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Scheduling methods for astronomical satellite Target of Opportunity tasks with high-frequency dynamic arrivals[J]. Chinese Journal of Space Science. doi: 10.11728/cjss2024-0125
Citation: Scheduling methods for astronomical satellite Target of Opportunity tasks with high-frequency dynamic arrivals[J]. Chinese Journal of Space Science. doi: 10.11728/cjss2024-0125

Scheduling methods for astronomical satellite Target of Opportunity tasks with high-frequency dynamic arrivals

doi: 10.11728/cjss2024-0125 cstr: 32142.14.cjss2024-0125
  • Received Date: 2024-10-08
  • Accepted Date: 2025-01-13
  • Rev Recd Date: 2025-01-08
  • Available Online: 2025-03-10
  • In the context of the growing demand for observing a vast number of variable celestial objects detected by sky survey equipment every day, the long sequence task planning problem consisting of high-frequency dynamic Target of Opportunity (ToO) events and follow-up observations has the characteristics of observation event randomness, high timeliness of data acquisition, multiple selectable options, and complex constraints, often considered an NP-hard problem. Consequently, obtaining labeled data for supervised learning is challenging. When applying unsupervised learning through deep reinforcement learning (DRL) methods to solve the long-sequence task planning problem, satellites as agents find it difficult to quickly converge to a global optimal strategy. To address this, this paper draws on the concept of local attention (LA) to improve the pointer network (PN), proposing the Local Attention Pointer Network (LA-PN) algorithm. This algorithm introduces a time window to focus the model on the crucial sequence parts for the current decision, reducing ineffective exploration. Simulation results demonstrate the algorithm's profitability, real-time performance, and generalization ability.

     

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    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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