Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles
The exploration of the underwater environments has recently accelerated with the development of the Autonomous Underwater Vehicle (AUV). One of the key elements for enhancing the autonomy of AUVs navigation across various applications is efficient path planning. Reinforcement Learning (RL) methods h...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10770217/ |
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| author | Elena Politi Artemis Stefanidou Christos Chronis George Dimitrakopoulos and Iraklis Varlamis |
| author_facet | Elena Politi Artemis Stefanidou Christos Chronis George Dimitrakopoulos and Iraklis Varlamis |
| author_sort | Elena Politi |
| collection | DOAJ |
| description | The exploration of the underwater environments has recently accelerated with the development of the Autonomous Underwater Vehicle (AUV). One of the key elements for enhancing the autonomy of AUVs navigation across various applications is efficient path planning. Reinforcement Learning (RL) methods have been successfully introduced for path planning of AUVs, particularly in high-dimensional state spaces, where prior knowledge of the environment is unfeasible. In this work, we propose a Deep Reinforcement Learning (DRL) method for efficient AUV navigation in 3 Dimension (3D) environments, utilizing input from vision sensors to obtain information about the motion of the AUV and the surrounding space. We adopt a multi-tier approach in order to validate the performance of the proposed DRL approach in three different neural network architectures leveraging on adaptation and accuracy, with path length, execution time and success of operation being considered as the optimization objectives. Finally, a simulation platform is built to evaluate the performance of the proposed method, with experimental results showcasing enhanced decision-making capability for the AUV navigation, which translates to a higher level of autonomy for the vehicle in unknown environments. |
| format | Article |
| id | doaj-art-8b3faf26bebd436aa50dce081f33c335 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8b3faf26bebd436aa50dce081f33c3352025-08-20T01:54:55ZengIEEEIEEE Access2169-35362024-01-011217820917822110.1109/ACCESS.2024.350803110770217Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater VehiclesElena Politi0https://orcid.org/0000-0001-8795-5560Artemis Stefanidou1https://orcid.org/0009-0008-2059-7429Christos Chronis2https://orcid.org/0000-0002-2768-7119George Dimitrakopoulos3https://orcid.org/0000-0002-7424-8557and Iraklis Varlamis4https://orcid.org/0000-0002-0876-8167Department of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceThe exploration of the underwater environments has recently accelerated with the development of the Autonomous Underwater Vehicle (AUV). One of the key elements for enhancing the autonomy of AUVs navigation across various applications is efficient path planning. Reinforcement Learning (RL) methods have been successfully introduced for path planning of AUVs, particularly in high-dimensional state spaces, where prior knowledge of the environment is unfeasible. In this work, we propose a Deep Reinforcement Learning (DRL) method for efficient AUV navigation in 3 Dimension (3D) environments, utilizing input from vision sensors to obtain information about the motion of the AUV and the surrounding space. We adopt a multi-tier approach in order to validate the performance of the proposed DRL approach in three different neural network architectures leveraging on adaptation and accuracy, with path length, execution time and success of operation being considered as the optimization objectives. Finally, a simulation platform is built to evaluate the performance of the proposed method, with experimental results showcasing enhanced decision-making capability for the AUV navigation, which translates to a higher level of autonomy for the vehicle in unknown environments.https://ieeexplore.ieee.org/document/10770217/AUVunderwateractor-critic algorithmAUV navigationdynamic path planningproximal policy optimization |
| spellingShingle | Elena Politi Artemis Stefanidou Christos Chronis George Dimitrakopoulos and Iraklis Varlamis Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles IEEE Access AUV underwater actor-critic algorithm AUV navigation dynamic path planning proximal policy optimization |
| title | Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles |
| title_full | Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles |
| title_fullStr | Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles |
| title_full_unstemmed | Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles |
| title_short | Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles |
| title_sort | adaptive deep reinforcement learning for efficient 3d navigation of autonomous underwater vehicles |
| topic | AUV underwater actor-critic algorithm AUV navigation dynamic path planning proximal policy optimization |
| url | https://ieeexplore.ieee.org/document/10770217/ |
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