Guidance and control of autonomous surface underwater vehicles for target tracking in ocean environment by deep reinforcement learning

Song, Dalei; Gan, Wenhao; Yao, Ping; Zang, Wenchuan; Zhang, Zhixuan; Qu, Xiuqing

doi:10.1016/j.oceaneng.2022.110947

Cited by 30 publications

(4 citation statements)

References 27 publications

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“…Song D et al [ 20 ], investigated the application of deep reinforcement learning, often known as DRL, for target tracking within the context of multiple autonomous surfaces underwater vehicle guidance and control architecture. The framework instructs the vehicles to accomplish the standoff tracking and sampling tasks along a circular route centered on the target while maintaining present relative positions throughout the procedure.…”

Section: Literature Surveymentioning

confidence: 99%

Enhancing underwater target localization through proximity-driven recurrent neural networks

Kumar,

Chinthaginjala,

et al. 2024

Heliyon

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Section: Literature Surveymentioning

confidence: 99%

Enhancing underwater target localization through proximity-driven recurrent neural networks

Kumar,

Chinthaginjala,

et al. 2024

Heliyon

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“…Therefore, robust target orientation detection underwater is challenging [5,6]. The cilia MEMS bionic vector hydrophone has features of low cost, low power consumption, miniaturization, and good low-frequency performance, as well as higher sensitivity and wider bandwidth with the continuous research and improvement of the MEMS vector hydrophone, exhibiting excellent performance in the field of hydroacoustic detection [7,8]. In 2022, Zhu Shan et al designed a noise measurement system based on a MEMS vector hydrophone for the measurement of radiated noise from underwater ships.…”

Section: Introductionmentioning

confidence: 99%

Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone

Liu,

Jing,

Zhang

et al. 2024

Micromachines

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Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of −17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring.

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“…Zhou et al proposed a trajectory tracking control method for the tractor-trailer wheeled robot [3]. Song et al studied a guidance and control framework of multiple autonomous surface underwater vehicles (multi-ASUV) based on deep reinforcement learning (DRL) for target tracking [4]. Miao et al presented a new robust path-following (PF) control method for underactuated marine vehicles with multiple disturbances and constraints [5].…”

Section: Introductionmentioning

confidence: 99%

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

Han,

Liu,

Peng

2024

J. vibroeng.

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In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

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Guidance and control of autonomous surface underwater vehicles for target tracking in ocean environment by deep reinforcement learning

Cited by 30 publications

References 27 publications

Enhancing underwater target localization through proximity-driven recurrent neural networks

Enhancing underwater target localization through proximity-driven recurrent neural networks

Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

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