Abstract
Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for on-orbit servicing and space debris removal systems. The navigation task includes the determination of target object motion, the identification of target object features suitable for grasping, and the identification of collision hazards and other keep-out zones. Given this knowledge, chaser spacecraft can be guided towards capture locations without damaging the target object or without unduly the operations of a servicing target by covering up solar arrays or communication antennas. One way to autonomously achieve target identification, characterization and feature recognition is by use of artificial intelligence algorithms. This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using experimental data obtained in formation flight simulations in the ORION Lab at Florida Institute of Technology. The simulation scenarios vary the yaw motion of the target object, the chaser approach trajectory, and the lighting conditions in order to test the algorithms in a wide range of realistic and performance limiting situations. The data analyzed include the mean average precision metrics in order to compare the performance of the object detectors. The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.
Abstract (translated)
在非合作空间物体周围进行自主导航和路径规划是航天服务和维护系统的一项关键技术。导航任务包括确定目标物体的运动,确定目标物体适合抓取的特征,以及确定碰撞危险和其他禁止区域。给定这些信息,追逐器航天器可以通过不损坏目标物体或通过覆盖太阳能电池板或通信天线的方式,引导到捕捉位置,而无需进行目标识别、特征提取或自主实现。实现目标识别、特征提取和自主实现的一种方法是利用人工智能算法。本文讨论了如何使用摄像机和机器学习算法实现相对导航任务。通过在佛罗里达州理工学院的奥罗尼亚实验室进行组合飞行模拟实验,获取了从 formation flight 到追逐器接近目标物体的一系列实验数据,来测试两个基于深度学习的目标检测算法,即更快的区域卷积神经网络(R-CNN)和You Only Look Once(YOLOv5)的性能。模拟场景根据目标物体的侧向运动、追逐器接近轨迹和照明条件的变化,以测试算法在多种实际和性能限制条件下的性能。数据分析包括平均精确度指标,以比较目标检测算法的性能。本文讨论了实现特征识别算法的路径,并将其集成到航天导航和控制系统中。
URL
https://arxiv.org/abs/2301.09056
