CubeSat Constellation Design Framework Using Simulated Annealing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing constellation design frameworks are inadequate for large-scale satellite systems, particularly for CubeSats, as they rely on computation-heavy commercial tools, lack consideration for connectivity, and are not scalable for hundreds of satellites, failing to provide optimized coverage and connectivity.
Innovation Solution
A computational framework for designing large-scale CubeSat constellations that includes an orbit propagation module, coverage estimation module, connectivity estimation module, and annealing module, using simulated annealing to optimize orbital parameters and inter-satellite links for robust connectivity and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional commercial tools are used for constellation design, then coverage estimation can be performed, but computation time becomes excessively long and scalability is limited
Solution Approach 1:
The patent divides the constellation design problem into separate modules: orbit propagation module, coverage estimation module, connectivity estimation module, and annealing module. Each module handles specific computations independently, allowing the system to process large-scale constellations efficiently without being bottlenecked by a single computationally intensive operation.
Solution Approach 2:
The patent replaces traditional commercial software tools with a custom-built computational framework that uses simulated annealing algorithms. This substitution of the computational approach enables the system to handle large-scale constellation design problems that were previously intractable for conventional tools, significantly reducing computation time while maintaining accuracy.
2Area of stationary object
If the number of satellites is increased to improve coverage, then coverage percentage increases, but system complexity and cost increase
Solution Approach 1:
The patent optimizes constellation design by systematically varying key parameters such as orbital altitude, orbital inclination, number of satellites per orbit, and number of orbital planes. The annealing algorithm explores different parameter combinations to find the optimal configuration that achieves required coverage with the minimum number of satellites, thereby reducing system complexity.
Solution Approach 2:
The patent employs dynamic optimization through simulated annealing, allowing the constellation parameters to evolve iteratively. The system can adaptively adjust the number of satellites, their orbital characteristics, and spatial distribution to achieve optimal coverage efficiency, transforming a static design problem into a dynamic optimization process.
3Reliability
If the number of satellites is increased to improve connectivity, then connectivity metrics improve, but the number of inter-satellite links increases
Solution Approach 1:
The patent optimizes connectivity by adjusting orbital parameters such as altitude and inclination, as well as the spatial distribution of satellites. The annealing algorithm finds parameter configurations that maximize connectivity metrics (number of active inter-satellite links, connectivity reliability) while controlling the overall system complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the connectivity estimation module evaluates the quality of inter-satellite links based on current constellation configuration. This feedback information is fed back to the annealing optimization process, allowing the system to iteratively improve connectivity while avoiding unnecessary increases in system complexity.
4Ease of operation
If existing design frameworks are used, then design process is simple, but they cannot handle large-scale constellations with hundreds of satellites
Solution Approach 1:
The patent structures the design framework as a modular system with distinct modules for orbit propagation, coverage estimation, connectivity estimation, and optimization. This segmentation allows the framework to handle large-scale constellations by processing computations in manageable chunks, maintaining operational simplicity while achieving scalability.
Solution Approach 2:
The patent creates a universal design framework that can handle various constellation configurations and requirements through a single integrated system. The annealing optimization module can adapt to different scenarios (global coverage, regional coverage, different satellite counts) making the framework versatile and scalable without requiring separate tools for different applications.
Data Source
AI summary
A computational framework for designing a constellation that includes a plurality of cube satellites (CubeSats) includes an orbit propagation module, a coverage estimation module, a connectivity estimation module and an annealing module. The orbit propagation module receives a plurality of static parameters for the constellation and determines a position vector, a ground track and sub-satellite points for each of the plurality of CubeSats. The coverage estimation module receives the plurality of static parameters for the constellation and estimates Earth coverage for the constellation. The connectivity estimation module receives the plurality of static parameters for the constellation and determines active inter-satellite links (ISL) in the constellation. The annealing module receives input from the orbit propagation module, the coverage estimation module and the connectivity module and employs an annealing algorithm that generates a constellation design.