Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network
With the development of technology, mobile robots are increasingly deployed in real-world environments. To enable robots to work safely in a variety of terrain environments, we proposed a ground-type detection method based on the Hilbert–Huang transform (HHT) and attention-based spatiotemporal coupl...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2023/4721508 |
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| _version_ | 1849307055674884096 |
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| author | Jixiang Niu Han Li Zhenxiong Liu Wei Liu Hejun Xu |
| author_facet | Jixiang Niu Han Li Zhenxiong Liu Wei Liu Hejun Xu |
| author_sort | Jixiang Niu |
| collection | DOAJ |
| description | With the development of technology, mobile robots are increasingly deployed in real-world environments. To enable robots to work safely in a variety of terrain environments, we proposed a ground-type detection method based on the Hilbert–Huang transform (HHT) and attention-based spatiotemporal coupled network. Taking a dataset containing multiple sets of robot signals from a Kaggle competition as an example; we use the proposed method to classify the signals and thus achieve a terrain classification of the robot’s location. Firstly, the signal data were processed using the discrete wavelet transform for noise reduction, and all channels in the dataset were ranked by importance using the permutation importance method. Next, the instantaneous frequencies of the two most important channels were extracted using the HHT and added to the original dataset to expand the feature dimension. Then the features in the expanded dataset were extracted by the convolutional neural network, long short-term memory, and attention module. Afterward, the fully extracted features were passed into the fully connected layer for classification, and an average classification accuracy of 83.14% was obtained. The effectiveness of each part in our method was demonstrated using ablation experiments. Finally, we compared our method with some common methods in the field and found that our method obtained the highest classification accuracy, proving the superiority of the proposed method. |
| format | Article |
| id | doaj-art-2022a9f90e3f441c96c2fa0e6b86e48f |
| institution | Kabale University |
| issn | 1751-9683 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-2022a9f90e3f441c96c2fa0e6b86e48f2025-08-20T03:54:52ZengWileyIET Signal Processing1751-96832023-01-01202310.1049/2023/4721508Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled NetworkJixiang Niu0Han Li1Zhenxiong Liu2Wei Liu3Hejun Xu4North China University of TechnologyNorth China University of TechnologyNorth China University of TechnologyNorth China University of TechnologyDepartment of Architecture and Civil EngineeringWith the development of technology, mobile robots are increasingly deployed in real-world environments. To enable robots to work safely in a variety of terrain environments, we proposed a ground-type detection method based on the Hilbert–Huang transform (HHT) and attention-based spatiotemporal coupled network. Taking a dataset containing multiple sets of robot signals from a Kaggle competition as an example; we use the proposed method to classify the signals and thus achieve a terrain classification of the robot’s location. Firstly, the signal data were processed using the discrete wavelet transform for noise reduction, and all channels in the dataset were ranked by importance using the permutation importance method. Next, the instantaneous frequencies of the two most important channels were extracted using the HHT and added to the original dataset to expand the feature dimension. Then the features in the expanded dataset were extracted by the convolutional neural network, long short-term memory, and attention module. Afterward, the fully extracted features were passed into the fully connected layer for classification, and an average classification accuracy of 83.14% was obtained. The effectiveness of each part in our method was demonstrated using ablation experiments. Finally, we compared our method with some common methods in the field and found that our method obtained the highest classification accuracy, proving the superiority of the proposed method.http://dx.doi.org/10.1049/2023/4721508 |
| spellingShingle | Jixiang Niu Han Li Zhenxiong Liu Wei Liu Hejun Xu Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network IET Signal Processing |
| title | Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network |
| title_full | Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network |
| title_fullStr | Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network |
| title_full_unstemmed | Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network |
| title_short | Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network |
| title_sort | robot ground media classification based on hilbert huang transform and attention based spatiotemporal coupled network |
| url | http://dx.doi.org/10.1049/2023/4721508 |
| work_keys_str_mv | AT jixiangniu robotgroundmediaclassificationbasedonhilberthuangtransformandattentionbasedspatiotemporalcouplednetwork AT hanli robotgroundmediaclassificationbasedonhilberthuangtransformandattentionbasedspatiotemporalcouplednetwork AT zhenxiongliu robotgroundmediaclassificationbasedonhilberthuangtransformandattentionbasedspatiotemporalcouplednetwork AT weiliu robotgroundmediaclassificationbasedonhilberthuangtransformandattentionbasedspatiotemporalcouplednetwork AT hejunxu robotgroundmediaclassificationbasedonhilberthuangtransformandattentionbasedspatiotemporalcouplednetwork |