空间科学学报2024，Vol.44Issue(1)：159-168,10.DOI:10.11728/cjss2024.01.2022-0074
面向即时响应的卫星在轨分布式协商智能任务规划
On-orbit Distributed Negotiation Intelligent Mission Planning for Instant Response
摘要
Abstract
The mission planning of LEO remote sensing constellation is a complex multi-objective op-timization 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 pro-cesses 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 simu-lation 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.关键词
在轨任务规划/即时响应/分布式协商/深度强化学习/多目标优化Key words
On orbit mission planning/Instant response/Distributed negotiation/Deep reinforcement learning/Multiple objective optimization引用本文复制引用
李英玉,史好迎,赵通..面向即时响应的卫星在轨分布式协商智能任务规划[J].空间科学学报,2024,44(1):159-168,10.基金项目
中国科学院重点部署项目资助(ZDRW-KT-2016-02) （ZDRW-KT-2016-02）
