Autonomy is increasingly crucial in space missions due to several factors driving the exploration and utilization of space. In the meanwhile, Artificial Intelligence methods begin to play a crucial role in addressing the challenges associated with and enhancing autonomy in space missions. The proposed work develops a closed-loop simulator for proximity operations scenarios, particularly for the inspection of an unknown and uncooperative target object, with a fully AI-based image processing and GNC chain. This tool is based on four main blocks: image generation, image processing, navigation filter, and guidance and control blocks. All of them have been separately tested and tuned to ensure the correct interface and compatibility in the close-loop architecture. Afterwards, the overall architecture is deployed in an extensive Montecarlo testing campaign to verify and validate the performance of the proposed IP-GNC loop.

中文翻译：

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用于自主检查不合作空间物体的闭环 AI 辅助图像 GNC


由于推动太空探索和利用的几个因素，自主性在太空任务中变得越来越重要。与此同时，人工智能方法开始在应对与太空任务相关的挑战和增强太空任务的自主性方面发挥关键作用。拟议的工作开发了一个用于接近操作场景的闭环模拟器，特别是用于检查未知和不合作的目标物体，具有完全基于 AI 的图像处理和 GNC 链。该工具基于四个主要模块：图像生成、图像处理、导航过滤器以及制导和控制模块。所有这些都经过了单独的测试和调整，以确保在闭环架构中具有正确的接口和兼容性。之后，将整体架构部署在广泛的 Montecarlo 测试活动中，以验证和确认拟议的 IP-GNC 环路的性能。