PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensing
Vision-based tactile sensors (VBTS) have become ubiquitous in robotics for contact and touch measurements due to their minimal instrumentation costs and exceptional high-resolution feedback. However, in the literature, VBTS often suffers from the requirement of extensive calibration and is limited t...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525003193 |
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| author | Abdullah Solayman Hussain Sajwani Oussama Abdul Hay Rui Chang Laith AbuAssi Abdulla Ayyad Yahya Zweiri Yarjan Abdul Samad |
| author_facet | Abdullah Solayman Hussain Sajwani Oussama Abdul Hay Rui Chang Laith AbuAssi Abdulla Ayyad Yahya Zweiri Yarjan Abdul Samad |
| author_sort | Abdullah Solayman |
| collection | DOAJ |
| description | Vision-based tactile sensors (VBTS) have become ubiquitous in robotics for contact and touch measurements due to their minimal instrumentation costs and exceptional high-resolution feedback. However, in the literature, VBTS often suffers from the requirement of extensive calibration and is limited to a specific range of forces. Recent advances in flexible, sensitive, and robust sensors have presented high potential for excellent use cases in robotics applications. The integration of graphene in piezoresistive sensors opened new horizons for such applications. This study presents PiezoSight, an encapsulated graphene-soaked textile in a stretchable elastomeric structure integrated with a VBTS. Piezosight overcomes VBTS weaknesses so that from the piezoresistive graphene textile, PiezoSight can detect a wide range of forces for robotic perception applications, varying from tiny forces for a soft touch and slip detection starting at 0.01 N up to an elevated force of 8 N. From VBTS, PiezoSight can infer high-resolution information, such as the direction of the contact measurements. Piezosight can sustain 10000 cycles with different compression rates at a strain of 40% with sensitives at 0.09 kPa-1. A machine learning LSTM model was used to train the data for different designs for using such sensors in unsupervised environments. |
| format | Article |
| id | doaj-art-747ab1024a8f408db526fc3f9e8a22f5 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-747ab1024a8f408db526fc3f9e8a22f52025-08-20T03:55:22ZengElsevierMaterials & Design0264-12752025-05-0125311389910.1016/j.matdes.2025.113899PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensingAbdullah Solayman0Hussain Sajwani1Oussama Abdul Hay2Rui Chang3Laith AbuAssi4Abdulla Ayyad5Yahya Zweiri6Yarjan Abdul Samad7Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Aerospace Engineering, Khalifa University, Abu Dhabi, United Arab Emirates; Cambridge Graphene Center, University of Cambridge, Cambridge, CB3 0FA, UK; Corresponding author at: Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.Vision-based tactile sensors (VBTS) have become ubiquitous in robotics for contact and touch measurements due to their minimal instrumentation costs and exceptional high-resolution feedback. However, in the literature, VBTS often suffers from the requirement of extensive calibration and is limited to a specific range of forces. Recent advances in flexible, sensitive, and robust sensors have presented high potential for excellent use cases in robotics applications. The integration of graphene in piezoresistive sensors opened new horizons for such applications. This study presents PiezoSight, an encapsulated graphene-soaked textile in a stretchable elastomeric structure integrated with a VBTS. Piezosight overcomes VBTS weaknesses so that from the piezoresistive graphene textile, PiezoSight can detect a wide range of forces for robotic perception applications, varying from tiny forces for a soft touch and slip detection starting at 0.01 N up to an elevated force of 8 N. From VBTS, PiezoSight can infer high-resolution information, such as the direction of the contact measurements. Piezosight can sustain 10000 cycles with different compression rates at a strain of 40% with sensitives at 0.09 kPa-1. A machine learning LSTM model was used to train the data for different designs for using such sensors in unsupervised environments.http://www.sciencedirect.com/science/article/pii/S0264127525003193GrapheneVision-based tactile sensingPiezoresistive sensingRobotic manipulation |
| spellingShingle | Abdullah Solayman Hussain Sajwani Oussama Abdul Hay Rui Chang Laith AbuAssi Abdulla Ayyad Yahya Zweiri Yarjan Abdul Samad PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensing Materials & Design Graphene Vision-based tactile sensing Piezoresistive sensing Robotic manipulation |
| title | PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensing |
| title_full | PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensing |
| title_fullStr | PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensing |
| title_full_unstemmed | PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensing |
| title_short | PiezoSight: Coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo-vision hybrid sensing |
| title_sort | piezosight coupling vision based tactile sensor neural network processing and piezoresistive stimulation for enhanced piezo vision hybrid sensing |
| topic | Graphene Vision-based tactile sensing Piezoresistive sensing Robotic manipulation |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525003193 |
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