| 引用本文: | 贾晓冷,叶东,王博,孙兆伟.模块化自重构卫星智能变构规划[J].哈尔滨工业大学学报,2025,57(4):1.DOI:10.11918/202401062 |
| JIA Xiaoleng,YE Dong,WANG Bo,SUN Zhaowei.Intelligent reconfiguration planning for modular self-reconfigurable satellite[J].Journal of Harbin Institute of Technology,2025,57(4):1.DOI:10.11918/202401062 |
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| 模块化自重构卫星智能变构规划 |
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贾晓冷1,2,叶东1,2,王博1,2,孙兆伟1,2
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(1.哈尔滨工业大学 航天学院,哈尔滨 150001; 2.微小型航天器快速设计与智能集群全国重点实验室(哈尔滨工业大学), 哈尔滨 150090)
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| 摘要: |
| 为解决航天任务复杂化与传统定构型卫星设计之间的矛盾,航天机构着眼于研究具有灵活构型变化能力的模块化自重构卫星,其中变构规划是一个具有挑战性的研究领域。针对模块化卫星变构问题,以立方体晶格型卫星作为研究对象,基于图论提出了描述卫星拓扑结构的构型矩阵和拓展矩阵。通过对卫星模块运动特点的研究,给出了求解模块运动可达空间的算法。将卫星的变构问题视为序列决策问题,基于深度强化学习理论,将变构过程建模为马尔可夫决策过程,设计了基于演员-评论家（actor-critic）模型的智能变构规划方法,建立多层神经网络以近似演员与评论家函数,通过训练神经网络,逐步改进卫星变构策略性能。仿真实验结果表明, 所提出的变构方法对于给定的卫星算例,可以得到逐步改进的卫星变构策略,针对不同模块数的卫星构型具有通用性,同时相比于传统基于启发式搜索的变构方法,在变构步数、计算时间和变构成功率上具有优势,验证了所提出的智能规划方法在未来模块化卫星设计工作中具有潜在的价值。 |
| 关键词: 模块化自重构卫星 变构规划 深度强化学习 神经网络 演员-评论家模型 |
| DOI：10.11918/202401062 |
| 分类号:V423.4+1 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(2,5) |
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| Intelligent reconfiguration planning for modular self-reconfigurable satellite |
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JIA Xiaoleng1,2,YE Dong1,2,WANG Bo1,2,SUN Zhaowei1,2
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(1.School of Astronautics, Harbin Institute of Technology, Harbin 150001, China; 2.State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster (Harbin Institure of Technology),Harbin 150090,China)
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| Abstract: |
| To address the conflict between the complexity of aerospace tasks and the design of traditional fixed-configuration satellites, aerospace institutions are focusing on the study of modular self-reconfigurable satellites with flexible configuration change capabilities. Among these efforts, configuration planning emerges as a particularly challenging area of research. Aiming at the configuration problem of modular satellites, cube-lattice satellites are taken as the research object, and based on graph theory, a configuration matrix and an extension matrix are proposed to describe the satellite topology. Through the study of the motion characteristics of the satellite module, an algorithm for solving the accessible space of the module motion is given. Considering the satellite configuration problem as a sequential decision-making problem, and based on the theory of deep reinforcement learning, the modification process is modeled as a Markov decision process. An intelligent modification planning method based on the actor-critic model is designed, incorporating a multi-layer neural network to approximate the actor and critic functions. Through training the neural network, the performance of the satellite reconfiguration strategy is progressively improved. The simulation experimental results show that the proposed configuration method yields progressively improved satellite reconfiguration strategies for the given satellite case studies. This approach exhibits generality across different satellite configurations with varying numbers of modules. Additionally, compared with the traditional configuration method based on heuristic search, it has advantages in the number of configuration steps, calculation time and configuration success rate, which validates that the proposed intelligent planning method has potential value in future modular satellite design work. |
| Key words: modular self-reconfigurable satellite planning of reconfiguration deep reinforcement learning neural network actor-critic model |
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