通过流固相互作用的触觉感知
Tactile Perception through Fluid-Solid Interaction
浏览论文内容
中文总结 AI 辅助
本文提出一种基于流固相互作用的无电子软体触觉传感器，利用流体压力模式编码触摸位置与力，结合机器学习实现精确感知，适用于水下等恶劣环境。
中文摘要 AI 辅助
软体触觉传感器通过增强的柔韧性和适应性提升了机器人触觉，但大多数现有设计依赖于嵌入式电子元件，这些元件易受干扰和环境限制。在本工作中，我们利用流固相互作用开发了一类软体触觉传感器，其在传感部位完全无需电子元件。该传感器由一个充满流体的弹性体通道组成，仅连接两个外部压力传感器。触摸弹性体表面的不同区域会推动粘性流体，产生不同的压力模式，这些模式编码了触摸位置和力。这些信号通过一个机器学习框架进行解码，该框架集成了特征提取、软聚类和自适应神经模糊推理，以实现精确的定位和力估计。我们通过在线性（一维）传感器中的单点触摸定位和力估计验证了这一概念，并通过使用空间填充曲线将通道布设在表面上的方式，将相同的传感原理扩展到二维触觉映射，同时保持相同的最简硬件设置。这种简单方法在传统电子传感器常常失效的环境中仍然有效，例如在水下或存在磁干扰的情况下。
英文摘要
Soft tactile sensors elevate robotic touch through enhanced flexibility and adaptability, yet most existing designs depend on embedded electronics that are susceptible to interference and environmental limitations. In this work, we leverage fluid-solid interactions to develop a class of soft tactile sensors that operate entirely without electronics at the sensing site. The sensor comprises a fluid-filled elastomeric channel connected to only two external pressure sensors. Touching different regions of the elastomeric surface displaces the viscous fluid, producing distinct pressure patterns that encode both touch position and force. These signals are decoded through a machine learning framework that integrates feature extraction, soft clustering, and adaptive neuro-fuzzy inference to achieve accurate localization and force estimation. We validate this concept through single-point touch localization and force estimation in a linear (1D) sensor and extend the same sensing principle to 2D tactile mapping by routing the channel across the surface using space-filling curves, while maintaining the same minimal hardware setup. This simple approach remains effective in environments where conventional electronic sensors often fail, such as underwater or in the presence of magnetic interference.
发表机构
- University of Southern Denmark（南丹麦大学）
- University of Twente（特文特大学）
- ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
