Abstract Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.

中文翻译：

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通过极限学习机和贝叶斯优化对不规则小体重力场进行建模

摘要 由于动态环境的不确定性，围绕小物体的近距离操作极具挑战性。围绕小天体的自主制导和导航需要对引力场进行快速准确的建模，以进行潜在的机载计算。在本文中，我们研究了一种基于模型、数据驱动的方法来计算和预测不规则小物体周围的重力加速度。更具体地说，我们采用极限学习机 (ELM) 理论来设计、训练和验证能够学习航天器位置与重力加速度之间关系的单层前馈网络 (SLFN)。基于 ELM 的神经网络在没有迭代调整的情况下进行训练，因此大大减少了训练时间。小行星 25143 Itokawa 和彗星 67/P Churyumov-Gerasimenko 的恒定密度模型的性能分析表明，基于 ELM 的 SLFN 能够在全局和表面附近选定的局部区域学习所需的函数关系。后者产生了一种鲁棒的神经算法，用于在小行星表面附近的近距离操作中对引导和控制所需的重力场进行机载实时计算。