Prototype of a Computer Vision-Based CubeSat Detection System for Laser Communications

被引:0
|
作者
I. Medina
J. J. Hernández-Gómez
C. R. Torres-San Miguel
L. Santiago
C. Couder-Castañeda
机构
[1] Instituto Politécnico Nacional,
[2] Centro de Desarrollo Aeroespacial,undefined
[3] Instituto Politécnico Nacional,undefined
[4] Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco,undefined
[5] Sección de Estudios de Posgrado e Investigación,undefined
关键词
Computer vision; CubeSat; Pointing; Tracking; Satellites;
D O I
暂无
中图分类号
学科分类号
摘要
Up to now, CubeSat nano-satellites have strong limitations in communication data rates (∼100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim \hbox {100}$$\end{document} kbps) and bandwidth due to the strictness of CubeSat standard. However, if they could be endowed with optical communications (data rates up to 1 Gbps in optimal state), CubeSat applications would exponentially increase. Nonetheless, laser communications face some important drawbacks as the development of a very strict and accurate tracking mechanism. This work proposes an on-board fine pointing system to locate an optical ground station beacon using an embedded system complying with the restrictive CubeSat standard. Such on-board fine pointing system works based on computer vision. The experimental prototype is implemented in Matlab/Simulink, within a Raspberry Pi 3B. The main outcome is the usage of off-the-shelf components (COTS), obtaining an efficient tracking with low power consumption in very noisy and reflective environments. The developed system proves to be fast, stable and strong. It also satisfies the strict size and power consumption restrictions of CubeSat standard.
引用
收藏
页码:717 / 725
页数:8
相关论文
共 50 条
  • [41] Vision-based vehicle detection for a driver assistance system
    Kuo, Ying-Che
    Pai, Neng-Sheng
    Li, Yen-Feng
    COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2011, 61 (08) : 2096 - 2100
  • [42] A Vision-Based System for Power Transmission Facilities Detection
    Wu, Denglu
    Li, Bingfeng
    Li, Wentao
    Xia, Yong
    Tang, Yandong
    APPLIED MATERIALS AND TECHNOLOGIES FOR MODERN MANUFACTURING, PTS 1-4, 2013, 423-426 : 2547 - +
  • [43] A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning
    Safyari, Yashar
    Mahdianpari, Masoud
    Shiri, Hodjat
    SENSORS, 2024, 24 (17)
  • [44] A Vision-based Infant Respiratory Frequency Detection System
    Fang, Chiung-Yao
    Hsieh, Hsin-Hung
    Chen, Sei-Wang
    2015 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), 2015, : 84 - 91
  • [45] Vision-Based Overload Detection System for Land Transportation
    Wang, Yuntao
    Xia, Chengxi
    Sun, Haibo
    Zhang, Yihan
    Liu, Zheyan
    Wang, Yufei
    Xu, Naixuan
    Zhu, Jianjia
    Zhang, Yuchen
    Wu, Huaqiang
    Shi, Yuanchun
    CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 4915 - 4928
  • [46] A Vision-Based Obstacle Detection System for Parking Assistance
    Lin, Yu-Chen
    Lin, Che-Tsung
    Liu, Wei-Cheng
    Chen, Long-Tai
    PROCEEDINGS OF THE 2013 IEEE 8TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2013, : 1627 - 1630
  • [47] Airborne Vision-Based Collision-Detection System
    Lai, John
    Mejias, Luis
    Ford, Jason J.
    JOURNAL OF FIELD ROBOTICS, 2011, 28 (02) : 137 - 157
  • [48] Deep Learning for Accurate Corner Detection in Computer Vision-Based Inspection
    Ercan, M. Fikret
    Ben Wang, Ricky
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT II, 2021, 12950 : 45 - 54
  • [49] Convolutional neural networks for computer vision-based detection and recognition of dumpsters
    Ramirez, Ivan
    Cuesta-Infante, Alfredo
    Pantrigo, Juan J.
    Montemayor, Antonio S.
    Moreno, Jose Luis
    Alonso, Valvanera
    Anguita, Gema
    Palombarani, Luciano
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (17): : 13203 - 13211
  • [50] Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges
    Zhou, Jing
    Huo, Linsheng
    JOURNAL OF SENSORS, 2021, 2021