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Soft Robotic Finger Embedded with Visual Sensor for Bending Perception

Published online by Cambridge University Press:  17 June 2020

Shixin Zhang ,
Jianhua Shan ,
Bin Fang Open the ORCID record for Bin Fang [Opens in a new window]  and
Fuchun Sun
Shixin Zhang
Affiliation:
College of Mechanical Engineering, Anhui University of Technology, Maanshan 243002, China E-mails: [email protected], [email protected]
Jianhua Shan
Affiliation:
College of Mechanical Engineering, Anhui University of Technology, Maanshan 243002, China E-mails: [email protected], [email protected]
Bin Fang*
Affiliation:
Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
Fuchun Sun*
Affiliation:
Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
*
*Corresponding author. E-mails: [email protected]; [email protected]
*Corresponding author. E-mails: [email protected]; [email protected]
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Summary

The various vision-based tactile sensors have been developed for robotic perception in recent years. In this paper, the novel soft robotic finger embedded with the visual sensor is proposed for perception. It consists of a colored soft inner chamber, an outer structure, and an endoscope camera. The bending perception algorithm based on image preprocessing and deep learning is proposed. The boundary of color regions and the position of marker dots are extracted from the inner chamber image and label image, respectively. Then the convolutional neural network with multi-task learning is trained to obtain bending states of the finger. Finally, the experiments are implemented to verify the effectiveness of the proposed method.

Keywords

Soft robotic fingerVisual sensorMulti-task learningConvolutional neural network
Type
Articles
Information
Robotica , Volume 39 , Issue 3 , March 2021 , pp. 378 - 390
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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Footnotes

Shixin Zhang and Jianhua Shan contributed equally to this work.

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