留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

自由漂浮空间非合作目标的运动预测

王齐帅 周邦召 刘晓峰 蔡国平

王齐帅,周邦召,刘晓峰,蔡国平. 自由漂浮空间非合作目标的运动预测 [J]. 应用数学和力学,2021,42(11):1103-1112 doi: 10.21656/1000-0887.420017
引用本文: 王齐帅,周邦召,刘晓峰,蔡国平. 自由漂浮空间非合作目标的运动预测 [J]. 应用数学和力学,2021,42(11):1103-1112 doi: 10.21656/1000-0887.420017
WANG Qishuai, ZHOU Bangzhao, LIU Xiaofeng, CAI Guoping. Motion Prediction of Free-Floating Space Non-Cooperative Targets[J]. Applied Mathematics and Mechanics, 2021, 42(11): 1103-1112. doi: 10.21656/1000-0887.420017
Citation: WANG Qishuai, ZHOU Bangzhao, LIU Xiaofeng, CAI Guoping. Motion Prediction of Free-Floating Space Non-Cooperative Targets[J]. Applied Mathematics and Mechanics, 2021, 42(11): 1103-1112. doi: 10.21656/1000-0887.420017

自由漂浮空间非合作目标的运动预测

doi: 10.21656/1000-0887.420017
基金项目: 国家自然科学基金(11772187;11802174);中国博士后科学基金(2018M632104)
详细信息
    作者简介:

    王齐帅(1992—),男,博士生(E-mail:wqs300390@sjtu.edu.cn

    蔡国平(1965—),男,教授,博士,博士生导师(通讯作者. E-mail:caigp@sjtu.edu.cn)

  • 中图分类号: V249

Motion Prediction of Free-Floating Space Non-Cooperative Targets

  • 摘要: 空间非合作目标的运动预测是航天器在轨服务中的一个重要问题。在获得非合作目标的运动预测结果后,追踪星即可规划运动轨迹以接近目标并对其进行捕获。该文提出了一种自由漂浮空间非合作目标的运动预测方法。该方法的核心思想是首先辨识出目标的姿态动力学参数和目标的质心运动学参数,然后利用参数辨识结果和目标的动力学方程实现对目标的运动预测。在姿态动力学参数的辨识过程中,首先对目标的惯性参数进行初步辨识,然后采用自适应无迹Kalman滤波器对姿态动力学参数进行粗略辨识,最后通过最优化方法进一步提高姿态动力学参数的辨识精度。该文通过数值仿真验证了所提运动预测方法的有效性。仿真结果表明,无论目标是做单轴旋转还是翻滚运动,所提运动预测方法都能够实现对目标的长时间高精度的运动预测。
  • 图  1  目标所在的坐标系

    Figure  1.  Coordinate systems of the target

    图  2  特征点和坐标系${\varSigma _b}$的关系

    Figure  2.  Feature points and frame ${\varSigma _b}$

    图  3  运动预测结果的误差

    Figure  3.  The errors of the predicted results

    图  4  100次仿真所得$ {e_{{\rm{rot}}}}(1\;087\;{\text{s}}) $${e_{{r_c}}}({\text{1 087}}\;{\text{s}})$${e_{{r_b}}}({\text{1 087}}\;{\text{s}})$的频率分布直方图

    Figure  4.  The histograms of frequency distribution of $ {e_{{\rm{rot}}}}(1\;087\;{\text{s}}) $, ${e_{{r_c}}}({\text{1 087}}\;{\text{s}})$ and ${e_{{r_b}}}({\text{1 087}}\;{\text{s}})$ in 100 simulations

