ソフト容量性電子皮膚における多点力推定・位置推定・伸縮の分離学習
Learning Decoupled Multi-touch Force Estimation, Localization and Stretch for Soft Capacitive E-skin
ソフト容量性電子皮膚のデータに機械学習を適用し、伸縮・力・接触検出・位置を分離して予測する手法を提案し、2点同時加圧時の影響も調査した。
著者: Abu Bakar Dawood, Claudio Coppola, Kaspar Althoefer
分類: cs.RO
原文アブストラクト
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.
