Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering ( IF 1.1 ) Pub Date : 2021-03-17 , DOI: 10.1177/0954410021996129 Seongmin Lim 1 , Jin-Hyung Kim 2 , Hae-Dong Kim 1, 2
Since nanosatellites are spotlighted as a verification platform for space technology, new studies on on-orbit satellite servicing using nanosatellites are being conducted. This servicing is based on space robotics using vision-based sensors in the rendezvous state with a target satellite. The space environment, such as sunlight and Earth albedo, affects the mission. Simulation of the space environment on the ground is difficult, but the development of robust algorithms which reflect the effect is essential. In particular, missions such as active debris removal require a method for overcoming changes in any known information due to external factors such as collisions. This study proposes a new strategy on nanosatellite for on-orbit space object classification by applying deep learning to sensor-based orbit satellite service activity. When previously known information is changed, a method of online learning on orbit after obtaining additional data at a short relative distance can help determine the final service part. Using the images and point cloud data that simulate the space environment, we apply a convolutional neural network and PointNet to classify the objects. The learning environment is studied using a general desktop and a micro-graphics processing unit (GPU) board that can be mounted on a nanosatellite. For the training, we used self-produced data using 3D models of nanosatellites and asteroids with similar shapes, which are difficult to distinguish with existing algorithms. Consequently, the proposed strategy by the author shows feasibility of using nanosatellite’s micro GPU for on-orbit space object classification, and it is verified that point cloud–based methods are more suitable by utilizing deep learning for nanosatellites. The proposed method with processor of nanosatellite is applicable to satellite service missions in orbit, such as capturing of robotic parts for extending life span or removing space debris.
中文翻译:
基于深度学习的在轨空间物体分类策略
由于将纳米卫星作为太空技术的验证平台而受到关注,因此正在开展有关使用纳米卫星的在轨卫星服务的新研究。这项服务基于太空机器人技术,该技术使用的是具有目标卫星的会合状态的基于视觉的传感器。诸如阳光和地球反照率之类的太空环境会影响飞行任务。在地面上模拟太空环境是困难的,但是开发能够反映这种影响的鲁棒算法至关重要。尤其是诸如主动清除碎片之类的任务需要一种方法来克服由于外部因素(例如碰撞)而导致的任何已知信息的变化。这项研究通过将深度学习应用于基于传感器的轨道卫星服务活动,提出了一种用于纳米卫星的在轨空间物体分类的新策略。如果更改了先前已知的信息,则可以在较短的相对距离下获取其他数据之后在轨在线学习的方法,可以帮助确定最终的服务部分。使用模拟空间环境的图像和点云数据,我们应用了卷积神经网络和PointNet对对象进行分类。使用通用台式机和可安装在纳米卫星上的微图像处理单元(GPU)板对学习环境进行研究。在训练中,我们使用了具有相似形状的纳米卫星和小行星的3D模型的自产生数据,这些数据很难与现有算法区分开。因此,作者提出的策略显示了使用纳米卫星的微型GPU进行在轨空间物体分类的可行性,事实证明,通过将深度学习用于纳米卫星,基于点云的方法更合适。所提出的带有纳米卫星处理器的方法适用于在轨卫星服务任务,例如捕获机器人零件以延长寿命或清除空间碎片。