An Improved DSA-Based Approach for Multi-AUV Cooperative Search
Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In t...
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Online Access: | http://dx.doi.org/10.1155/2018/2186574 |
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doaj-f06477fc9fdc44f8820d8b59367ccea82020-11-25T01:02:25ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732018-01-01201810.1155/2018/21865742186574An Improved DSA-Based Approach for Multi-AUV Cooperative SearchJianjun Ni0Liu Yang1Pengfei Shi2Chengming Luo3College of IOT Engineering, Hohai University, Changzhou 213022, ChinaCollege of IOT Engineering, Hohai University, Changzhou 213022, ChinaCollege of IOT Engineering, Hohai University, Changzhou 213022, ChinaCollege of IOT Engineering, Hohai University, Changzhou 213022, ChinaMulti-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage. Then the self-organizing map (SOM) neural network is used to build dynamic alliances in real time. Finally, an improved DSA algorithm is presented to realize the team search. Furthermore, some simulations are conducted, and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the target search task in unknown 3D underwater environment efficiently.http://dx.doi.org/10.1155/2018/2186574 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianjun Ni Liu Yang Pengfei Shi Chengming Luo |
spellingShingle |
Jianjun Ni Liu Yang Pengfei Shi Chengming Luo An Improved DSA-Based Approach for Multi-AUV Cooperative Search Computational Intelligence and Neuroscience |
author_facet |
Jianjun Ni Liu Yang Pengfei Shi Chengming Luo |
author_sort |
Jianjun Ni |
title |
An Improved DSA-Based Approach for Multi-AUV Cooperative Search |
title_short |
An Improved DSA-Based Approach for Multi-AUV Cooperative Search |
title_full |
An Improved DSA-Based Approach for Multi-AUV Cooperative Search |
title_fullStr |
An Improved DSA-Based Approach for Multi-AUV Cooperative Search |
title_full_unstemmed |
An Improved DSA-Based Approach for Multi-AUV Cooperative Search |
title_sort |
improved dsa-based approach for multi-auv cooperative search |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2018-01-01 |
description |
Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage. Then the self-organizing map (SOM) neural network is used to build dynamic alliances in real time. Finally, an improved DSA algorithm is presented to realize the team search. Furthermore, some simulations are conducted, and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the target search task in unknown 3D underwater environment efficiently. |
url |
http://dx.doi.org/10.1155/2018/2186574 |
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1725205071415738368 |