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Open AccessArticle

Human-Touch-Inspired Material Recognition for Robotic Tactile Sensing

by Yu Xie 1,2, Chuhao Chen 1,3, Dezhi Wu 1, Wenming Xi 1 and Houde Liu 2,*
1
School of Aerospace Engineering, Xiamen University, Xiamen 361102, China
2
Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
3
Shenzhen Research Institute of Xiamen University, Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2537; https://doi.org/10.3390/app9122537
Received: 29 April 2019 / Accepted: 12 June 2019 / Published: 21 June 2019
(This article belongs to the Special Issue Human Friendly Robotics)
This paper proposes a novel material recognition method for robotic tactile sensing. The method is composed of two steps. Firstly, a human-touch-inspired short-duration (1 s) slide action is conducted by the robot to obtain the tactile data. Then, the tactile data is processed with a machine learning algorithm, where 11 bioinspired features were designed to imitate the mechanical stimuli towards the four main types of tactile receptors in the skin. In this paper, a material database consisting of 144,000 tactile images is used to train seven classifiers, and the most accurate classifier is selected to recognize 12 household objects according to their properties and materials. In the property recognition, the materials are classified into 4 categories according to their compliance and texture, and the best accuracy reaches 96% in 36 ms. In the material recognition, the specific materials are recognized, and the best accuracy reaches 90% in 37 ms. The results verify the effectiveness of the proposed method. View Full-Text
Keywords: material recognition; tactile sensor; short-duration exploration; wavelet transformation; multi-layer perceptron; human touch material recognition; tactile sensor; short-duration exploration; wavelet transformation; multi-layer perceptron; human touch
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MDPI and ACS Style

Xie, Y.; Chen, C.; Wu, D.; Xi, W.; Liu, H. Human-Touch-Inspired Material Recognition for Robotic Tactile Sensing. Appl. Sci. 2019, 9, 2537.

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