初期滑移中时空特征检测的建模、仿真与应用
Modeling, Simulation, and Application of Spatio-Temporal Characteristics Detection in Incipient Slip
AI总结：
本文提出一种基于特征应变率极端事件与局部滑移状态关系的初期滑移时空建模与检测方法，适用于视觉触觉传感器，经仿真和实验验证，能准确描绘滑移并支持摩擦估计与自适应抓取。
摘要：
初期滑移检测为机器人抓取和操作任务提供了关键反馈。然而，在不同物体属性和复杂工作条件下保持其适应性仍然具有挑战性。本文强调了完全表征滑移时空特征的重要性，并提出了一种新的初期滑移建模与检测方法。基于对局部位移现象的分析，我们建立了特征应变率极端事件与局部滑移状态之间的关系。该方法能够检测粘滑区域的时空分布和动态变化。此外，所提出的方法可应用于应变分布传感设备，如基于视觉的触觉传感器。仿真和原型实验验证了该方法在不同接触条件下的有效性，包括不同的接触几何形状、摩擦系数和组合载荷。实验表明，该方法不仅准确可靠地描绘了初期滑移，而且有助于摩擦参数估计和自适应抓取控制。
英文摘要：
Incipient slip detection provides critical feedback for robotic grasping and manipulation tasks. However, maintaining its adaptability under diverse object properties and complex working conditions remains challenging. This article highlights the importance of completely representing spatio-temporal features of slip, and proposes a novel approach for incipient slip modeling and detection. Based on the analysis of localized displacement phenomenon, we establish the relationship between the characteristic strain rate extreme events and the local slip state. This approach enables the detection of both the spatial distribution and temporal dynamics of stick-slip regions. Also, the proposed method can be applied to strain distribution sensing devices, such as vision-based tactile sensors. Simulations and prototype experiments validated the effectiveness of this approach under varying contact conditions, including different contact geometries, friction coefficients, and combined loads. Experiments demonstrated that this method not only accurately and reliably delineates incipient slip, but also facilitates friction parameter estimation and adaptive grasping control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