    表  1  数值仿真中参数的设置

    Table  1.   Setting of the parameters in the numerical simulations

    parametervalue
    inertia matrix $ {{\bar{\boldsymbol{I}}}_{{b_0}}} $ of the target $ {\rm{diag}}(1,0.8,0.52) $
    rotation vector $ {{\boldsymbol{p}}_{{b_0}b}} $ from $ {\varSigma _{{b_0}}} $ to $ {\varSigma _b} $ $ {\left[ {1,\;2,\;3} \right]^{\text{T}}} $
    coordinate vector $ {\boldsymbol{\rho }}_{cb}^{{b_0}} $ $ {\left[ {0.5,{\text{ 0}}{\text{.2}},{\text{ 0}}.3} \right]^{\text{T}}} $
    initial angular velocity parameter $ {k_{\rm{e}}} $ $ 0,\;0.01,\;0.035,\;0.05,\;0.1,\;0.3,\;0.5,\;0.8 $
    noise levels $ \left[ {{\sigma _{{\rm{tran}}}},\;{\sigma _{{\rm{rot}}}}} \right] $ (Gaussian white noise), and from small to large denoted by S, M, L and XL $ \left[ {5\;{\text{mm}},\;1^\circ } \right] $,$ \left[ {10\;{\text{mm}},\;2^\circ } \right] $,
    $ \left[ {15\;{\text{mm}},\;3^\circ } \right] $,$ \left[ {20\;{\text{mm}},\;{\text{ 4}}^\circ } \right] $
    下载: 导出CSV

    表  2  在所有组合情况下所提运动预测方法所得的$ {t_{{\rm{val}}}} $(单位:s)

    Table  2.   The values of $ {t_{{\rm{val}}}} $ obtained with the proposed method under all working conditions (unit: s)

    noise level $k_{\rm{e}}$
    00.010.0350.050.10.30.50.8
    S2292298205420512430237522961453
    M2191087936935122810751162740
    X34662110951093810811582932
    XL932933523621794703478296
    下载: 导出CSV

    表  3  在所有组合情况下所提运动预测方法所用平均观测时间$ {T_{{\rm{average}}}} $(单位:s)

    Table  3.   The mean observation time $ {T_{{\rm{average}}}} $ of 100 simulations obtained with the proposed method under all working conditions (unit: s)

