面向航天器巡检的光照感知主动感知
Illumination-Aware Active Perception for Spacecraft Inspection
AI总结：
针对航天器巡检中现有规划器忽略光照效应的问题，提出基于分类器变分自编码器（VAE）的巡检规划框架，在仿真实验中其光照表现优于带光照带的几何规划与纯几何规划。
摘要：
恶劣且快速变化的轨道光照会在航天器巡检过程中引发故障模式。现有规划器依赖视线巡检指标，该指标针对已知目标进行遮挡检查，并将几何可见性与任务相关信息混为一谈。这些指标忽略了光照效应，包括日食、自阴影以及航天器材料的镜面反射，且往往在对应图像缺乏足够信息的情况下仍判定部件已完成巡检。我们提出一种巡检规划框架，该框架用从分类器变分自编码器（VAE）直接对捕获图像进行操作所得到的相机信息学习度量，取代几何可见性。编码器模型经训练可从渲染图像块中对航天器表面部件进行分类，其总潜在方差用于学习上述部件的变化光照，用连续的、基于图像的量取代二元可见性检查。我们通过在超写实物理模拟器中实现制导、导航与控制架构来评估该方法，并针对多条轨道改变光照和观测几何。结果显示，尽管所有方法均成功最大化自身目标，但我们的方法能实现更好的光照效果：平均亮度为0.57，81.6%的观测值高于0.25；而带光照带的几何规划平均亮度为0.02，仅3.0%的观测值高于0.25；纯几何规划平均亮度为0.04，仅6.0%的观测值高于0.25。
英文摘要：
Harsh and rapidly varying orbital illumination introduces failure modes during spacecraft inspection. Existing planners rely on line-of-sight inspection metrics which perform occlusion checks against a known target and conflate geometric visibility with task-relevant information. These metrics overlook illumination effects including eclipse, self-shadowing, and specular reflection from spacecraft materials and tend to declare components as inspected despite the corresponding images lacking sufficient information. We propose an inspection-planning framework that replaces geometric visibility with a learned measure of camera information derived from a classifier variational autoencoder (VAE) that operates directly on the captured image. The encoder model is trained to classify spacecraft surface components from rendered patches, and its total latent variance is used to learn the varying illumination of said components, replacing binary visibility checks with a continuous, image-grounded quantity. We evaluate the approach by implementing a Guidance, Navigation, and Control architecture in a photorealistic physics simulator, and vary lighting and viewing geometries with multiple orbits. We show that, although all approaches successfully maximize their own objective, our approach results in better illumination: a mean brightness of $0.57$, with $81.6\%$ of observations above $0.25$, compared with $0.02$ ($3.0\%$) for geometric planning with an illumination band and $0.04$ ($6.0\%$) for purely geometric planning.
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