The estimation of hydrodynamic coefficients of an autonomous underwater vehicle by comparing a dynamic mesh model with a horizontal planar motion mechanism experiment

Lin, Yong; Chiu, Yen-Chen

doi:10.1016/j.oceaneng.2022.110847

Cited by 16 publications

(1 citation statement)

References 23 publications

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“…The Hybrid method [ 60 , 61 ] combines the traditional physical methods and the data-driven methods, and not only has theoretical interpretability and application generalization but also has strong environmental adaptability.…”

Section: Key Technologies Of the Uvmsmentioning

confidence: 99%

Underwater Robots and Key Technologies for Operation Control

Sun,

Wang,

Hui

et al. 2024

Cyborg Bionic Syst

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Over time, the utilization of the Underwater Vehicle-Manipulator System (UVMS) has steadily increased in exploring and harnessing marine resources. However, the underwater environment poses big challenges for controlling, navigating, and communicating with UVMS. These challenges have not only spurred the continuous advancement of related technologies, but also made the development of the UVMS even more captivating. This article firstly provides a review of development status of the UVMS and discusses the current limitations and future directions, and then reviews in detail the dynamic and hydrodynamic modeling methods, and analyzes the principles, advantages, and disadvantages of various approaches. Then, we try to review 2 key technologies of operation control methods, including underwater positioning and navigation technologies and vehicle-manipulator coordinated control approaches. Finally, a reasonable prospect for the future development of UVMS is given.

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Section: Key Technologies Of the Uvmsmentioning

confidence: 99%

Underwater Robots and Key Technologies for Operation Control

Sun,

Wang,

Hui

et al. 2024

Cyborg Bionic Syst

View full text Add to dashboard Cite

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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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