Robotic-Based Touch Panel Test System Using Pattern Recognition Methods
In this study, pattern recognition methods are applied to a five-degrees-of-freedom robot arm that can key in words on a touch screen for an automatic smartphone test. The proposed system can recognize Chinese characters and Mandarin phonetic symbols. The mechanical arm is able to perform correspond...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8339 |
| _version_ | 1797546922924310528 |
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| author | Chia-Chi Lu Jih-Gau Juang |
| author_facet | Chia-Chi Lu Jih-Gau Juang |
| author_sort | Chia-Chi Lu |
| collection | DOAJ |
| description | In this study, pattern recognition methods are applied to a five-degrees-of-freedom robot arm that can key in words on a touch screen for an automatic smartphone test. The proposed system can recognize Chinese characters and Mandarin phonetic symbols. The mechanical arm is able to perform corresponding movements and edit words on the screen. Pattern matching is based on the Red-Green-Blue (RGB) color space and is transformed to binary images for higher correct rate and geometric matching. A web camera is utilized to capture patterns on the tested smartphone screen. The proposed control scheme uses a support vector machine with a histogram of oriented gradient classifier to recognize Chinese Mandarin phonetic symbols and provide correct coordinates during the control process. The control scheme also calculates joint angles of the robot arm during the movement using the Denavit–Hartenberg parameters (D-H) model and fuzzy logic system. Fuzzy theory is applied to use the position error between the robot arm and target location then resend the command to adjust the arm’s position. From the experiments, the proposed control scheme can control the robot to press desired buttons on the tested smartphone. For Chinese Mandarin phonetic symbols, recognition accuracy of the test system can reach 90 percent. |
| first_indexed | 2024-03-10T14:36:17Z |
| format | Article |
| id | doaj.art-c9e645f17118475491b76fa4348a1123 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:36:17Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c9e645f17118475491b76fa4348a11232023-11-20T22:09:34ZengMDPI AGApplied Sciences2076-34172020-11-011023833910.3390/app10238339Robotic-Based Touch Panel Test System Using Pattern Recognition MethodsChia-Chi Lu0Jih-Gau Juang1Wistron Corporation, 158 Xingshan Road, Taipei 11469, TaiwanDepartment of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung 20224, TaiwanIn this study, pattern recognition methods are applied to a five-degrees-of-freedom robot arm that can key in words on a touch screen for an automatic smartphone test. The proposed system can recognize Chinese characters and Mandarin phonetic symbols. The mechanical arm is able to perform corresponding movements and edit words on the screen. Pattern matching is based on the Red-Green-Blue (RGB) color space and is transformed to binary images for higher correct rate and geometric matching. A web camera is utilized to capture patterns on the tested smartphone screen. The proposed control scheme uses a support vector machine with a histogram of oriented gradient classifier to recognize Chinese Mandarin phonetic symbols and provide correct coordinates during the control process. The control scheme also calculates joint angles of the robot arm during the movement using the Denavit–Hartenberg parameters (D-H) model and fuzzy logic system. Fuzzy theory is applied to use the position error between the robot arm and target location then resend the command to adjust the arm’s position. From the experiments, the proposed control scheme can control the robot to press desired buttons on the tested smartphone. For Chinese Mandarin phonetic symbols, recognition accuracy of the test system can reach 90 percent.https://www.mdpi.com/2076-3417/10/23/8339support vector machinehistogram of oriented gradientfuzzy controlimage processgeometric matching |
| spellingShingle | Chia-Chi Lu Jih-Gau Juang Robotic-Based Touch Panel Test System Using Pattern Recognition Methods Applied Sciences support vector machine histogram of oriented gradient fuzzy control image process geometric matching |
| title | Robotic-Based Touch Panel Test System Using Pattern Recognition Methods |
| title_full | Robotic-Based Touch Panel Test System Using Pattern Recognition Methods |
| title_fullStr | Robotic-Based Touch Panel Test System Using Pattern Recognition Methods |
| title_full_unstemmed | Robotic-Based Touch Panel Test System Using Pattern Recognition Methods |
| title_short | Robotic-Based Touch Panel Test System Using Pattern Recognition Methods |
| title_sort | robotic based touch panel test system using pattern recognition methods |
| topic | support vector machine histogram of oriented gradient fuzzy control image process geometric matching |
| url | https://www.mdpi.com/2076-3417/10/23/8339 |
| work_keys_str_mv | AT chiachilu roboticbasedtouchpaneltestsystemusingpatternrecognitionmethods AT jihgaujuang roboticbasedtouchpaneltestsystemusingpatternrecognitionmethods |