仅用自身体感信号，实现波士顿动力机器狗对地形的高精度分类。
Terrain Classification for the Spot Quadrupedal Mobile Robot Using Only Proprioceptive Sensing
- 利用100+个本体感知信号，通过降维提取关键特征
- 在野外测试中对三种地形分类准确率达97%
- 适合需自主避障的复杂地形移动机器人使用
四足移动机器人比轮式机器人能适应更广泛的地形类型，但在不同地形上的表现差异显著，易出现下陷和打滑等不良行为。为解决此问题，本文提出一种基于本体感知信号的地形分类方法，可生成可通行性地图，辅助机器人规划安全路径。该方法针对波士顿动力Spot机器人设计，利用其超过100个本体感知信号（如足部嵌入深度、受力、关节角度等），结合降维技术提取有效特征，并采用分类算法区分不同地形的可通行性。在典型野外测试中，该分类器对三种不同地形的识别准确率约为97%。
原文摘要 · Abstract (English)
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%
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