Optimization Design of Response Satellite Deployment for Regional Target Emergency Observation

Huang, Huan; Sun, Cai-yi; Hu, Jiaxin; Cai, Weiwei

doi:10.1007/978-981-15-8155-7_47

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2024

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“…传统地面集中式卫星管控的方式在资源状态收集方面存在很高的通信代价且有更新延迟的现象 [4] , 并且从任务到载荷指令的最优化求解也面临规模爆炸难题, 该方式不适应星座规模和用户需求的日益增长. 近年来, 基于遗传算法、模拟退火、禁忌搜索等启发式算法求解多星多任务规划的研究较多, 但Wang [5] 分析该方式面临"建模难"的问题, 并且Huang [6] 发现该方式在突发应急任务的即时处理问题上存在不足. 无论是传统地面集中式卫星管控的方式还是启发式算法均不适合处理大量动态到达且需要即时响应的用户需求.…”

Section: Introductionunclassified

On-orbit Distributed Negotiation Intelligent Mission Planning for Instant Response

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ZHAO

2024

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The mission planning of LEO remote sensing constellation is a complex multi-objective optimization 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

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Section: Introductionunclassified