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"AI + Orbit Design": A Preliminary Exploration of Intelligent Orbit Optimization Based on Data Generation Using STK

Author(s)

Danhe Chen*, Jiahui Yang, Yao Shen

Affiliation(s)

Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China *Corresponding Author

Abstract

With the continuous breakthrough of technology in the aerospace field, the demand for compound innovative talents is becoming more and more urgent. The traditional orbital design or orbital dynamics and control teaching mainly depends on theoretical explanation, case teaching and problem calculation, and the evaluation ability to solve complex problems and practical engineering needs is insufficient. With the rapid development of artificial intelligence and computing capabilities, the integration of AI and platform visual simulation in teaching will guide students to master and use machine learning models for rapid orbit parameter selection and optimization. In this paper, a typical case of "comprehensive optimization of LEO constellation coverage performance" is designed. The constellation orbit is generated by STK simulation tool, and parametric modeling is carried out. Then Python is driven to call STK Connect to automatically extract performance indicators to form a structured data set. Then, AI model training is used to learn to use the optimization algorithm to quickly search the Pareto frontier. Finally, the optimal parameter combination obtained by calculation is re-entered into STK for high-fidelity simulation verification. This research design can not only greatly improve the computational efficiency and scheme optimization, but also significantly enhance the students' interest in learning and cultivate the cross-innovation ability of aerospace talents.

Keywords

AI+; Intelligent Orbit Design; STK; Aerospace Talent Cultivation.

References

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