Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression

Sun, Hongming; Guo, Wei; Lan, Yanjun; Wei, Zhenzhuo; Gao, Sen; Sun, Yi; Fu, Yifan

doi:10.3390/jmse10050575

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Traditional autonomous underwater vehicles (AUV) and remote underwater vehicles cannot accomplish this task [3,4]. Thus, deep-sea landing vehicles (DSLV) have attracted increased attention as underwater autonomous navigation platforms [5].…”

Section: Introductionmentioning

confidence: 99%

Theoretical Analysis Method for Roll Motion of Popup Data Communication Beacons

Song

Chi

Liang

et al. 2023

JMSE

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The popup data communication beacon (PDCB) can send data to the shore and ships through the BeiDou navigation satellite system (BDS) when it surfaces. The data can be collected by a deep-sea landing vehicle (DSLV) and transmitted using a magnetic induction coil. PDCBs can reduce the cost of DSLV recovery and redeployment. Whether the data can be successfully sent mainly depends on the outlet height and roll angle of the PDCB. Thus, accurately assessing the effect of the roll angle on data transmission is crucial. In this study, first, the differential equation of roll motion was preliminarily established using the small-amplitude wave theory along with the shape characteristics of the PDCB. Next, the nonlinear term of the recovery moment was processed using the Linz Ted Poincaré method. Then, the wave current force was analyzed using the Morrison theoretical formula along with an additional inertia moment calculation formula that is suitable for slender cylindrical small buoys. Finally, the theoretical calculation results were verified using the computational fluid dynamics (CFD) method and pool test. The roll angle error of the theoretical calculation was within 5%. Thus, the heave and roll response of PDCBs can be evaluated using theoretical calculation methods. The proposed calculation formula of additional inertia moment has guiding significance for the further optimization of the structure.

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Section: Introductionmentioning

confidence: 99%

Theoretical Analysis Method for Roll Motion of Popup Data Communication Beacons

Song

Chi

Liang

et al. 2023

JMSE

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“…The abundance of marine resources has attracted global attention as resources, energy, and space available on land are gradually diminishing [1,2]. The Deep-Sea Landing Vehicle (DSLV) is an autonomous deep-sea equipment capable of long-term, large-scale, and shortdistance refinement operations in the deep-sea environment [3,4]. There is a huge challenge for DSLV to complete autonomous sampling operations because the forward-looking sonar has a large ranging error caused by-deep sea noise and the battery power of the DSLV is limited.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The detected obstacle volume may be smaller than the actual volume due to the decrease in accuracy [15,16]. (2) The limited energy of DSLV will be depleted more quickly due to the absence of energy consumption-related evaluation sub-functions in the DWA algorithm [3]. To address the above issues, improvements and additional evaluation sub-functions have been introduced to ensure the safety and reduce energy consumption during autonomous sampling operations.…”

Section: Introductionmentioning

confidence: 99%

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Path Planning of Deep-Sea Landing Vehicle Based on the Safety Energy-Dynamic Window Approach Algorithm

Pan,

Guo,

Sun

et al. 2023

JMSE

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To ensure the safety and energy efficiency of autonomous sampling operations for a deep-sea landing vehicle (DSLV), the Safety Energy-Dynamic Window Approach (SE-DWA) algorithm was proposed. The safety assessment sub-function formed from the warning obstacle zone and safety factor addresses the safety issue arising from the excessive range measurement error of forward-looking sonar. The trajectory comparison evaluation sub-function with the effect of reducing energy consumption achieves a reduction in path length by causing the predicted trajectory to deviate from the historical trajectory when encountering “U”-shaped obstacles. The pseudo-power evaluation sub-function with further energy consumption reduction ensures optimal linear and angular velocities by minimizing variables when encountering unknown obstacles. The simulation results demonstrate that compared with the Minimum Energy Consumption-DWA algorithm, the SE-DWA algorithm improves the minimum distance to an actual obstacle zone by 68% while reducing energy consumption by 11%. Both the SE-DWA algorithm and the Maximum Safety-DWA (MS-DWA) algorithm ensure operational safety with minimal distance to the actual obstacle zone, yet the SE-DWA algorithm achieves a 24% decrease in energy consumption. In conclusion, the path planned by the SE-DWA algorithm ensures not only safety but also energy consumption reduction during autonomous sampling operations by a DSLV in the deep sea.

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“…This study proposed a low-energy consumption multipoint sampler carried by a deepsea landing vehicle (DSLV) [18][19][20] to meet the requirements of time series sampling in local areas and location series sampling in wide areas. The multipoint sampler uses the underwater stepping motor to drive its core component, namely the sampling structure, to penetrate the seafloor sediments for sampling.…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of Multipoint Sampler for Seafloor Sediment Carried by a Deep-Sea Landing Vehicle

Gao

Zhou

Guo

et al. 2022

JMSE

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The present study proposes a low-energy consumption multipoint sampler carried by a deep-sea landing vehicle (DSLV) to meet the requirements of time series sampling in local areas and location series sampling in wide areas, and an optimization method of sampling structure based on least-squares support-vector machine (LSSVM) surrogate model and a multi-objective particle swarm optimization (MOPSO) algorithm. First, the overall structure and core components, such as the multipoint sampler’s sampling structure, were designed. The optimization variables were the cone angle, sampling tube inner diameter, and sampling tube inner hole length, which were determined by considering the force with which the sampling structure penetrates the seafloor sediment. Then, the sampling process was simulated by the finite element method-smoothed particle hydrodynamics (FEM-SPH) method, while the accurate LSSVM model of force required for sampling and sampling tube volume was established. Finally, the MOPSO algorithm was used for multi-objective optimization of model parameters of sampling structure. The optimal model of sampling structure that can provide theoretical support for the optimal design of multipoint sampler effectively reduces energy consumption and improves sampling efficiency by force required for sampling 25.89% lower and sampling tube volume 34.81% higher than the original model.

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Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression

Cited by 4 publications

References 18 publications

Theoretical Analysis Method for Roll Motion of Popup Data Communication Beacons

Theoretical Analysis Method for Roll Motion of Popup Data Communication Beacons

Path Planning of Deep-Sea Landing Vehicle Based on the Safety Energy-Dynamic Window Approach Algorithm

Design and Optimization of Multipoint Sampler for Seafloor Sediment Carried by a Deep-Sea Landing Vehicle

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