Next Article in Journal
Improved Atomization via a Mechanical Atomizer with Optimal Geometric Parameters and an Air-Assisted Component
Previous Article in Journal
Micromirror-Embedded Coverslip Assembly for Bidirectional Microscopic Imaging
Previous Article in Special Issue
Editorial of Special Issue “Tactile Sensing Technology and Systems”
Open AccessArticle

Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array

1
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, China
2
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
3
The Biorobotic Institute, Scuola Universitaria Superiore Pisa, 56025 Pisa, Italy
*
Author to whom correspondence should be addressed.
Micromachines 2020, 11(6), 583; https://doi.org/10.3390/mi11060583
Received: 9 May 2020 / Revised: 1 June 2020 / Accepted: 9 June 2020 / Published: 10 June 2020
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect. View Full-Text
Keywords: sparse tactile sensor array; machine learning; neural network; discrimination of curvature; compliant contact sparse tactile sensor array; machine learning; neural network; discrimination of curvature; compliant contact
Show Figures

Figure 1

MDPI and ACS Style

Liu, W.; Zhan, B.; Gu, C.; Yu, P.; Zhang, G.; Fu, X.; Cipriani, C.; Hu, L. Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array. Micromachines 2020, 11, 583.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop