小天体引力场建模的模块化流程：变密度球谐系数的有效表示
Modular pipeline for small bodies gravity field modeling: an efficient representation of variable density spherical harmonics coefficients
AI总结：
本文提出一种基于多面体形状模型和密度分布生成变密度球谐系数引力场模型的模块化流程，用于小天体附近GNC算法的设计、验证与硬件在环测试，并通过多目标对比验证了其建模精度和计算效率。
摘要：
针对小行星和彗星等小天体的接近操作需要高度自主性，以实现具有成本效益、安全且可靠的制导、导航与控制（GNC）方案。因此，在这些目标附近实现自主GNC能力对于未来的空间应用至关重要。然而，这些天体附近环境具有高度非线性和不确定性，带来了独特挑战，需要加以评估以确保对未知形状和引力场的鲁棒性。本文提出了一种用于生成变密度引力场模型的流程，能够生成一组一致的场景，可用于GNC算法的设计、验证和测试。所提出的方法包括处理具有给定密度分布的天体多面体形状模型，以计算与引力场相关的球谐展开系数。为验证该方法，在具有不同形态和物理特性的多种目标上，与解析解、文献结果以及更高保真度模型进行了多项对比。仿真结果证明了该方法的有效性，在建模精度和计算效率方面表现出良好性能。本研究提出了一个更快、更鲁棒的框架，用于生成环境模型，以用于星载GNC算法的仿真和硬件在环测试。
英文摘要：
Proximity operations to small bodies, such as asteroids and comets, demand high levels of autonomy to achieve cost-effective, safe, and reliable Guidance, Navigation and Control (GNC) solutions. Enabling autonomous GNC capabilities in the vicinity of these targets is thus vital for future space applications. However, the highly non-linear and uncertain environment characterizing their vicinity poses unique challenges that need to be assessed to grant robustness against unknown shapes and gravity fields. In this paper, a pipeline designed to generate variable density gravity field models is proposed, allowing the generation of a coherent set of scenarios that can be used for design, validation, and testing of GNC algorithms. The proposed approach consists in processing a polyhedral shape model of the body with a given density distribution to compute the coefficients of the spherical harmonics expansion associated with the gravity field. To validate the approach, several comparison are conducted against analytical solutions, literature results, and higher fidelity models, across a diverse set of targets with varying morphological and physical properties. Simulation results demonstrate the effectiveness of the methodology, showing good performances in terms of modeling accuracy and computational efficiency. This research presents a faster and more robust framework for generating environmental models to be used in simulation and hardware-in-the-loop testing of onboard GNC algorithms.
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