We propose a novel method for autonomous legged robot navigation in densely vegetated environments with a variety of pliable/traversable and non-pliable/untraversable vegetation. We present a novel few-shot learning classifier that can be trained on a few hundred RGB images to differentiate flora that can be navigated through, from the ones that must be circumvented. Using the vegetation classification and 2D lidar scans, our method constructs a vegetation-aware traversability cost map that accurately represents the pliable and non-pliable obstacles with lower, and higher traversability costs, respectively. Our cost map construction accounts for misclassifications of the vegetation and further lowers the risk of collisions, freezing and entrapment in vegetation during navigation. Furthermore, we propose holonomic recovery behaviors for the robot for scenarios where it freezes, or gets physically entrapped in dense, pliable vegetation. We demonstrate our method on a Boston Dynamics Spot robot in real-world unstructured environments with sparse and dense tall grass, bushes, trees, etc. We observe an increase of 25-90% in success rates, 10-90% decrease in freezing rate, and up to 65% decrease in the false positive rate compared to existing methods.

翻译：摘要：本文针对密集植被环境中包含可穿越/柔韧性与不可穿越/非柔韧性植被的复杂场景，提出一种新型自主腿式机器人导航方法。我们设计了一种少样本学习分类器，仅需数百张RGB图像即可训练，用于区分可导航穿越的植物与必须绕行的植物。通过融合植被分类结果与二维激光雷达扫描数据，本方法构建了植被感知的通行性代价地图——该地图对柔韧性障碍物与非柔韧性障碍物分别赋予较低与较高的通行代价。代价地图构建过程特别处理了植被误分类问题，从而降低导航过程中碰撞、卡滞与被困的风险。此外，针对机器人因密集柔韧性植被导致卡滞或物理被困的情形，我们提出了全向恢复行为机制。在波士顿动力Spot机器人平台上，我们于包含稀疏/密集高草丛、灌木、树木等真实非结构化环境中验证了该方法。相比现有方法，本方案成功率提升25-90%，卡滞率降低10-90%，误报率最高降低65%。