Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots
For terrain recognition needs during vehicle driving, this paper carries out terrain classification research based on vibration and image information. Twenty time-domain features and eight frequency-domain features of vibration signals that are highly correlated with terrain are selected, and princi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/8/214 |
| _version_ | 1797583013395038208 |
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| author | Hui Wang En Lu Xin Zhao Jialin Xue |
| author_facet | Hui Wang En Lu Xin Zhao Jialin Xue |
| author_sort | Hui Wang |
| collection | DOAJ |
| description | For terrain recognition needs during vehicle driving, this paper carries out terrain classification research based on vibration and image information. Twenty time-domain features and eight frequency-domain features of vibration signals that are highly correlated with terrain are selected, and principal component analysis (PCA) is used to reduce the dimensionality of the time-domain and frequency-domain features and retain the main information. Meanwhile, the texture features of the terrain images are extracted using the gray-level co-occurrence matrix (GLCM) technique, and the feature information of the vibration and images are fused in the feature layer. Then, the improved weighted K-nearest neighbor (WKNN) algorithm is used to achieve the terrain classification during the travel process of tracked robots. Finally, the experimental results verify that the proposed method improves the terrain classification accuracy of the tracked robot and provides a basis for improving the stable autonomous driving of tracked vehicles. |
| first_indexed | 2024-03-10T23:29:48Z |
| format | Article |
| id | doaj.art-80637e5af5eb4d7f9c3f1618246c1b25 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T23:29:48Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-80637e5af5eb4d7f9c3f1618246c1b252023-11-19T03:24:19ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-08-0114821410.3390/wevj14080214Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked RobotsHui Wang0En Lu1Xin Zhao2Jialin Xue3Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, ChinaKey Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, ChinaKey Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, ChinaKey Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, ChinaFor terrain recognition needs during vehicle driving, this paper carries out terrain classification research based on vibration and image information. Twenty time-domain features and eight frequency-domain features of vibration signals that are highly correlated with terrain are selected, and principal component analysis (PCA) is used to reduce the dimensionality of the time-domain and frequency-domain features and retain the main information. Meanwhile, the texture features of the terrain images are extracted using the gray-level co-occurrence matrix (GLCM) technique, and the feature information of the vibration and images are fused in the feature layer. Then, the improved weighted K-nearest neighbor (WKNN) algorithm is used to achieve the terrain classification during the travel process of tracked robots. Finally, the experimental results verify that the proposed method improves the terrain classification accuracy of the tracked robot and provides a basis for improving the stable autonomous driving of tracked vehicles.https://www.mdpi.com/2032-6653/14/8/214tracked robotterrain classificationdata fusionPCAWKNN |
| spellingShingle | Hui Wang En Lu Xin Zhao Jialin Xue Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots World Electric Vehicle Journal tracked robot terrain classification data fusion PCA WKNN |
| title | Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots |
| title_full | Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots |
| title_fullStr | Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots |
| title_full_unstemmed | Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots |
| title_short | Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots |
| title_sort | vibration and image texture data fusion based terrain classification using wknn for tracked robots |
| topic | tracked robot terrain classification data fusion PCA WKNN |
| url | https://www.mdpi.com/2032-6653/14/8/214 |
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