N形缝光线引入器太阳敏感器技术研究

为适应微小卫星发展的需求,提出了基于线阵电荷耦合器(Charge Coupled Devices, CCD)的N字形狭缝数字太阳敏感器研究方案,以减小系统功耗并降低系统复杂度。该方案通过采用带有N字形狭缝的光线引入器以及单个线阵CCD作为光线探测器,实现了太阳敏感器对两轴太阳角的精确计算。提出利用迭代算法和修正系数对系统折射误差进行修正的方法,进一步结合质心算法,能够快速准确修正系统误差,提高系统精度和分辨率。N型数字太阳敏感器视场角可达(±60°)×(±60°),在整个视场范围内定姿精度优于0.1°,功耗 300mW。该数字太阳敏感器具有低功耗、大视场角和高精度的特点,设计、算法均大大简化,实现了太阳敏感器的微型化,在各种微小卫星上有广阔的应用前景。     

A mask R-CNN based method for inspecting cable brackets in aircraft

In the aviation industry, cable bracket is one of the most common parts. The traditional assembly state inspection method of cable bracket is to manually compare by viewing 3D models. The purpose of this paper is to address the problem of inefficiency of traditional inspection method. In order to solve the problem that machine learning algorithm requires large dataset and manually labeling of dataset is a laborious and time-consuming task, a simulation platform is developed to automatically generate synthetic realistic brackets images with pixel-level annotations based on 3D digital mock-up. In order to obtain accurate shapes of brackets from 2D image, a brackets recognizer based on Mask R-CNN is trained. In addition, a semi-automatic cable bracket inspection method is proposed. With this method, the inspector can easily obtain the inspection result only by taking a picture with a portable device, such as augmented reality (AR) glasses. The inspection task will be automatically executed via bracket recognition and matching. The experimental result shows that the proposed method for automatically labeling dataset is valid and the proposed cable bracket inspection method can effectively inspect cable bracket in the aircraft. Finally, a prototype system based on client-server framework has been developed for validation purpose.