Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method
The lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perceptio...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/14/7241 |
| _version_ | 1797407574595731456 |
|---|---|
| author | Yanyun Pu Zheyi Hang Gaoang Wang Huan Hu |
| author_facet | Yanyun Pu Zheyi Hang Gaoang Wang Huan Hu |
| author_sort | Yanyun Pu |
| collection | DOAJ |
| description | The lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perception ability of fish, we propose an artificial lateral line system composed of pressure sensors that respond to a target’s relative position by measuring the pressure change of the target vibration near the lateral line. Based on the shortcomings of the idealized constrained modeling approach, a multilayer perceptron network was built in this paper to process the pressure signal and predict the coordinates on a two-dimensional plane. Previous studies primarily focused on the localization of a single dipole source and rarely considered the localization of multiple vibration sources. In this paper, we explore the localization of numerous dipole sources of the same and different frequency vibrations based on the prediction of the two-dimensional coordinates of double dipoles. The experimental results show that the mutual interference of two vibration sources causes an increase in the localization error. Compared with multiple sources of vibration at the same frequency, the positioning accuracies of various vibration sources at different frequencies are higher. In addition, we explored the effects of the number of sensors on the localization results. |
| first_indexed | 2024-03-09T03:43:31Z |
| format | Article |
| id | doaj.art-7f2673f166364d03ac08145935887726 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T03:43:31Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7f2673f166364d03ac081459358877262023-12-03T14:37:19ZengMDPI AGApplied Sciences2076-34172022-07-011214724110.3390/app12147241Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network MethodYanyun Pu0Zheyi Hang1Gaoang Wang2Huan Hu3ZJU-UIUC Institute, International Campus, Zhejiang University, Haining 314400, ChinaZJU-UIUC Institute, International Campus, Zhejiang University, Haining 314400, ChinaZJU-UIUC Institute, International Campus, Zhejiang University, Haining 314400, ChinaZJU-UIUC Institute, International Campus, Zhejiang University, Haining 314400, ChinaThe lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perception ability of fish, we propose an artificial lateral line system composed of pressure sensors that respond to a target’s relative position by measuring the pressure change of the target vibration near the lateral line. Based on the shortcomings of the idealized constrained modeling approach, a multilayer perceptron network was built in this paper to process the pressure signal and predict the coordinates on a two-dimensional plane. Previous studies primarily focused on the localization of a single dipole source and rarely considered the localization of multiple vibration sources. In this paper, we explore the localization of numerous dipole sources of the same and different frequency vibrations based on the prediction of the two-dimensional coordinates of double dipoles. The experimental results show that the mutual interference of two vibration sources causes an increase in the localization error. Compared with multiple sources of vibration at the same frequency, the positioning accuracies of various vibration sources at different frequencies are higher. In addition, we explored the effects of the number of sensors on the localization results.https://www.mdpi.com/2076-3417/12/14/7241artificial lateral lineunderwater localizationartificial neural networkmulti-source vibration |
| spellingShingle | Yanyun Pu Zheyi Hang Gaoang Wang Huan Hu Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method Applied Sciences artificial lateral line underwater localization artificial neural network multi-source vibration |
| title | Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method |
| title_full | Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method |
| title_fullStr | Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method |
| title_full_unstemmed | Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method |
| title_short | Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method |
| title_sort | bionic artificial lateral line underwater localization based on the neural network method |
| topic | artificial lateral line underwater localization artificial neural network multi-source vibration |
| url | https://www.mdpi.com/2076-3417/12/14/7241 |
| work_keys_str_mv | AT yanyunpu bionicartificiallaterallineunderwaterlocalizationbasedontheneuralnetworkmethod AT zheyihang bionicartificiallaterallineunderwaterlocalizationbasedontheneuralnetworkmethod AT gaoangwang bionicartificiallaterallineunderwaterlocalizationbasedontheneuralnetworkmethod AT huanhu bionicartificiallaterallineunderwaterlocalizationbasedontheneuralnetworkmethod |