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无人星球车非几何障碍感知研究进展

张天翼 彭松 田鹤

张天翼, 彭松, 田鹤. 无人星球车非几何障碍感知研究进展[J]. 深空探测学报(中英文). doi: 10.15982/j.issn.2096-9287.2020.20200034
引用本文: 张天翼, 彭松, 田鹤. 无人星球车非几何障碍感知研究进展[J]. 深空探测学报(中英文). doi: 10.15982/j.issn.2096-9287.2020.20200034
ZHANG Tianyi, PENG Song, TIAN He. Research Progress of Non-geometric Hazard Perception for Unmanned Planetary Rover[J]. Journal of Deep Space Exploration. doi: 10.15982/j.issn.2096-9287.2020.20200034
Citation: ZHANG Tianyi, PENG Song, TIAN He. Research Progress of Non-geometric Hazard Perception for Unmanned Planetary Rover[J]. Journal of Deep Space Exploration. doi: 10.15982/j.issn.2096-9287.2020.20200034

无人星球车非几何障碍感知研究进展

doi: 10.15982/j.issn.2096-9287.2020.20200034
基金项目: 国家中长期科技发展规划重大专项资助项目
详细信息
    作者简介:

    张天翼(1994– ),女,博士生,主要研究方向:巡视器环境感知。通讯地址:北京空间飞行器总体设计部(100094)E-mail:kaikaizty@163.com

    彭松(1986– ),男,高级工程师,主要研究方向:深空探测总体技术,系统设计、仿真和试验。本文通讯作者。通讯地址:北京空间飞行器总体设计部(100094)E-mail:pengsong20@163.com

    田鹤(1992– ),男,博士生,主要研究方向:巡视器自主科学探测。 通讯地址:北京空间飞行器总体设计部(100094)E-mail:tianhe1118@126.com

  • ● Non-geometric hazard perception is divided into two categories:hazard estimation and hazard prediction. ● Future research on non-geometric obstacles should focus on the following aspects,including the optimization of wheel-soil interaction mechanics model,multi-source information fusion and the application of artificial intelligence technology. ● A slip ratio prediction method based on soil parameter identification is described.
  • 中图分类号: V476.3

Research Progress of Non-geometric Hazard Perception for Unmanned Planetary Rover

  • 摘要: 提升星球车的环境感知能力是提高系统智能性、自主性的前提,非几何障碍感知是其中关键的一环。通过回顾星球车非几何障碍感知发展历程,梳理出非几何障碍感知内容分为当前障碍估测和前方障碍预测两大类;对障碍估测和障碍预测涉及到的沉陷量估测、滑移率估测、星壤参数在线识别、地形分类等国内外研究现状进行了介绍和评述分析,总结得出:未来非几何障碍识别研究应多关注轮地力学模型优化、多源信息融合感知和人工智能技术应用;对基于星壤参数识别的滑移预测方法进行了阐述。
    Highlights
    ● Non-geometric hazard perception is divided into two categories:hazard estimation and hazard prediction. ● Future research on non-geometric obstacles should focus on the following aspects,including the optimization of wheel-soil interaction mechanics model,multi-source information fusion and the application of artificial intelligence technology. ● A slip ratio prediction method based on soil parameter identification is described.
  • 图  1  基于轮地作用图像的车轮沉陷量估测[8]

    Fig.  1  Estimation of wheel sinkage based on wheel-ground interaction image[8]

    图  2  星球车中常见的滑移估计方法[22]

    Fig.  2  Most common slippage estimation approaches for rovers[22]

    图  3  半经验模型轮壤作用力分析图[24]

    Fig.  3  Semi-empirical model of wheel action[24]

    图  4  “好奇号”导航相机图像地形分类预测示例

    Fig.  4  Examples of terrain classifier predicted classes in Curiosity Navcam image

    图  5  3种地形条件下的滑移−坡度曲线[23]

    Fig.  5  Slip vs. slope curves for three terrain classes[23]

    图  6  基于星壤参数识别的滑转预测方法构想

    Fig.  6  Conception of slip prediction framework based on star soil parameter identification

    表  1  无人星球车非几何障碍感知技术

    Table  1  Non-geometric hazard perception of unmanned planetary rover

    星球车非几何障碍感知
    “月球1号”(Luna-1)/ “月球2号”(Luna-2)
    (1970/1973年,前苏联,月球)
    由获取非驱动轮与驱动轮转数估测打滑
    “索杰纳号”(Sojourner)(1996年,美国,火星)通过电机电流大小估测牵引系数来表征地面力学特性
    “勇气号”(Spirit)/“机遇号”(Opportunity)(2004年,美国,火星)视觉测程法估测滑移率
    “好奇号”(Curiosity)(2012年,美国,火星)视觉测程法估测滑移率,车辙辅助估测滑移,通过SPOC-G软件预测前方滑移率
    “玉兔号”(Yutu)/“玉兔2号”(Yutu-2)(2013/2019年,中国,月球)视觉测程法估测滑移率,车辙信息辅助判断沉陷量
    下载: 导出CSV

