Deep Neural Network Based Electrical Impedance Tomographic Sensing Methodology for Large-Area Robotic Tactile Sensing

Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challeng...

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Veröffentlicht in:IEEE transactions on robotics Jg. 37; H. 5; S. 1570 - 1583
Hauptverfasser: Park, Hyunkyu, Park, Kyungseo, Mo, Sangwoo, Kim, Jung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial intelligence (AI) based methods
Artificial neural networks
Computational efficiency
Conductivity
deep learning in robotics and automation
Discrimination
Electrical impedance
Electrodes
force and tactile sensing
Hardness tests
Image reconstruction
Indentation
Neural networks
Reconstruction
Robot sensing systems
Sensitivity
Sensors
Spatial resolution
Tactile sensors (robotics)
Tomography
Touch
Voltage measurement
ISSN:1552-3098, 1941-0468
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Zusammenfassung:Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2021.3060342