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.

翻译：能够检测多个空间分布刺激的分布式传感器阵列，被视为实现外部感知和本体感知软体机器人的关键要素。本文对先前提出的解耦局部压痕压力与位置测量（使其与全局变形分离）的方法进行了拓展，利用柔性电容式电子皮肤所受的整体拉伸变形。我们采用机器学习方法解耦并预测这些高度耦合的变形刺激，通过从柔性传感器电子皮肤采集数据，并将其输入由线性回归器、高斯过程回归器、支持向量机和随机森林分类器组成的机器学习系统，分别实现拉伸、力、检测和定位功能。此外，我们还研究了同时施加两个力时，定位和力分布所受的影响。通过适当选择机器学习技术辅助的柔性传感器阵列，可为电子皮肤在软体机器人中解码多模态刺激铺平道路。