    noise level $k_{\rm{e}}$
    00.010.0350.050.10.30.50.8
    S732.5701.4702.8702.8708.7704.2704.2700
    M830.2793.2784.5784.5785.0783.2783.1792.7
    X1159.71059.61046.21046.21039.21038.51038.31045.6
    XL1341.71305.11274.51274.51277.71289.61268.61277.7
    下载: 导出CSV
  • [1] FLORES-ABAD A, MA O, PHAM K, et al. A review of space robotics technologies for on-orbit servicing[J]. Progress in Aerospace Sciences, 2014, 68: 1-26. doi: 10.1016/j.paerosci.2014.03.002
    [2] OPROMOLLA R, FASANO G, RUFINO G, et al. A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations[J]. Progress in Aerospace Sciences, 2017, 93: 53-72. doi: 10.1016/j.paerosci.2017.07.001
    [3] 徐方暖, 王博, 邓子辰, 等. 基于四元数方法的绳系机器人姿态控制[J]. 应用数学和力学, 2017, 38(12): 1309-1318. (XU Fangnuan, WANG Bo, DENG Zichen, et al. Attitude control of targets captured by tethered space robots based on the quaternion theory[J]. Applied Mathematics and Mechanics, 2017, 38(12): 1309-1318.(in Chinese)
    [4] 王永芳, 於晓榛, 余航. 基于专利分析的自主在轨操作技术发展研究[J]. 飞行力学, 2019, 37(5): 12-17. (WANG Yongfang, YU Xiaozhen, YU Hang. Research on autonomous on-orbit operation technology development based on patent analysis[J]. Flight Dynamics, 2019, 37(5): 12-17.(in Chinese)
    [5] 张治彬, 李新洪, 安继萍, 等. 一种地球静止轨道空间碎片主动清除方式[J]. 现代电子技术, 2018, 41(16): 88-91. (ZHANG Zhibin, LI Xinhong, AN Jiping, et al. An active removal method of space debris in geostationary orbit[J]. Modern Electronics Technique, 2018, 41(16): 88-91.(in Chinese)
    [6] BONNAL C, RUAULT J M, DESJEAN M C. Active debris removal: recent progress and current trends[J]. Acta Astronautica, 2013, 85: 51-60. doi: 10.1016/j.actaastro.2012.11.009
    [7] WOFFINDEN D C, GELLER D K. Navigating the road to autonomous orbital rendezvous[J]. Journal of Spacecraft and Rockets, 2007, 44(4): 898-909. doi: 10.2514/1.30734
    [8] SHARMA S, D’AMICO S. Pose estimation for non-cooperative rendezvous using neural networks[C]//AAS/AIAA Space Flight Mechanics Meeting. Maui County, Hawaii, USA, 2019.
    [9] STRUBE M, HENRY R, SKELETON E, et al. Raven: an on-orbit relative navigation demonstration using international space station visiting vehicles[C]//AAS GNC Conference. Breckenridge, CO, 2015.
    [10] HILLENBRAND U, LAMPARIELLO R. Motion and parameter estimation of a free-floating space object from range data for motion prediction[C]//8th International Symposium on Artificial Intelligence, Robotics and Automation in Space. Munich, Germany, 2005.
    [11] AGHILI F, PARSA K. Motion and parameter estimation of space objects using laser-vision data[J]. Journal of Guidance Control and Dynamics, 2009, 32(2): 537-549.
    [12] LICHTER M D, DUBOWSKY S. Estimation of state, shape, and inertial parameters of space objects from sequences of range images[C]//Proceedings of SPIE: the International Society for Optical Engineering. 2003, 5267: 194-205.
    [13] LICHTER M D, DUBOWSKY S. State, shape, and parameter estimation of space objects from range images[C]//IEEE International Conference on Robotics and Automation. New Orleans, LA, 2004.
    [14] LICHTER M D. Shape, motion, and inertial parameter estimation of space objects using teams of cooperative vision sensors[D]. PhD Thesis. Cambridge: Massachusetts Institute of Technology, 2005.
    [15] TWEDDLE B E, SAENZ-OTERO A, LEONARD J J, et al. Factor graph modeling of rigid-body dynamic for localization, mapping, and parameter estimation of a spinning object in space[J]. Journal of Field Robotics, 2015, 32(6): 897-933. doi: 10.1002/rob.21548
    [16] YUAN J P, HOU X H, SUN C, et al. Fault-tolerant pose and inertial parameters estimation of an uncooperative spacecraft based on dual vector quaternions[J]. Proceedings of the Institution of Mechanical Engineers, Part G:Journal of Aerospace Engineering, 2019, 233(4): 1250-1269. doi: 10.1177/0954410017751766
    [17] LI Y P, WANG Y P, XIE Y C. Using consecutive point clouds for pose and motion estimation of tumbling non-cooperative target[J]. Advances in Space Research, 2019, 63(5): 1576-1587. doi: 10.1016/j.asr.2018.11.024
    [18] MA C, DAI H H, YUAN J P. Estimation of inertial characteristics of tumbling spacecraft using constant state filter[J]. Advances in Space Research, 2017, 60(3): 513-530. doi: 10.1016/j.asr.2017.03.032
    [19] BENNINGHOFF H, BOGE T. Rendezvous involving a non-cooperative, tumbling target-estimation of moments of inertia and center of mass of an unknown target[C]//25th International Symposium on Space Flight Dynamic. München, Deutschland, 2015.
    [20] ZHOU B Z, CAI G P, LIU Y M, et al. Motion prediction of a non-cooperative space target[J]. Advances in Space Research, 2018, 61(1): 207-222. doi: 10.1016/j.asr.2017.10.028
    [21] WAN E A, VAN DER MERWE R. The Unscented Kalman Filter[M]. John Wiley & Sons Inc, 2001.
    [22] LAGARIAS J C, REEDS J A, WRIGHT M H, et al. Convergence properties of the Nelder-Mead simplex method in low dimensions[J]. SIAM Journal of Optimization, 1998, 9(1): 112-147. doi: 10.1137/S1052623496303470
  • 加载中
图(5) / 表(3)
计量
  • 文章访问数:  305
  • HTML全文浏览量:  86
  • PDF下载量:  57
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-01-18
  • 修回日期:  2021-03-16
  • 网络出版日期:  2021-12-07
  • 刊出日期:  2021-11-30

目录

    /

    返回文章
    返回