Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization
Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relativ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/4/1050 |
| _version_ | 1811301689566691328 |
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| author | Zhen Deng Yannick Jonetzko Liwei Zhang Jianwei Zhang |
| author_facet | Zhen Deng Yannick Jonetzko Liwei Zhang Jianwei Zhang |
| author_sort | Zhen Deng |
| collection | DOAJ |
| description | Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors. |
| first_indexed | 2024-04-13T07:14:27Z |
| format | Article |
| id | doaj.art-4cc042fd1b0b42e699272d7e94309f77 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:14:27Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4cc042fd1b0b42e699272d7e94309f772022-12-22T02:56:48ZengMDPI AGSensors1424-82202020-02-01204105010.3390/s20041050s20041050Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object StabilizationZhen Deng0Yannick Jonetzko1Liwei Zhang2Jianwei Zhang3School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaTAMS, Department of Informatics, University of Hamburg, D-22527 Hamburg, GermanySchool of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaTAMS, Department of Informatics, University of Hamburg, D-22527 Hamburg, GermanyGrasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.https://www.mdpi.com/1424-8220/20/4/1050grasping force controlforce estimationslip detectionmulti-fingered robotic hands |
| spellingShingle | Zhen Deng Yannick Jonetzko Liwei Zhang Jianwei Zhang Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization Sensors grasping force control force estimation slip detection multi-fingered robotic hands |
| title | Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization |
| title_full | Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization |
| title_fullStr | Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization |
| title_full_unstemmed | Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization |
| title_short | Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization |
| title_sort | grasping force control of multi fingered robotic hands through tactile sensing for object stabilization |
| topic | grasping force control force estimation slip detection multi-fingered robotic hands |
| url | https://www.mdpi.com/1424-8220/20/4/1050 |
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