A new bionic lateral line system applied to pitch motion parameters perception for autonomous underwater vehicles. (June 2020)
- Record Type:
- Journal Article
- Title:
- A new bionic lateral line system applied to pitch motion parameters perception for autonomous underwater vehicles. (June 2020)
- Main Title:
- A new bionic lateral line system applied to pitch motion parameters perception for autonomous underwater vehicles
- Authors:
- Liu, Guijie
Wang, Mengmeng
Xu, Lei
Incecik, Atilla
Sotelo, Miguel Angel
Li, Zhixiong
Li, Weihua - Abstract:
- Highlights: l An fish-shaped underwater vehicle is fabricated l A sensor array is built to collect pressure of the vehicle in pitch motion l Experiment and simulation are performed l Machine learning algorithms are used to identify the motion parameters Abstract: Attitude perception for autonomous underwater vehicles (AUVs) is a challenging task. Most of existing sensor systems are unable to detect the navigation attitude of AUVs while at the same time to identify the attitude geometric parameters. In order to bridge this research gap, a new artificial lateral line sensor (ALLS) system based on a pressure sensor array is proposed to perform pitch motion perception for AUVs. First of all, a boxfish-like robot was constructed based on the geometrical morphology of a boxfish. The proposed ALLS system was fabricated in the fish robot. Then sensing experiments in the conditions of different pitch motions of the robot were conducted and the experimental measurements were compared with numerical simulation results. The comparison showed consistent mechanisms between the numerical bionic sensor model and the ALLS in perceiving the pitch attitude of the fish robot. Subsequently, pressure measurements of the fish robot were processed by popular machine learning algorithms such as random forest and artificial neural network to establish a vehicle pitch identification model. The analysis results demonstrate that the developed ALLS system is effective in pitch motion parametersHighlights: l An fish-shaped underwater vehicle is fabricated l A sensor array is built to collect pressure of the vehicle in pitch motion l Experiment and simulation are performed l Machine learning algorithms are used to identify the motion parameters Abstract: Attitude perception for autonomous underwater vehicles (AUVs) is a challenging task. Most of existing sensor systems are unable to detect the navigation attitude of AUVs while at the same time to identify the attitude geometric parameters. In order to bridge this research gap, a new artificial lateral line sensor (ALLS) system based on a pressure sensor array is proposed to perform pitch motion perception for AUVs. First of all, a boxfish-like robot was constructed based on the geometrical morphology of a boxfish. The proposed ALLS system was fabricated in the fish robot. Then sensing experiments in the conditions of different pitch motions of the robot were conducted and the experimental measurements were compared with numerical simulation results. The comparison showed consistent mechanisms between the numerical bionic sensor model and the ALLS in perceiving the pitch attitude of the fish robot. Subsequently, pressure measurements of the fish robot were processed by popular machine learning algorithms such as random forest and artificial neural network to establish a vehicle pitch identification model. The analysis results demonstrate that the developed ALLS system is effective in pitch motion parameters recognition, which may provide a new way for self-attitude perception and adjustment of AUVs. … (more)
- Is Part Of:
- Applied ocean research. Volume 99(2020)
- Journal:
- Applied ocean research
- Issue:
- Volume 99(2020)
- Issue Display:
- Volume 99, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 99
- Issue:
- 2020
- Issue Sort Value:
- 2020-0099-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-06
- Subjects:
- Autonomous underwater vehicle -- Bionic lateral lines -- Bionic sensor -- Motion perception
Ocean engineering -- Periodicals
620.416205 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01411187 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.apor.2020.102142 ↗
- Languages:
- English
- ISSNs:
- 0141-1187
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1576.240000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13454.xml