用轮辐压电传感器+物理储层计算，实时识别地形
Physical Reservoir Computing in Hook-Shaped Rover Wheel Spokes for Real-Time Terrain Identification
- 将轮子振动转为高维特征，用物理系统做计算
- 3个传感器在轮辐上实现90%地形识别准确率
- 适合低功耗、实时性要求高的火星车等机器人
在未知环境中有效探测地形对安全高效机器人导航至关重要。传统方法依赖计算量大的数据处理，需要大量机载算力，限制了巡视器的实时性能。本文提出一种新方法：将压电传感器嵌入巡视器轮辐，结合物理储层计算，实现实时地形识别。利用轮子运动产生的振动，将地形引起的动态信号转化为高维特征，用于机器学习分类。实验表明，在轮辐上合理布置三个传感器，可达到90%的分类准确率，验证了该方法的可行性与精度。结果还显示，系统不仅能有效区分已知地形，还能通过分析与已学类别的相似性识别未知地形。该方法提供了一种鲁棒、低功耗的实时地形分类与粗糙度估计框架，显著提升了巡视器在非结构化环境中的自主性与适应能力。
原文摘要 · Abstract (English)
Effective terrain detection in unknown environments is crucial for safe and efficient robotic navigation. Traditional methods often rely on computationally intensive data processing, requiring extensive onboard computational capacity and limiting real-time performance for rovers. This study presents a novel approach that combines physical reservoir computing with piezoelectric sensors embedded in rover wheel spokes for real-time terrain identification. By leveraging wheel dynamics, terrain-induced vibrations are transformed into high-dimensional features for machine learning-based classification. Experimental results show that strategically placing three sensors on the wheel spokes achieves 90$\%$ classification accuracy, which demonstrates the accuracy and feasibility of the proposed method. The experiment results also showed that the system can effectively distinguish known terrains and identify unknown terrains by analyzing their similarity to learned categories. This method provides a robust, low-power framework for real-time terrain classification and roughness estimation in unstructured environments, enhancing rover autonomy and adaptability.
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