An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle
The development of launch and recovery technology is key for the application to the unmanned surface vehicle (USV). Also, a launch and recovery system (L&RS) based on a pneumatic ejection mechanism has been developed in our previous study. To improve the launch accuracy and reduce the influence...
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| Format: | Article |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5662 |
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| author | Yang Yang Ping Pan Xingang Jiang Shuanghua Zheng Yongjian Zhao Yi Yang Songyi Zhong Yan Peng |
| author_facet | Yang Yang Ping Pan Xingang Jiang Shuanghua Zheng Yongjian Zhao Yi Yang Songyi Zhong Yan Peng |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | The development of launch and recovery technology is key for the application to the unmanned surface vehicle (USV). Also, a launch and recovery system (L&RS) based on a pneumatic ejection mechanism has been developed in our previous study. To improve the launch accuracy and reduce the influence of the sea waves, we propose a stacking model of one-dimensional convolutional neural network and long short-term memory neural network predicting the attitude of the USV. The data from experiments by “Jinghai VII” USV developed by Shanghai University, China, under levels 1–4 sea conditions are used to train and test the network. The results show that the stabilized platform with the proposed prediction method can keep the launching angle of the launching mechanism constant by regulating the pitching joint and rotation joint under the random influence from the wave. Finally, the efficiency and effectiveness of the L&RS are demonstrated by the successful application in actual environments. |
| first_indexed | 2024-03-10T15:52:12Z |
| format | Article |
| id | doaj.art-e0cbd64a01c648089d73bd1e2c6be7c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:52:12Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e0cbd64a01c648089d73bd1e2c6be7c52023-11-20T16:01:22ZengMDPI AGSensors1424-82202020-10-012019566210.3390/s20195662An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface VehicleYang Yang0Ping Pan1Xingang Jiang2Shuanghua Zheng3Yongjian Zhao4Yi Yang5Songyi Zhong6Yan Peng7School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaThe development of launch and recovery technology is key for the application to the unmanned surface vehicle (USV). Also, a launch and recovery system (L&RS) based on a pneumatic ejection mechanism has been developed in our previous study. To improve the launch accuracy and reduce the influence of the sea waves, we propose a stacking model of one-dimensional convolutional neural network and long short-term memory neural network predicting the attitude of the USV. The data from experiments by “Jinghai VII” USV developed by Shanghai University, China, under levels 1–4 sea conditions are used to train and test the network. The results show that the stabilized platform with the proposed prediction method can keep the launching angle of the launching mechanism constant by regulating the pitching joint and rotation joint under the random influence from the wave. Finally, the efficiency and effectiveness of the L&RS are demonstrated by the successful application in actual environments.https://www.mdpi.com/1424-8220/20/19/5662unmanned surface vehicle (USV)launch and recovery system (L&RS)attitude predictionconvolutional neural network (CNN)long short-term memory (LSTM) neural network |
| spellingShingle | Yang Yang Ping Pan Xingang Jiang Shuanghua Zheng Yongjian Zhao Yi Yang Songyi Zhong Yan Peng An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle Sensors unmanned surface vehicle (USV) launch and recovery system (L& RS) attitude prediction convolutional neural network (CNN) long short-term memory (LSTM) neural network |
| title | An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle |
| title_full | An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle |
| title_fullStr | An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle |
| title_full_unstemmed | An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle |
| title_short | An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle |
| title_sort | attitude prediction method for autonomous recovery operation of unmanned surface vehicle |
| topic | unmanned surface vehicle (USV) launch and recovery system (L& RS) attitude prediction convolutional neural network (CNN) long short-term memory (LSTM) neural network |
| url | https://www.mdpi.com/1424-8220/20/19/5662 |
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