Support Surface Estimation for Legged Robots
Support Surface Estimation for Legged Robots
复制标题
支持腿式机器人的表面估计
DOI：
10.1109/icra.2019.8793646
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Marco Hutter
中科院分区：
文献类型：
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作者：
Timon Homberger;Lorenz Wellhausen;Péter Fankhauser;Marco Hutter
腿式系统的高度灵活性使其能够在恶劣的户外环境中运行。在这些情况下，有关地形几何形状的知识对于立足点规划以实现安全移动至关重要。然而，在可穿透或高度顺应的地形（例如草地）上，支撑地面的可见性受到阻碍，即深度传感器无法直接感知。我们提出了一种通过将足部接触闭合位置的触觉信息与外感受传感相融合来估计底层地形的方法。为了从稀疏采样的立足点获得密集的支撑表面估计，我们应用高斯过程回归。通过根据立足点处的离散穿透深度测量来估计可穿透表面层的高度，将外感受信息集成到支撑表面估计过程中。该方法的设计使其能够提供连续的支撑表面估计，即使由于阴影效应而仅存在部分外感受信息。四足机器人 ANYmal 的现场实验展示了机器人如何在茂密的植被中平稳、安全地导航。
The high agility of legged systems allows them to operate in rugged outdoor environments. In these situations, knowledge about the terrain geometry is key for foothold planning to enable safe locomotion. However, on penetrable or highly compliant terrain (e.g. grass) the visibility of the supporting ground surface is obstructed, i.e. it cannot directly be perceived by depth sensors. We present a method to estimate the underlying terrain topography by fusing haptic information about foot contact closure locations with exteroceptive sensing. To obtain a dense support surface estimate from sparsely sampled footholds we apply Gaussian process regression. Exteroceptive information is integrated into the support surface estimation procedure by estimating the height of the penetrable surface layer from discrete penetration depth measurements at the footholds. The method is designed such that it provides a continuous support surface estimate even if there is only partial exteroceptive information available due to shadowing effects. Field experiments with the quadrupedal robot ANYmal show how the robot can smoothly and safely navigate in dense vegetation.