Reinforcement Learning Based Energy Efficient Underwater Localization

You, Xudong; Lv, Zefang; Ding, Yong; Su, Wei; Xiao, Liang

doi:10.1109/wcsp49889.2020.9299789

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…In general, the performance of the AUV controller is based on the cumulative reward, training time, the motion path of the AUV and the target, and the number of collision violations (CV) [43][44][45][46][47]. The reward is obtained by the AUV from the environment during training.…”

Section: Evaluation Standardmentioning

confidence: 99%

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

Mao

Gao

Zhang

et al. 2022

JMSE

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This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-agent GAIL (MAG) algorithm is proposed. The GAIL enables the AUV to directly learn from expert demonstrations, overcoming the difficulty of slow initial training of the network. Parallel training of multi-agents reduces the high correlation between samples to avoid local convergence. In addition, a reward function is designed to help training. Finally, the results show that in the unity simulation platform test, the proposed algorithm has a strong optimal decision-making ability in the tracking process.

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Section: Evaluation Standardmentioning

confidence: 99%

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

Mao

Gao

Zhang

et al. 2022

JMSE

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“…Meanwhile, RL has been applied in the underwater communication and localization. For example, an RL-based energy-efficient underwater localization algorithm proposed in [19] applies Dyna-Q to reduce the localization error and the energy consumption. An unsupervised wireless localization method proposed in [20] applies deep RL to reduce localization error.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to [19], UUV estimates the distance l between itself and anchor node based on signal reception time and average speed v ave obtained above. Then according to the received anchor node coordinates, UUV calculates its own position u k which is given by…”

Section: Rl-based Mobile Underwater Localization Algorithmmentioning

confidence: 99%

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Reinforcement Learning Based Mobile Underwater Localization for Silent UUV in Underwater Acoustic Sensor Networks

Liao

et al. 2022

Wireless Communications and Mobile Computing

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Unmanned underwater vehicles (UUVs) that are widely utilized for underwater cooperative combat, underwater environment detection and underwater resource exploration have to be localized by underwater acoustic sensor networks (UASNs). However, the localization accuracy is hard to guarantee due to the limited bandwidths, long propagation latency, and limited energy resources of the UASNs. In this paper, we propose a reinforcement learning (RL) and neural network based mobile underwater localization scheme to optimize the anchor nodes selection in the UASNs to localize the target precisely. More specifically, this scheme applies SqueezeNet to select the line-of-sight (LOS) anchor nodes based on the received signals. In addition, an RL-based approach is further proposed to make further selection from the LOS anchor nodes without knowing the underwater environment model. The Dyna architecture is applied to reduce the convergence time of the anchor nodes selection. Simulation results based on a nonisovelocity geometry-based underwater acoustic channel model show that the proposed schemes significantly improve the localization accuracy and reduce energy consumption of the UASN to achieve trajectory correction.

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Reinforcement Learning Based Energy Efficient Underwater Passive Localization of Hidden Mobile Node

Liao

You

et al. 2021

2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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Reinforcement Learning Based Energy Efficient Underwater Localization

Cited by 5 publications

References 13 publications

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

Reinforcement Learning Based Mobile Underwater Localization for Silent UUV in Underwater Acoustic Sensor Networks

Reinforcement Learning Based Energy Efficient Underwater Passive Localization of Hidden Mobile Node

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