Wheeled robot navigation has been widely used in urban environments, but little research has been conducted on its navigation in wild vegetation. External sensors (LiDAR, camera etc.) are often used to construct point cloud map of the surrounding environment, however, the supporting rigid ground used for travelling cannot be detected due to the occlusion of vegetation. This often causes unsafe or not smooth path during planning process. To address the drawback, we propose the PE-RRT* algorithm, which effectively combines a novel support plane estimation method and sampling algorithm to generate real-time feasible and safe path in vegetation environments. In order to accurately estimate the support plane, we combine external perception and proprioception, and use Multivariate Gaussian Processe Regression (MV-GPR) to estimate the terrain at the sampling nodes. We build a physical experimental platform and conduct experiments in different outdoor environments. Experimental results show that our method has high safety, robustness and generalization.

翻译：轮式机器人导航已在城市环境中得到广泛应用，但在野生植被环境中的导航研究尚不充分。外部传感器（如激光雷达、相机等）常被用于构建周围环境的点云地图，然而，由于植被遮挡，用于行驶的支撑刚性地面无法被探测到。这通常会导致规划过程中产生不安全或不平滑的路径。为解决这一问题，我们提出PE-RRT*算法，该算法有效结合了一种新颖的支撑面估计方法与采样算法，能够在植被环境中生成实时可行且安全的路径。为精确估计支撑面，我们融合了外部感知与本体感知，并利用多元高斯过程回归（MV-GPR）对采样节点处的地形进行估计。我们搭建了物理实验平台，并在不同户外环境中开展了实验，结果表明我们的方法具有高安全性、鲁棒性和泛化能力。