"Traditional satellite layout optimization has heavily relied on manual methods, which are not only time-consuming but also prevent us from fully exploring the potential of the design space," said Yufeng Xia, the paper's lead author. "With increasingly complex and diverse satellite missions, developing efficient holistic satellite design methods has become an urgent challenge."
The 3D-SCALO approach addresses the complex bilevel nature of satellite component layout design, integrating discrete assignment optimization and continuous position optimization into a single model using Mixed Integer Programming (MIP). This eliminates the need for iterative, nested calculations by transforming the problem into a single-level formulation, significantly reducing computational complexity.
To further enhance the precision of 3D spatial relationships within this model, the team developed a linearized 3D Phi-function method, which explicitly manages non-overlapping and safety distance constraints for cuboid components. For more complex, irregular shapes, the Finite Rectangle Method (FRM) was proposed, extending the model's applicability to a broader range of satellite designs.
Testing on real-world cases demonstrated the model's efficiency. In one engineering scenario involving 27 components distributed across 5 modules, the approach found an optimal solution in just 193.5 seconds, highlighting its potential for real-time application in high-stakes aerospace projects.
The team plans to continue advancing the 3D-SCALO framework, focusing on refined modeling and algorithmic enhancements to further support intelligent satellite design.
Research Report:Mixed integer programming modeling for the satellite three-dimensional component assignment and layout optimization problem
Related Links
Beijing University of Aeronautics and Astronautics
Space Technology News - Applications and Research
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