    表  2  星壤参数

    Table  2  Parameter of soil

    参数代表意义 参数代表意义
    ${k_c}$星壤内聚力模量${k_\varphi }$星壤内摩擦力模量
    $c$星壤内聚力$\varphi $星壤内摩擦角
    K星壤剪切弹性模量n沉陷指数
    ${c_{\rm{2}}}$与最大应力角位置相关的车轮接触角系数${c_{\rm{1}}}$与车轮进入角位置相关的车轮接触角系数
    下载: 导出CSV

    表  3  基于经典轮地力学模型的星壤参数在线识别研究

    Table  3  Research on soil parameter online identification based on classic wheel-soil interaction mechanics model

    提出人求解过程求解参数 提出人求解过程求解参数
    Iagnemma[28-29]简化垂直载荷W和转动扭矩T模型,使用线性最小二乘法在线识别c $\varphi $崔平远[32]运用两点高斯求积公式对力学模型进行简化,先由最速下降法求取一组近似值,再将近似值作为初值运用于牛顿迭代法进行参数求解n ${k_c}$ ${k_\varphi }$
    Hutangkabodee[30]复合辛普森积分对挂钩牵引力进行近似求解,采用牛顿迭代法进行识别$\varphi $ ${k_c}/b + {k_\varphi }$ KMeng[33]简化力学模型,采用线性最小二乘法与牛顿迭代法$\varphi $ ${k_c}/b + {k_\varphi }$ K
    Cross[31]通过多层感知机算法对c和$\varphi $进行在线估计c $\varphi $丁亮[34]采用龙贝格积分法和应力分布线性化方法简化模型,分步采用改进牛顿算法和拟牛顿算法分别对剪切特性参数和承压特性参数进行识别c $\varphi $ ${k_c}/b + {k_\varphi }$ K
    下载: 导出CSV

    表  4  用于地形分类的6类分类器效果对比

    Table  4  Comparison of the effects of six classifiers for terrain classifications

    分类器训练及分类时间分类效果
    SVM训练最慢,分类快精度最高
    kNN训练最快、分类较慢精度较高
    Brooks训练较慢,分类最快精度较差
    PNN训练较快,分类最慢精度相对较差
    贝叶斯训练较快、分类一般精度相对较差
    决策树训练较慢、分类较慢精度相对较差
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-06-15
  • 修回日期:  2020-08-06
  • 网络出版日期:  2021-01-04

无人星球车非几何障碍感知研究进展

doi: 10.15982/j.issn.2096-9287.2020.20200034
    基金项目:  国家中长期科技发展规划重大专项资助项目
    作者简介:

    张天翼(1994– ),女,博士生,主要研究方向:巡视器环境感知。通讯地址:北京空间飞行器总体设计部(100094)E-mail:kaikaizty@163.com

    彭松(1986– ),男,高级工程师,主要研究方向:深空探测总体技术,系统设计、仿真和试验。本文通讯作者。通讯地址:北京空间飞行器总体设计部(100094)E-mail:pengsong20@163.com

    田鹤(1992– ),男,博士生,主要研究方向:巡视器自主科学探测。 通讯地址:北京空间飞行器总体设计部(100094)E-mail:tianhe1118@126.com

  • ● Non-geometric hazard perception is divided into two categories:hazard estimation and hazard prediction. ● Future research on non-geometric obstacles should focus on the following aspects,including the optimization of wheel-soil interaction mechanics model,multi-source information fusion and the application of artificial intelligence technology. ● A slip ratio prediction method based on soil parameter identification is described.
  • 中图分类号: V476.3

摘要: 提升星球车的环境感知能力是提高系统智能性、自主性的前提,非几何障碍感知是其中关键的一环。通过回顾星球车非几何障碍感知发展历程,梳理出非几何障碍感知内容分为当前障碍估测和前方障碍预测两大类;对障碍估测和障碍预测涉及到的沉陷量估测、滑移率估测、星壤参数在线识别、地形分类等国内外研究现状进行了介绍和评述分析,总结得出:未来非几何障碍识别研究应多关注轮地力学模型优化、多源信息融合感知和人工智能技术应用;对基于星壤参数识别的滑移预测方法进行了阐述。

注释:
1)  ● Non-geometric hazard perception is divided into two categories:hazard estimation and hazard prediction. ● Future research on non-geometric obstacles should focus on the following aspects,including the optimization of wheel-soil interaction mechanics model,multi-source information fusion and the application of artificial intelligence technology. ● A slip ratio prediction method based on soil parameter identification is described.

English Abstract

张天翼, 彭松, 田鹤. 无人星球车非几何障碍感知研究进展[J]. 深空探测学报(中英文). doi: 10.15982/j.issn.2096-9287.2020.20200034
引用本文: 张天翼, 彭松, 田鹤. 无人星球车非几何障碍感知研究进展[J]. 深空探测学报(中英文). doi: 10.15982/j.issn.2096-9287.2020.20200034
ZHANG Tianyi, PENG Song, TIAN He. Research Progress of Non-geometric Hazard Perception for Unmanned Planetary Rover[J]. Journal of Deep Space Exploration. doi: 10.15982/j.issn.2096-9287.2020.20200034
Citation: ZHANG Tianyi, PENG Song, TIAN He. Research Progress of Non-geometric Hazard Perception for Unmanned Planetary Rover[J]. Journal of Deep Space Exploration. doi: 10.15982/j.issn.2096-9287.2020.20200034
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