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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2020-10-01
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Series: | International Journal of Advanced Robotic Systems |
Online Access: | https://doi.org/10.1177/1729881420948727 |
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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 |
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1724553880243535872 |