Correction: Xing et al. An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation. J. Mar. Sci. Eng. 2021, 9, 1156

Xing, Xiang; Liu, Bainian; Zhang, Weimin; Wu, Jianping; Cao, Xiaoqun; Huang, Qunbo

doi:10.3390/jmse10070998

Cited by 1 publication

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“…For the complete coverage path planning mission, the algorithms commonly used today include traditional algorithms, [13] unit-decomposition algorithms, [14] and intelligent algorithms. [15][16][17] Ai et al propose an autonomous coverage path planning model for maritime SAR based on reinforcement learning and design a multi-objective optimization reward function consisting of avoiding maritime obstacles, avoiding repeated paths, and preferentially searching for high-probability areas. [18] Tan et al aimed at the problems of the local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm, a complete coverage path planning algorithm based on Qlearning.…”

Section: Introductionmentioning

confidence: 99%

“…For the complete coverage path planning mission, the algorithms commonly used today include traditional algorithms, [ 13 ] unit‐decomposition algorithms, [ 14 ] and intelligent algorithms. [ 15–17 ] Ai et al. propose an autonomous coverage path planning model for maritime SAR based on reinforcement learning and design a multi‐objective optimization reward function consisting of avoiding maritime obstacles, avoiding repeated paths, and preferentially searching for high‐probability areas.…”

Section: Introductionmentioning

confidence: 99%

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An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

Xing,

Wang,

Liu

2024

Advcd Theory and Sims

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This paper proposes a deep reinforcement learning algorithm to achieve complete coverage path planning for deep‐sea mining vehicle clusters. First, the mining vehicles and the deep‐sea mining environment are modeled. Then, this paper implements a series of algorithm designs and optimizations based on Deep Q Networks (DQN). The map fusion mechanism can integrate the grid matrix data from multiple mining vehicles to get the state matrix of the complete environment. In this paper, a preprocessing method for the state matrix is also designed to provide suitable training data for the neural network. The reward function and action selection mechanism of the algorithm are also optimized according to the requirements of cluster cooperative operation. Furthermore, the algorithm uses distance constraints to prevent the entanglement of underwater hoses. To improve the training efficiency of the neural network, the algorithm filters and extracts training samples for training through the sample quality score. Considering the requirement of cluster complete coverage mission, this paper introduces Long Short‐Term Memory (LSTM) based on the neural network to achieve a better training effect. After completing the above optimization and design, the algorithm proposed in this paper is verified through simulation experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

Xing,

Wang,

Liu

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

Correction: Xing et al. An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation. J. Mar. Sci. Eng. 2021, 9, 1156

Abstract: The authors wish to make the following corrections to this paper [...]

Cited by 1 publication

References 1 publication

An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

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