A localization algorithm based on pose graph using Forward-looking sonar for deep-sea mining vehicle

Xu, Wenhao; Yang, Jianmin; Wei, Handi; Lü, Haining; Tian, Xinliang; Li, Xin

doi:10.1016/j.oceaneng.2023.114968

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…The total reward R total can then be expressed as a linear superposition of the multiple reward functions described above: R total = r main + r cov + r rep + r dis (12)…”

Section: Design Of Reward Functionmentioning

confidence: 99%

“…Over the past few years, there have been unprecedented developments in unmanned mobile vehicles, which have wide applications in multiple fields such as logistics [11], minerals [12], agriculture [13], etc. Unlike those on land, deep-sea sediments are characterized by low shear strength, high adhesion [14], large pores, and some fluid properties.…”

Section: Introductionmentioning

confidence: 99%

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The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Xing,

Wang,

Liu

2024

JMSE

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The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.

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“…The total reward R total can then be expressed as a linear superposition of the multiple reward functions described above: R total = r main + r cov + r rep + r dis (12)…”

Section: Design Of Reward Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Xing,

Wang,

Liu

2024

JMSE

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“…Mining vehicles need to use a variety of technologies, such as environmental perception, [ 9 ] self‐positioning, [ 10,11 ] motion control, [ 12 ] and complete coverage path planning in the deep‐sea environment.…”

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

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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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“…Zhang utilizes BSLAM (Bathymetric Particle Filter SLAM) which is accurate and fast for oceanographic surveys, demining, and seabed mappings [25]. Yang proposes a SLAM localization algorithm using forward-looking sonar for deep-sea mining [26]. Mahajan proposed a pilot aid using visual SLAM for seabed surveying applications [27].…”

Section: Introductionmentioning

confidence: 99%

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

Liu,

Wang,

et al. 2024

JMSE

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The feature matching of the near-bottom visual SLAM is influenced by underwater raised sediments, resulting in tracking loss. In this paper, the novel visual SLAM system is proposed in the underwater raised sediments environment. The underwater images are firstly classified based on the color recognition method by adding the weights of pixel location to reduce the interference of similar colors on the seabed. The improved adaptive median filter method is proposed to filter the classified images by using the mean value of the filter window border as the discriminant condition to retain the original features of the image. The filtered images are finally processed by the tracking module to obtain the trajectory of underwater vehicles and the seafloor maps. The datasets of seamount areas captured in the western Pacific Ocean are processed by the improved visual SLAM system. The keyframes, mapping points, and feature point matching pairs extracted from the improved visual SLAM system are improved by 5.2%, 11.2%, and 4.5% compared with that of the ORB-SLAM3 system, respectively. The improved visual SLAM system has the advantage of robustness to dynamic disturbances, which is of practical application in underwater vehicles operated in near-bottom areas such as seamounts and nodules.

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A localization algorithm based on pose graph using Forward-looking sonar for deep-sea mining vehicle

Cited by 5 publications

References 28 publications

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

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

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

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