Dataset Information

PDMS/MWCNT-based tactile sensor array with coplanar electrodes for crosstalk suppression.

ABSTRACT: The severe crosstalk effect is widely present in tactile sensor arrays with a sandwich structure. Here we present a novel design for a resistive tactile sensor array with a coplanar electrode layer and isolated sensing elements, which were made from polydimethylsiloxane (PDMS) doped with multiwalled carbon nanotubes (MWCNTs) for crosstalk suppression. To optimize its properties, both mechanical and electrical properties of PDMS/MWCNT-sensing materials with different PDMS/MWCNT ratios were investigated. The experimental results demonstrate that a 4 wt% of MWCNTs to PDMS is optimal for the sensing materials. In addition, the pressure-sensitive layer consists of three microstructured layers (two aspectant PDMS/MWCNT-based films and one top PDMS-based film) that are bonded together. Because of this three-layer microstructure design, our proposed tactile sensor array shows sensitivity up to -1.10 kPa-1, a response time of 29 ms and reliability in detecting tiny pressures.

SUBMITTER: Wang L

PROVIDER: S-EPMC6444708 | biostudies-other | 2016

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

PDMS/MWCNT-based tactile sensor array with coplanar electrodes for crosstalk suppression.

Wang Luxian L Peng Huiling H Wang Xiaolin X Chen Xiang X Yang Chunsheng C Yang Bin B Liu Jingquan J

Microsystems & nanoengineering 20161219

The severe crosstalk effect is widely present in tactile sensor arrays with a sandwich structure. Here we present a novel design for a resistive tactile sensor array with a coplanar electrode layer and isolated sensing elements, which were made from polydimethylsiloxane (PDMS) doped with multiwalled carbon nanotubes (MWCNTs) for crosstalk suppression. To optimize its properties, both mechanical and electrical properties of PDMS/MWCNT-sensing materials with different PDMS/MWCNT ratios were invest ...[more]

PMID: 31057844

Similar Datasets

Project description:Flexible wearable devices have attracted wide attention in capacious fields because of their real-time and continuous monitoring of human information. The development of flexible sensors and corresponding integration with wearable devices is of great significance to build smart wearable devices. In this work, multi-walled carbon nanotube/polydimethylsiloxane-based (MWCNT/PDMS) resistive strain sensors and pressure sensors were developed to integrate a smart glove for human motion/perception detection. Firstly, MWCNT/PDMS conductive layers with excellent electrical and mechanical properties (resistivity of 2.897 KΩ · cm, elongation at break of 145%) were fabricated via a facile scraping-coating method. Then, a resistive strain sensor with a stable homogeneous structure was developed due to the similar physicochemical properties of the PDMS encapsulation layer and MWCNT/PDMS sensing layer. The resistance changes of the prepared strain sensor exhibited a great linear relationship with the strain. Moreover, it could output obvious repeatable dynamic response signals. It still had good cyclic stability and durability after 180° bending/restoring cycles and 40% stretching/releasing cycles. Secondly, MWCNT/PDMS layers with bioinspired spinous microstructures were formed by a simple sandpaper retransfer process and then assembled face-to-face into a resistive pressure sensor. The pressure sensor presented a linear relationship of relative resistance change and pressure in the range of 0-31.83 KPa with a sensitivity of 0.026 KPa-1, and a sensitivity of 2.769 × 10-4 KPa-1 over 32 KPa. Furthermore, it responded quickly and kept good cycle stability at 25.78 KPa dynamic loop over 2000 s. Finally, as parts of a wearable device, resistive strain sensors and a pressure sensor were then integrated into different areas of the glove. The cost-effective, multi-functional smart glove can recognize finger bending, gestures, and external mechanical stimuli, which holds great potential in the fields of medical healthcare, human-computer cooperation, and so on.

Project description:Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. This is in stark contrast to robotics. In this field, the relative lack of good real-world interaction models-along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods-has so far rendered haptic exploration a largely underdeveloped skill. In robot vision however, deep learning approaches and an abundance of available training data have triggered huge advances. In the present work, we connect recent advances in recurrent models of visual attention with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel architecture that learns a generative model of haptic exploration in a simulated three-dimensional environment. This environment contains a set of rigid static objects representing a selection of one-dimensional local shape features embedded in a 3D space: an edge, a flat and a convex surface. The proposed algorithm simultaneously optimizes main perception-action loop components: feature extraction, integration of features over time, and the control strategy, while continuously acquiring data online. Inspired by the Recurrent Attention Model, we formalize the target task of haptic object identification in a reinforcement learning framework and reward the learner in the case of success only. We perform a multi-module neural network training, including a feature extractor and a recurrent neural network module aiding pose control for storing and combining sequential sensory data. The resulting haptic meta-controller for the rigid 16 × 16 tactile sensor array moving in a physics-driven simulation environment, called the Haptic Attention Model, performs a sequence of haptic glances, and outputs corresponding force measurements. The resulting method has been successfully tested with four different objects. It achieved results close to 100% while performing object contour exploration that has been optimized for its own sensor morphology.

Dataset Information

PDMS/MWCNT-based tactile sensor array with coplanar electrodes for crosstalk suppression.

Publications

PDMS/MWCNT-based tactile sensor array with coplanar electrodes for crosstalk suppression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets