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- W4386821851 abstract "Autonomous underwater vehicle (AUV) is required to have the hovering positioning ability to maintain hovering and counteract environmental disturbance when performing some tasks such as ocean exploration and fishing. The complex and changeable marine environment, as well as the strong coupling and nonlinearity of the underwater vehicle itself, pose a great challenge to its control system. In this paper, a hover positioning method of autonomous underwater vehicle based on deep reinforcement learning algorithm is proposed to resist the interference of unknown size and direction of ocean current. Two kinds of neural networks, Actor and Critical, are designed using the depth deterministic strategy gradient method. The Actor neural network gives the control strategy, and the Critical neural network is used to evaluate the strategy. Combined with the principle of environmental optimal heading, the simulation results show that the AUV can effectively achieve hover control and save energy consumption under the uncertain environment." @default.
- W4386821851 created "2023-09-19" @default.
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- W4386821851 date "2023-07-24" @default.
- W4386821851 modified "2023-09-26" @default.
- W4386821851 title "Reinforcement Learning Method for AUV Hover Control in Uncertain Environment" @default.
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- W4386821851 doi "https://doi.org/10.23919/ccc58697.2023.10240614" @default.
- W4386821851 hasPublicationYear "2023" @default.
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