Abstract
In this paper, we design a vision-based tactile sensor which can capture the two-dimensional deformation images when stressed. The sensor mainly consists of a transparent elastomer, a \(4 \times 4\) or \(8 \times 8\) array of single layer markers on the surface of the elastomer and a camera which is used to capture the markers. According to Hooke’s law, we propose a scheme using the nonlinear fitting and activation function of neural network with three layers to fit stiffness coefficient of elastomer, then we can calculate the three-dimensional tactile information. Compared to the tactile sensor with two layers having markers of different colors, our method has the advantage of simpler manufacturing process. Besides, it does not need expensive camera and precise focusing process, which is necessary for DFD(Depth from defocus) algorithm to calculate depth displacement information of markers. Further more, our method adapts neural network to fit the stiffness coefficient of elastomer, making the parameters have physical significance, which accords with the physical properties of elastomer. The sensor can reconstruct three-dimensional tactile information in real time with a speed of about 30fps.
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Acknowledgment
The work was supported by the National Natural Science Foundation of China (Grant Nos. 91420302, 91520201).
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Yang, C., Sun, F., Fang, B., Li, L. (2017). A Stable and Efficient Vision-Based Tactile Sensor with Tactile Detection Using Neural Network. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_34
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DOI: https://doi.org/10.1007/978-981-10-5230-9_34
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