Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
The flow field is difficult to evaluate, and underwater robotics can only partly adapt to the submarine environment. However, fish can sense the complex underwater environment by their lateral line system. In order to reveal the fish flow sensing mechanism, a robust nonlinear signal estimation metho...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/16/7759 |
| _version_ | 1797524702547148800 |
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| author | Xinghua Lin Qing Qin Xiaoming Wang Junxia Zhang |
| author_facet | Xinghua Lin Qing Qin Xiaoming Wang Junxia Zhang |
| author_sort | Xinghua Lin |
| collection | DOAJ |
| description | The flow field is difficult to evaluate, and underwater robotics can only partly adapt to the submarine environment. However, fish can sense the complex underwater environment by their lateral line system. In order to reveal the fish flow sensing mechanism, a robust nonlinear signal estimation method based on the Volterra series model with the Kautz kernel function is provided, which is named KKF-VSM. The flow field signal around a square target is used as the original signal. The sinusoidal noise and the signal around a triangular obstacle are considered undesired signals, and the predicting performance of KKF-VSM is analyzed after introducing them locally in the original signals. Compared to the radial basis function neural network model (RBF-NNM), the advantages of KKF-VSM are not only its robustness but also its higher sensitivity to weak signals and its predicting accuracy. It is confirmed that even for strong nonlinear signals, such as pressure responses in the flow field, KKF-VSM is more efficient than the commonly used RBF-NNM. It can provide a reference for the application of the artificial lateral line system on underwater robotics, improving its adaptability in complex environments based on flow field information. |
| first_indexed | 2024-03-10T09:01:45Z |
| format | Article |
| id | doaj.art-cdb9128210d54bbbad77260ebbf869ac |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:01:45Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cdb9128210d54bbbad77260ebbf869ac2023-11-22T06:46:27ZengMDPI AGApplied Sciences2076-34172021-08-011116775910.3390/app11167759Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater RoboticsXinghua Lin0Qing Qin1Xiaoming Wang2Junxia Zhang3School of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, ChinaChina Automotive Technology and Research Center Co., Ltd., Tianjin 300300, ChinaSchool of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, ChinaSchool of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, ChinaThe flow field is difficult to evaluate, and underwater robotics can only partly adapt to the submarine environment. However, fish can sense the complex underwater environment by their lateral line system. In order to reveal the fish flow sensing mechanism, a robust nonlinear signal estimation method based on the Volterra series model with the Kautz kernel function is provided, which is named KKF-VSM. The flow field signal around a square target is used as the original signal. The sinusoidal noise and the signal around a triangular obstacle are considered undesired signals, and the predicting performance of KKF-VSM is analyzed after introducing them locally in the original signals. Compared to the radial basis function neural network model (RBF-NNM), the advantages of KKF-VSM are not only its robustness but also its higher sensitivity to weak signals and its predicting accuracy. It is confirmed that even for strong nonlinear signals, such as pressure responses in the flow field, KKF-VSM is more efficient than the commonly used RBF-NNM. It can provide a reference for the application of the artificial lateral line system on underwater robotics, improving its adaptability in complex environments based on flow field information.https://www.mdpi.com/2076-3417/11/16/7759underwater roboticmachine learningsignal estimation methodflow sensingVolterra series modelunderwater targets recognition |
| spellingShingle | Xinghua Lin Qing Qin Xiaoming Wang Junxia Zhang Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics Applied Sciences underwater robotic machine learning signal estimation method flow sensing Volterra series model underwater targets recognition |
| title | Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics |
| title_full | Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics |
| title_fullStr | Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics |
| title_full_unstemmed | Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics |
| title_short | Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics |
| title_sort | robust flow field signal estimation method for flow sensing by underwater robotics |
| topic | underwater robotic machine learning signal estimation method flow sensing Volterra series model underwater targets recognition |
| url | https://www.mdpi.com/2076-3417/11/16/7759 |
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