Research on Operation Intention Based on Flexible Tactile Sensing Handle
In order to obtain and analyze the operator's intention comprehensively and accurately in human-robot interaction, an array-type flexible tactile sensor was designed. The sensor was encapsulated into a tactile handle to sense the grasping state of the human hand in real time. According to the a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9321478/ |
| _version_ | 1818668805712248832 |
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| author | Tiejun Li Kaiwen Zheng Jinyue Liu Xiaohui Jia Jianbin Feng |
| author_facet | Tiejun Li Kaiwen Zheng Jinyue Liu Xiaohui Jia Jianbin Feng |
| author_sort | Tiejun Li |
| collection | DOAJ |
| description | In order to obtain and analyze the operator's intention comprehensively and accurately in human-robot interaction, an array-type flexible tactile sensor was designed. The sensor was encapsulated into a tactile handle to sense the grasping state of the human hand in real time. According to the analysis of different operators' grasping posture and grasping habits, the grasping state was defined as 5 modes. Based on Harris feature point positioning and extraction, a method of grasping posture conversion was proposed to ensure the completeness and standard of the extracted grasping features. A set of Convolutional Neural Networks (CNN) suitable for the real-time classification of the grasping intention was built to distinguish the grasping state sensed by the handle in real time, accurately determine the operator's intention, and complete the interaction with the robot. Using a UR collaborative robot as the experimental platform and the haptic handle as the intent sensing device, the intent-behaviour mapping relationship was constructed to control the motion of the UR collaborative robot. The experimental results show that the classification accuracy of operation intention is as high as 97.87%. |
| first_indexed | 2024-12-17T06:42:10Z |
| format | Article |
| id | doaj.art-d229ddc70ab541c3ab019406aa824d28 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T06:42:10Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d229ddc70ab541c3ab019406aa824d282022-12-21T21:59:50ZengIEEEIEEE Access2169-35362021-01-019123621237310.1109/ACCESS.2021.30509919321478Research on Operation Intention Based on Flexible Tactile Sensing HandleTiejun Li0https://orcid.org/0000-0002-4147-446XKaiwen Zheng1https://orcid.org/0000-0002-9378-4953Jinyue Liu2https://orcid.org/0000-0003-4189-9027Xiaohui Jia3https://orcid.org/0000-0003-2092-8626Jianbin Feng4https://orcid.org/0000-0002-4743-0839School of Mechanical Engineering, Hebei University of Technology, Tianjin, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, ChinaIn order to obtain and analyze the operator's intention comprehensively and accurately in human-robot interaction, an array-type flexible tactile sensor was designed. The sensor was encapsulated into a tactile handle to sense the grasping state of the human hand in real time. According to the analysis of different operators' grasping posture and grasping habits, the grasping state was defined as 5 modes. Based on Harris feature point positioning and extraction, a method of grasping posture conversion was proposed to ensure the completeness and standard of the extracted grasping features. A set of Convolutional Neural Networks (CNN) suitable for the real-time classification of the grasping intention was built to distinguish the grasping state sensed by the handle in real time, accurately determine the operator's intention, and complete the interaction with the robot. Using a UR collaborative robot as the experimental platform and the haptic handle as the intent sensing device, the intent-behaviour mapping relationship was constructed to control the motion of the UR collaborative robot. The experimental results show that the classification accuracy of operation intention is as high as 97.87%.https://ieeexplore.ieee.org/document/9321478/Human-robot interactiontactile perceptionintention understandinggrasp recognition |
| spellingShingle | Tiejun Li Kaiwen Zheng Jinyue Liu Xiaohui Jia Jianbin Feng Research on Operation Intention Based on Flexible Tactile Sensing Handle IEEE Access Human-robot interaction tactile perception intention understanding grasp recognition |
| title | Research on Operation Intention Based on Flexible Tactile Sensing Handle |
| title_full | Research on Operation Intention Based on Flexible Tactile Sensing Handle |
| title_fullStr | Research on Operation Intention Based on Flexible Tactile Sensing Handle |
| title_full_unstemmed | Research on Operation Intention Based on Flexible Tactile Sensing Handle |
| title_short | Research on Operation Intention Based on Flexible Tactile Sensing Handle |
| title_sort | research on operation intention based on flexible tactile sensing handle |
| topic | Human-robot interaction tactile perception intention understanding grasp recognition |
| url | https://ieeexplore.ieee.org/document/9321478/ |
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