Actor-Critic) neural network with dynamic temporal relation Mask (TRMA2C) is proposed. Firstly, a discrete state space related to the quality of observation windows is designed, and a hybrid optimization objective function that integrates task completion rate, time window quality, and the timeliness of observation activities is constructed. Secondly, the TRM is designed for application in the process of policy gradient updates and value function estimation. The effectiveness and efficiency of the TRM-A2C method are validated through testing and comparative experimental simulations. This approach thereby provides theoretical and technical support for the operation and management of Chinese mega-constellations.",
keywords = "A2C, Mask, mega-constellation, region observation, reinforcement learning",
author = "Jiadao He and Rui Xu and Zhaoyu Li and Xiuwei Li and Shengying Zhu and Tao Nie",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 37th Chinese Control and Decision Conference, CCDC 2025 ; Conference date: 16-05-2025 Through 19-05-2025",
year = "2025",
doi = "10.1109/CCDC65474.2025.11090191",
language = "English",
series = "Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "520--526",
booktitle = "Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025",
address = "United States",
}