TRM-A2C Planning Method for Mega-Constellation Region Observation Mission

• Published in: Chinese Control and Decision Conference pp. 520 - 526
• Main Authors: He, Jiadao, Xu, Rui, Li, Zhaoyu, Li, Xiuwei, Zhu, Shengying, Nie, Tao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.05.2025
• Subjects: A2C; Feature extraction; Mask; mega-constellation; Neural networks; Optimization; Planning; region observation; reinforcement learning; Satellites; Testing; Time factors; Time series analysis; Training; Transmission line measurements
• ISSN: 1948-9447
• DOI: 10.1109/CCDC65474.2025.11090191

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.