Contact Position Estimation Algorithm using Image-based Areal Touch Sensor based on Artificial Neural Network Prediction
| Autor: | Jongil Lee, Hyun Min Oh, Min Young Kim, Su Woong Lee, Bo Ram Cho, Ki Hoon Kwon |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science 010401 analytical chemistry Experimental data 020206 networking & telecommunications 02 engineering and technology 01 natural sciences 0104 chemical sciences Image (mathematics) Approximation error Contact position 0202 electrical engineering electronic engineering information engineering Algorithm Image based |
| Zdroj: | Journal of the Institute of Industrial Applications Engineers. 6:100-106 |
| ISSN: | 2187-8811 2188-1758 |
| DOI: | 10.12792/jiiae.6.100 |
| Popis: | In this paper, we propose an artificial neural network fitting model to estimate contact position an image-based areal touch sensor(IATS) with soft physical contact. First, the principle of the proposed artificial neural network fitting model for contact position estimation is described. Then, the structure of the IATS for verifying the algorithm is described. Second, an experiment was conducted to verify the model. Experimental data was obtained and analyzed for accuracy. Accuracy is estimated based on the relative error rate to show the accuracy between actual and estimated contact positions. As a result, the accuracy for each axis is as follows. The accuracy for the X-axis is 86.7% on average and the accuracy for the Y-axis is 96.5% on average. The depth accuracy is 94.9%. It is analyzed to solve various problems and it is expected that it will be possible to develop a sensor with accuracy similar to the actual contact position in the future. |
| Databáze: | OpenAIRE |
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