Visual inspection of space-borne assets is of increasing interest to spacecraft operators looking to plan maintenance, characterise damage, and extend the life of high-value satellites in orbit. The environment of Low Earth Orbit (LEO) presents unique challenges when planning inspection operations that maximise visibility, information, and data quality. Specular reflection of sunlight from spacecraft bodies, self-shadowing, and dynamic lighting in LEO significantly impact the quality of data captured throughout an orbit. This is exacerbated by the relative motion between spacecraft, which introduces variable imaging distances and attitudes during inspection. Planning inspection trajectories with the aide of simulation is a common approach. However, the ability to design and optimise an inspection trajectory specifically to improve the resulting image quality in proximity operations remains largely unexplored. In this work, we present $\partial$LITE, an end-to-end differentiable simulation pipeline for on-orbit inspection operations. We leverage state-of-the-art differentiable rendering tools and a custom orbit propagator to enable end-to-end optimisation of orbital parameters based on visual sensor data. $\partial$LITE enables us to automatically design non-obvious trajectories, vastly improving the quality and usefulness of attained data. To our knowledge, our differentiable inspection-planning pipeline is the first of its kind and provides new insights into modern computational approaches to spacecraft mission planning. Project page: https://appearance-aware.github.io/dlite/

翻译：对在轨航天器资产进行视觉巡检，正日益受到航天器运营商的关注，以规划维护任务、表征损伤并延长高价值卫星在轨寿命。低地球轨道（LEO）的环境为规划巡检任务带来了独特挑战，需在最大化可见性、信息获取与数据质量之间取得平衡。航天器表面的日光镜面反射、自遮挡以及LEO中的动态光照，显著影响整个轨道周期内捕获的数据质量。航天器间的相对运动进一步加剧了这一问题，导致巡检过程中成像距离与姿态持续变化。借助仿真辅助规划巡检轨迹是常见方法，然而，专门为提升近距离操作中图像质量而设计与优化巡检轨迹的能力，目前仍鲜有研究。本文提出∂LITE，一种端到端可微分的在轨巡检仿真流程。我们利用前沿的可微分渲染工具与定制轨道传播器，实现了基于视觉传感器数据的轨道参数端到端优化。∂LITE使我们能够自动设计非直观轨迹，大幅提升所获数据的质量与实用性。据我们所知，这一可微分巡检规划流程属同类首创，为现代航天器任务规划的计算方法提供了新见解。项目页面：https://appearance-aware.github.io/dlite/