Quadrupedal mobile robots can traverse a wider range of terrain types than their wheeled counterparts but do not perform the same on all terrain types. These robots are prone to undesirable behaviours like sinking and slipping on challenging terrains. To combat this issue, we propose a terrain classifier that provides information on terrain type that can be used in robotic systems to create a traversability map to plan safer paths for the robot to navigate. The work presented here is a terrain classifier developed for a Boston Dynamics Spot robot. Spot provides over 100 measured proprioceptive signals describing the motions of the robot and its four legs (e.g., foot penetration, forces, joint angles, etc.). The developed terrain classifier combines dimensionality reduction techniques to extract relevant information from the signals and then applies a classification technique to differentiate terrain based on traversability. In representative field testing, the resulting terrain classifier was able to identify three different terrain types with an accuracy of approximately 97%

翻译：四足移动机器人相比轮式机器人能够穿越更广泛的地形类型，但在不同地形上的表现并不相同。这些机器人在挑战性地形上容易出现下沉和打滑等不良行为。为解决这一问题，我们提出了一种地形分类器，可提供地形类型信息，该信息可用于机器人系统创建可通行性地图，从而为机器人规划更安全的导航路径。本文提出的地形分类器专为波士顿动力Spot机器人开发。Spot提供超过100个测量的本体感知信号，描述机器人及其四条腿的运动（例如足端穿透深度、受力、关节角度等）。所开发的地形分类器结合降维技术从信号中提取相关信息，然后应用分类技术根据可通行性区分地形。在代表性现场测试中，该地形分类器能够以约97%的准确率识别三种不同的地形类型。