Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration
Unmanned underwater vehicles (UUV) are widely used tools in ocean development, which can be applied in areas such as marine scientific research, ocean resources exploration, and ocean security. However, as ocean exploration advances, UUVs face increasingly challenging operational environments with w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/9/1783 |
| _version_ | 1797576534471475200 |
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| author | Yuriy Kozhubaev Victor Belyaev Yuriy Murashov Oleg Prokofev |
| author_facet | Yuriy Kozhubaev Victor Belyaev Yuriy Murashov Oleg Prokofev |
| author_sort | Yuriy Kozhubaev |
| collection | DOAJ |
| description | Unmanned underwater vehicles (UUV) are widely used tools in ocean development, which can be applied in areas such as marine scientific research, ocean resources exploration, and ocean security. However, as ocean exploration advances, UUVs face increasingly challenging operational environments with weaker communication signals. Consequently, autonomous obstacle avoidance planning for UUVs becomes increasingly important. With the deepening of ocean exploration, the operational environment of UUVs has become increasingly difficult to access, and the communication signals in the environment have become weaker. Therefore, autonomous obstacle avoidance planning of UUVs has become increasingly important. Traditional dynamic programming methods face challenges in terms of accuracy and real-time performance, requiring the design of auxiliary strategies to achieve ideal avoidance and requiring cumbersome perception equipment to support them. Therefore, exploring an efficient and easy-to-implement dynamic programming method has significant theoretical and practical value. In this study, an LSTM-RNN network structure suitable for UUVs was designed to learn the dynamic programming mode of UUVs in an unknown environment. The research was divided into three main aspects: collecting the required sample dataset for training deep networks, designing the LSTM-RNN network structure, and utilizing LSTM-RNN to achieve dynamic programming. Experimental results demonstrated that LSTM-RNN can learn planning patterns in unknown environments without the need for constructing an environment model or complex perception devices, thus providing significant theoretical and practical value. Consequently, this approach offers an effective solution for autonomous obstacle avoidance planning for UUVs. |
| first_indexed | 2024-03-10T21:54:26Z |
| format | Article |
| id | doaj.art-4aa83236d0f1428ba9a3f763232a4c37 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T21:54:26Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-4aa83236d0f1428ba9a3f763232a4c372023-11-19T13:12:36ZengMDPI AGSymmetry2073-89942023-09-01159178310.3390/sym15091783Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources ExplorationYuriy Kozhubaev0Victor Belyaev1Yuriy Murashov2Oleg Prokofev3Department of Informatics and Computer Technologies, St. Petersburg Mining University, 2, 21st Line, St. Petersburg 199106, RussiaDepartment of Informatics and Computer Technologies, St. Petersburg Mining University, 2, 21st Line, St. Petersburg 199106, RussiaInstitute of Computer Science and Technology, Higher School of Cyberphysical Systems & Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, RussiaInstitute of Computer Science and Technology, Higher School of Cyberphysical Systems & Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, RussiaUnmanned underwater vehicles (UUV) are widely used tools in ocean development, which can be applied in areas such as marine scientific research, ocean resources exploration, and ocean security. However, as ocean exploration advances, UUVs face increasingly challenging operational environments with weaker communication signals. Consequently, autonomous obstacle avoidance planning for UUVs becomes increasingly important. With the deepening of ocean exploration, the operational environment of UUVs has become increasingly difficult to access, and the communication signals in the environment have become weaker. Therefore, autonomous obstacle avoidance planning of UUVs has become increasingly important. Traditional dynamic programming methods face challenges in terms of accuracy and real-time performance, requiring the design of auxiliary strategies to achieve ideal avoidance and requiring cumbersome perception equipment to support them. Therefore, exploring an efficient and easy-to-implement dynamic programming method has significant theoretical and practical value. In this study, an LSTM-RNN network structure suitable for UUVs was designed to learn the dynamic programming mode of UUVs in an unknown environment. The research was divided into three main aspects: collecting the required sample dataset for training deep networks, designing the LSTM-RNN network structure, and utilizing LSTM-RNN to achieve dynamic programming. Experimental results demonstrated that LSTM-RNN can learn planning patterns in unknown environments without the need for constructing an environment model or complex perception devices, thus providing significant theoretical and practical value. Consequently, this approach offers an effective solution for autonomous obstacle avoidance planning for UUVs.https://www.mdpi.com/2073-8994/15/9/1783deep learningdynamic planningLSTM-RNNant colony algorithmUUV |
| spellingShingle | Yuriy Kozhubaev Victor Belyaev Yuriy Murashov Oleg Prokofev Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration Symmetry deep learning dynamic planning LSTM-RNN ant colony algorithm UUV |
| title | Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration |
| title_full | Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration |
| title_fullStr | Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration |
| title_full_unstemmed | Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration |
| title_short | Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration |
| title_sort | controlling of unmanned underwater vehicles using the dynamic planning of symmetric trajectory based on machine learning for marine resources exploration |
| topic | deep learning dynamic planning LSTM-RNN ant colony algorithm UUV |
| url | https://www.mdpi.com/2073-8994/15/9/1783 |
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