Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/11/6/583 |
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| author | Weiting Liu Binpeng Zhan Chunxin Gu Ping Yu Guoshi Zhang Xin Fu Christian Cipriani Liang Hu |
| author_facet | Weiting Liu Binpeng Zhan Chunxin Gu Ping Yu Guoshi Zhang Xin Fu Christian Cipriani Liang Hu |
| author_sort | Weiting Liu |
| collection | DOAJ |
| description | 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/cm<sup>2</sup>, 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. |
| first_indexed | 2024-03-10T19:15:02Z |
| format | Article |
| id | doaj.art-6d2986400ee042caa285aa1b9209e3de |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T19:15:02Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-6d2986400ee042caa285aa1b9209e3de2023-11-20T03:28:45ZengMDPI AGMicromachines2072-666X2020-06-0111658310.3390/mi11060583Discrimination of Object Curvature Based on a Sparse Tactile Sensor ArrayWeiting Liu0Binpeng Zhan1Chunxin Gu2Ping Yu3Guoshi Zhang4Xin Fu5Christian Cipriani6Liang Hu7State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, ChinaThe Biorobotic Institute, Scuola Universitaria Superiore Pisa, 56025 Pisa, ItalyState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, ChinaObject 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/cm<sup>2</sup>, 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.https://www.mdpi.com/2072-666X/11/6/583sparse tactile sensor arraymachine learningneural networkdiscrimination of curvaturecompliant contact |
| spellingShingle | Weiting Liu Binpeng Zhan Chunxin Gu Ping Yu Guoshi Zhang Xin Fu Christian Cipriani Liang Hu Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array Micromachines sparse tactile sensor array machine learning neural network discrimination of curvature compliant contact |
| title | Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array |
| title_full | Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array |
| title_fullStr | Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array |
| title_full_unstemmed | Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array |
| title_short | Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array |
| title_sort | discrimination of object curvature based on a sparse tactile sensor array |
| topic | sparse tactile sensor array machine learning neural network discrimination of curvature compliant contact |
| url | https://www.mdpi.com/2072-666X/11/6/583 |
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