Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation
Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target's position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 24(2024), 2 vom: 10. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wen, Zhiwen [VerfasserIn] |
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| Links: |
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| Themen: |
AUV formation |
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| Anmerkungen: |
Date Revised 23.01.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/s24020437 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367480204 |
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| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target's position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a AUV formation | |
| 650 | 4 | |a LSTM | |
| 650 | 4 | |a artificial potential field | |
| 650 | 4 | |a collaborative detection | |
| 650 | 4 | |a collaborative surrounding attack | |
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| 700 | 1 | |a Wang, Zhong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Daming |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Dezhou |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Yichen |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Junchang |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Huachao |e verfasserin |4 aut | |
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