フック型ローバーホイールスポークを用いた物理リザバーコンピューティングによるリアルタイム地形識別
Physical Reservoir Computing in Hook-Shaped Rover Wheel Spokes for Real-Time Terrain Identification
ローバーのホイールスポークに圧電センサを埋め込み、物理リザバーコンピューティングを活用して振動から地形をリアルタイムに識別する手法を提案し、90%の分類精度を達成した。
著者: Xiao Jin, Zihan Wang, Zhenhua Yu, Changrak Choi, Kalind Carpenter, Thrishantha Nanayakkara
分類: cs.RO
原文アブストラクト
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.
