Research Progress of Non-geometric Hazard Perception for Unmanned Planetary Rover
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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。Abstract: Improving the environment perception ability is a prerequisite for improving the intelligence and autonomy of the rover. Non-geometric hazard perception is the key part of autonomous navigation. Reviewing the development of the non-geometric hazard perception of the rover, it shows that the key aspects of the non-geometric hazard perception are hazard estimation and hazard prediction. Non-geometric hazard estimation and prediction include wheel-soil model, wheel sinkage estimation, slip ratio estimation, identification of soil parameters and terrain classification, and the relevant research on theses aspects are introduced and analyzed. The future research on non-geometric hazard perception should focus on the optimization of wheel-soil interaction mechanics model, multi-source information fusion and the application of artificial intelligence technology. Finally, a slip ratio prediction method based on soil parameter identification is described.
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Key words:
- planetary rover /
- environment perception /
- non-geometric hazard /
- slip prediction
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 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 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 
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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
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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 $ ${k_c}/b + {k_\varphi }$ K Meng[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
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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
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