Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning

The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiang Cao [verfasserIn] Changyin Sun [verfasserIn] Mingzhong Yan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Target search frontier exploration deep learning reinforcement learning

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 96549-96559
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:96549-96559

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2929120

Katalog-ID:	DOAJ073317608

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allfields	10.1109/ACCESS.2019.2929120 doi (DE-627)DOAJ073317608 (DE-599)DOAJ59dac52ec2d2464eb8d72402f0ba6014 DE-627 ger DE-627 rakwb eng TK1-9971 Xiang Cao verfasserin aut Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time. Target search frontier exploration deep learning reinforcement learning Electrical engineering. Electronics. Nuclear engineering Changyin Sun verfasserin aut Mingzhong Yan verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 96549-96559 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:96549-96559 https://doi.org/10.1109/ACCESS.2019.2929120 kostenfrei https://doaj.org/article/59dac52ec2d2464eb8d72402f0ba6014 kostenfrei https://ieeexplore.ieee.org/document/8764329/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 96549-96559
spelling	10.1109/ACCESS.2019.2929120 doi (DE-627)DOAJ073317608 (DE-599)DOAJ59dac52ec2d2464eb8d72402f0ba6014 DE-627 ger DE-627 rakwb eng TK1-9971 Xiang Cao verfasserin aut Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time. Target search frontier exploration deep learning reinforcement learning Electrical engineering. Electronics. Nuclear engineering Changyin Sun verfasserin aut Mingzhong Yan verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 96549-96559 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:96549-96559 https://doi.org/10.1109/ACCESS.2019.2929120 kostenfrei https://doaj.org/article/59dac52ec2d2464eb8d72402f0ba6014 kostenfrei https://ieeexplore.ieee.org/document/8764329/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 96549-96559
allfields_unstemmed	10.1109/ACCESS.2019.2929120 doi (DE-627)DOAJ073317608 (DE-599)DOAJ59dac52ec2d2464eb8d72402f0ba6014 DE-627 ger DE-627 rakwb eng TK1-9971 Xiang Cao verfasserin aut Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time. Target search frontier exploration deep learning reinforcement learning Electrical engineering. Electronics. Nuclear engineering Changyin Sun verfasserin aut Mingzhong Yan verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 96549-96559 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:96549-96559 https://doi.org/10.1109/ACCESS.2019.2929120 kostenfrei https://doaj.org/article/59dac52ec2d2464eb8d72402f0ba6014 kostenfrei https://ieeexplore.ieee.org/document/8764329/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 96549-96559
allfieldsGer	10.1109/ACCESS.2019.2929120 doi (DE-627)DOAJ073317608 (DE-599)DOAJ59dac52ec2d2464eb8d72402f0ba6014 DE-627 ger DE-627 rakwb eng TK1-9971 Xiang Cao verfasserin aut Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time. Target search frontier exploration deep learning reinforcement learning Electrical engineering. Electronics. Nuclear engineering Changyin Sun verfasserin aut Mingzhong Yan verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 96549-96559 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:96549-96559 https://doi.org/10.1109/ACCESS.2019.2929120 kostenfrei https://doaj.org/article/59dac52ec2d2464eb8d72402f0ba6014 kostenfrei https://ieeexplore.ieee.org/document/8764329/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 96549-96559
allfieldsSound	10.1109/ACCESS.2019.2929120 doi (DE-627)DOAJ073317608 (DE-599)DOAJ59dac52ec2d2464eb8d72402f0ba6014 DE-627 ger DE-627 rakwb eng TK1-9971 Xiang Cao verfasserin aut Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time. Target search frontier exploration deep learning reinforcement learning Electrical engineering. Electronics. Nuclear engineering Changyin Sun verfasserin aut Mingzhong Yan verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 96549-96559 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:96549-96559 https://doi.org/10.1109/ACCESS.2019.2929120 kostenfrei https://doaj.org/article/59dac52ec2d2464eb8d72402f0ba6014 kostenfrei https://ieeexplore.ieee.org/document/8764329/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 96549-96559
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source	In IEEE Access 7(2019), Seite 96549-96559 volume:7 year:2019 pages:96549-96559
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authorswithroles_txt_mv	Xiang Cao @@aut@@ Changyin Sun @@aut@@ Mingzhong Yan @@aut@@
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topic_title	TK1-9971 Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning Target search frontier exploration deep learning reinforcement learning
topic	misc TK1-9971 misc Target search misc frontier exploration misc deep learning misc reinforcement learning misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Target search misc frontier exploration misc deep learning misc reinforcement learning misc Electrical engineering. Electronics. Nuclear engineering
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title	Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning
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title_full	Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning
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title_auth	Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning
abstract	The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time.
abstractGer	The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time.
abstract_unstemmed	The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time.
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title_short	Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning
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up_date	2024-07-03T17:01:55.314Z
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