用神经网络解决三维非均匀触觉皮肤的触点定位问题
A Machine Learning Approach to Contact Localization in Variable Density Three-Dimensional Tactile Artificial Skin
- 用全连接神经网络学习非均匀分布触觉传感器的触点位置
- 在半锥形3D表面实现5.7±3.0mm的定位精度
- 适合复杂形状与不规则传感器布局的触觉系统
触点定位是触觉感知装置感知环境的核心功能。现有方法多基于平面几何与均匀传感器分布假设，难以应用于具有可变密度传感阵列的三维表面。本文研究了嵌入互电容触觉传感器、以未知非均匀分布排列于半锥形三维结构上的仿生触觉皮肤的接触定位问题。采用全连接神经网络对嵌入式触觉传感器上的触点进行定位，所提出的在线模型达到5.7±3.0mm的定位误差。该研究为形状复杂且内部传感器分布不明确的触觉系统提供了一种通用且鲁棒的解决方案。
原文摘要 · Abstract (English)
Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry and uniform sensor distributions as a simplifying assumption, limiting their ability to be used on 3D surfaces with variable density sensing arrays. This paper studies contact localization on an artificial skin embedded with mutual capacitance tactile sensors, arranged non-uniformly in an unknown distribution along a semi-conical 3D geometry. A fully connected neural network is trained to localize the touching points on the embedded tactile sensors. The studied online model achieves a localization error of $5.7 \pm 3.0$ mm. This research contributes a versatile tool and robust solution for contact localization that is ambiguous in shape and internal sensor distribution.
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