Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm

Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tact...

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Main Authors:	Chenlei Jiao, Binbin Lian, Zhe Wang, Yimin Song, Tao Sun
Format:	Article
Language:	English
Published:	SAGE Publishing 2020-10-01
Series:	International Journal of Advanced Robotic Systems
Online Access:	https://doi.org/10.1177/1729881420948727

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spelling	doaj-fce5a406c6bd4ac4b2c505a67f00e5b02020-11-25T03:35:32ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142020-10-011710.1177/1729881420948727Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithmChenlei JiaoBinbin LianZhe WangYimin SongTao SunObject recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.https://doi.org/10.1177/1729881420948727
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Chenlei Jiao Binbin Lian Zhe Wang Yimin Song Tao Sun
spellingShingle	Chenlei Jiao Binbin Lian Zhe Wang Yimin Song Tao Sun Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm International Journal of Advanced Robotic Systems
author_facet	Chenlei Jiao Binbin Lian Zhe Wang Yimin Song Tao Sun
author_sort	Chenlei Jiao
title	Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm
title_short	Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm
title_full	Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm
title_fullStr	Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm
title_full_unstemmed	Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm
title_sort	visual–tactile object recognition of a soft gripper based on faster region-based convolutional neural network and machining learning algorithm
publisher	SAGE Publishing
series	International Journal of Advanced Robotic Systems
issn	1729-8814
publishDate	2020-10-01
description	Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.
url	https://doi.org/10.1177/1729881420948727
work_keys_str_mv	AT chenleijiao visualtactileobjectrecognitionofasoftgripperbasedonfasterregionbasedconvolutionalneuralnetworkandmachininglearningalgorithm AT binbinlian visualtactileobjectrecognitionofasoftgripperbasedonfasterregionbasedconvolutionalneuralnetworkandmachininglearningalgorithm AT zhewang visualtactileobjectrecognitionofasoftgripperbasedonfasterregionbasedconvolutionalneuralnetworkandmachininglearningalgorithm AT yiminsong visualtactileobjectrecognitionofasoftgripperbasedonfasterregionbasedconvolutionalneuralnetworkandmachininglearningalgorithm AT taosun visualtactileobjectrecognitionofasoftgripperbasedonfasterregionbasedconvolutionalneuralnetworkandmachininglearningalgorithm
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Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm

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