An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Liang, Weixing; Lou, Min; Chen, Zhangxing; Qin, Huiyang; Zhang, Chen; Cui, Chengwei; Wang, Yangyang

doi:10.1016/j.oceaneng.2024.117415

Ocean Engineering

2024

DOI: 10.1016/j.oceaneng.2024.117415

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An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Weixing Liang,

Min Lou,

Zhangxing Chen

et al.

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Cited by 10 publications

References 27 publications

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Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

Guan,

Qin,

et al. 2024

Advcd Theory and Sims

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The aim of this paper is to propose a novel method for identifying the hydrodynamic parameters of a deep‐sea mining vehicle during deployment and retrieval. The proposed approach combines numerical simulation with a nonlinear filter. Initially, a dedicated hydrodynamic model for the deployment and retrieval of the mining vehicle is constructed. The identification process commences with simulations based on computational fluid dynamics (CFD). This approach utilizes CFD to simulate the motion of the deep‐sea mining vehicle during deployment and retrieval, employing an implicit solution approach to analyze its motion in Heave and Yaw degrees of freedom under periodic external forces. Consequently, this provides hydrodynamic performance data. Subsequently, the unscented Kalman filter (UKF) estimator is applied to optimally solve an augmented matrix that incorporates both motion data and hydrodynamic parameters, yielding numerical values for the hydrodynamic parameters. Simulation results demonstrate that, in comparison to motion performance obtained by the CFD method, the hydrodynamic model derived from UKF enables an effective prediction of the motion of the deep‐sea mining vehicle, with prediction errors consistently below 6%.

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Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

Guan,

Qin,

et al. 2024

Advcd Theory and Sims

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Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks

Tuppad

2024

Environ Monit Assess

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Enhancing sand cat swarm optimization based on multi-strategy mixing for solving engineering optimization problems

Wang,

Han,

Zhang

et al. 2024

Evol. Intel.

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An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Cited by 10 publications

References 27 publications

Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks

Enhancing sand cat swarm optimization based on multi-strategy mixing for solving engineering optimization problems

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