English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Tactile sensing is essential to the human perception system, so as to robot.In this paper, we develop a novel optical-based tactile sensor "FingerVision"with effective signal processing algorithms. This sensor is composed of softskin with embedded marker array bonded to rigid frame, and a web camera with afisheye lens. While being excited with contact force, the camera tracks themovements of markers and deformation field is obtained. Compared to existingtactile sensors, our sensor features compact footprint, high resolution, andease of fabrication. Besides, utilizing the deformation field estimation, wepropose a slip classification framework based on convolution Long Short TermMemory (convolutional LSTM) networks. The data collection process takesadvantage of the human sense of slip, during which human hand holds 12 dailyobjects, interacts with sensor skin and labels data with a slip or non-slipidentity based on human feeling of slip. Our slip classification frameworkperforms high accuracy of 97.62% on the test dataset. It is expected to becapable of enhancing the stability of robot grasping significantly, leading tobetter contact force control, finer object interaction and more active sensingmanipulation.

FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network