contact detection
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2021 ◽  
Vol 140 ◽  
pp. 104430
Author(s):  
Mojtaba Mousakhani ◽  
Klaus Thoeni ◽  
Stephen Fityus ◽  
Anna Giacomini

Author(s):  
Yuanjun Chen ◽  
Lizhong Jiang ◽  
Changqing Li ◽  
Jing Li ◽  
Ping Shao ◽  
...  

2021 ◽  
Author(s):  
Xiaojie Wang

In this paper, we presented a multi-frame constrained block sparse Bayesian learning (MFC-BSBL) reconstruction algorithm to tackle the challenge of poor-quality reconstruction images in electrical impedance tomography (EIT) for tactile sensing. The fundamental idea of MFC-BSBL is to explore the sparsity, intra-frame correlation, and inter-frame correlation of impedance distributions by extending the Bayesian inference framework. To verify the proposed algorithm, we conducted numerical simulations for different cases to identify one, multiple, round, and square targets. The simulation results demonstrated that this method can effectively detect the target positions and shapes by reducing artifacts and noise in the reconstructed images. To demonstrate the application of this approach to real EIT-based tactile sensing, we conducted real-contact detection experiments using the EIT tactile sensor system. Compared with traditional methods, the tactile sensor system using the MFC-BSBL algorithm can achieve accurate contact detection and significantly reduce artifacts and noise.


2021 ◽  
Author(s):  
Xiaojie Wang

In this paper, we presented a multi-frame constrained block sparse Bayesian learning (MFC-BSBL) reconstruction algorithm to tackle the challenge of poor-quality reconstruction images in electrical impedance tomography (EIT) for tactile sensing. The fundamental idea of MFC-BSBL is to explore the sparsity, intra-frame correlation, and inter-frame correlation of impedance distributions by extending the Bayesian inference framework. To verify the proposed algorithm, we conducted numerical simulations for different cases to identify one, multiple, round, and square targets. The simulation results demonstrated that this method can effectively detect the target positions and shapes by reducing artifacts and noise in the reconstructed images. To demonstrate the application of this approach to real EIT-based tactile sensing, we conducted real-contact detection experiments using the EIT tactile sensor system. Compared with traditional methods, the tactile sensor system using the MFC-BSBL algorithm can achieve accurate contact detection and significantly reduce artifacts and noise.


2021 ◽  
Author(s):  
Ayumi Amemiya ◽  
Aya Matsumura ◽  
Ryutaro Kase ◽  
Yasuhisa Sugasawa ◽  
Takashiro Minowa ◽  
...  

2021 ◽  
pp. 108269
Author(s):  
Danping Huang ◽  
Jianqiu Ye ◽  
Shaodong Yu ◽  
Ying Tian ◽  
Xiaomei Wen ◽  
...  

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