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高轨航天器集群在轨服务智能任务规划方法

郑鑫宇 曹栋栋 唐佩佳 张轶 彭升人 周杰 党朝辉

中国空间科学技术(中英文)2025,Vol.45Issue(1):34-45,12.
中国空间科学技术(中英文)2025,Vol.45Issue(1):34-45,12.DOI:10.16708/j.cnki.1000-758X.2025.0004

高轨航天器集群在轨服务智能任务规划方法

Intelligent mission planning method for on-orbit service of high-orbit spacecraft cluster

郑鑫宇 1曹栋栋 1唐佩佳 1张轶 1彭升人 1周杰 1党朝辉2

作者信息

  • 1. 中国空间技术研究院 钱学森空间技术实验室,北京 100094
  • 2. 西北工业大学 航天学院,西安 710072
  • 折叠

摘要

Abstract

A mission planning model for on-orbit service of high-orbit spacecraft with two optimization objectives,fuel consumption and time consumption,is developed for the high-orbit spacecraft multi-to-multi on-orbit service mission planning.And the Q-learning-based Multi-objective Genetic Algorithm(QMGA)is proposed to solve the model.Firstly,a multi-to-multi objective assignment model based on four-impulse Lambert transfer is established.The velocity impulse consumption and time consumption are taken as the objective functions.By decoupling the problem into the orbit transfer optimization problem and the target assignment optimization problem,the dimension of the optimization variables is reduced,and the calculation process is simplified.Then,combined with Q-learning,the QMGA algorithm is proposed.The Q-learning is used to update the crossover probability and mutation probability of the multi-objective genetic algorithm,which improves the optimization ability of the algorithm.Finally,the QMGA algorithm is adopted to solve the model,and the calculation results are compared with that of the traditional multi-objective genetic algorithm.It is found that the QMGA algorithm can obtain better results and complete multi-to-multi on-orbit service tasks with less fuel consumption in a shorter time.The fuel consumption and the time consumption computed with the QMGA algorithm were 6.2%and 19.7%lower than those computed with MGA algorithm on average,respectively.This proves that the reinforcement learning method can further empower the traditional intelligent optimization method,thereby improving the mission capability of the spacecraft cluster.

关键词

Q学习/多目标遗传算法/多目标分配任务规划/多脉冲Lambert转移/集群任务规划

Key words

Q-learning/multi-objective genetic algorithm/multi-objective assignment mission planning/multi-pulse Lambert transfer/cluster task planning

分类

航空航天

引用本文复制引用

郑鑫宇,曹栋栋,唐佩佳,张轶,彭升人,周杰,党朝辉..高轨航天器集群在轨服务智能任务规划方法[J].中国空间科学技术(中英文),2025,45(1):34-45,12.

基金项目

国家自然科学基金(12172288) (12172288)

中国空间科学技术(中英文)

OA北大核心

1000-758X

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