学习软电容电子皮肤的去耦多点触控力估计、定位和拉伸
Learning Decoupled Multi-touch Force Estimation, Localization and Stretch for Soft Capacitive E-skin
摘要：
分布式传感器阵列能够检测多个空间分布的刺激被认为是一个重要的元素，在实现外感受和本体感受软体机器人。本文扩展了先前提出的想法解耦的压力和局部压痕的位置从全球变形的测量，使用的整体拉伸所经历的软电容电子皮肤。我们采用机器学习方法来解耦和预测这些高度耦合的变形刺激，从软传感器电子皮肤收集数据，然后将其馈送到机器学习系统，该系统包括线性回归器、高斯过程回归器、SVM和随机森林分类器，分别用于拉伸、力、检测和定位。我们还研究了当两个力同时施加时，局部化和力是如何受到影响的。在适当选择的机器学习技术的辅助下，软传感器阵列可以为能够破译软体机器人中的多模态刺激的电子皮肤铺平道路。
英文摘要：
Distributed sensor arrays capable of detecting multiple spatially distributed stimuli are considered an important element in the realisation of exteroceptive and proprioceptive soft robots. This paper expands upon the previously presented idea of decoupling the measurements of pressure and location of a local indentation from global deformation, using the overall stretch experienced by a soft capacitive e-skin. We employed machine learning methods to decouple and predict these highly coupled deformation stimuli, collecting data from a soft sensor e-skin which was then fed to a machine learning system comprising of linear regressor, gaussian process regressor, SVM and random forest classifier for stretch, force, detection and localisation respectively. We also studied how the localisation and forces are affected when two forces are applied simultaneously. Soft sensor arrays aided by appropriately chosen machine learning techniques can pave the way to e-skins capable of deciphering multi-modal stimuli in soft robots.
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。
