Ocean Engineering最新文献_第8页

A multi-objective economic nonlinear model predictive controller for power and platform motion on floating offshore wind turbines 海上浮式风力发电机组动力与平台运动多目标经济非线性模型预测控制器

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122888

L. Pustina , F. Biral , E. Bertolazzi , J. Serafini

{"title":"A multi-objective economic nonlinear model predictive controller for power and platform motion on floating offshore wind turbines","authors":"L. Pustina , F. Biral , E. Bertolazzi , J. Serafini","doi":"10.1016/j.oceaneng.2025.122888","DOIUrl":"10.1016/j.oceaneng.2025.122888","url":null,"abstract":"<div><div>An Economic Nonlinear Model Predictive Controller is developed to maximize power and reduce fore-aft motion of floating wind turbines compared to a standard power controller. A nonlinear reduced-order model of floating turbines is developed to predict platform motion, rotor thrust, aerodynamic power, and generator temperature. A grey-box approach and a black-box approach to platform modeling are validated and compared. The model is used for the synthesis of ENMPC that determines the optimal generator torque and pitch angle over a future time horizon. The objective of this optimization is a combination of aerodynamic power and fore-aft nacelle velocity under realistic constraints. The controller’s performance and robustness are assessed using a wide set of realistic wind and sea conditions. Significantly higher power production and lower fore-aft platform motion are achieved by adopting the multi-objective ENMPC. Finally, considering the difficulty in predicting the sea diffraction forces and the incoming wind, the performances are positively verified in the absence of that information. The main drawback of the multi-objective controller is the increase of fatigue loads when it is requested to minimize the platform fore-aft motion due to the use of thrust to control it.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122888"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formation control and intention compensating of AUVs using multi-agent reinforcement learning and predict network 基于多智能体强化学习与预测网络的auv群体控制与意向补偿

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122854

Mengqi Wang , Rongshun Juan , Zezhong Li , Zhongke Gao

{"title":"Formation control and intention compensating of AUVs using multi-agent reinforcement learning and predict network","authors":"Mengqi Wang , Rongshun Juan , Zezhong Li , Zhongke Gao","doi":"10.1016/j.oceaneng.2025.122854","DOIUrl":"10.1016/j.oceaneng.2025.122854","url":null,"abstract":"<div><div>Autonomous Underwater Vehicles (AUVs) have played an important role in numerous marine tasks, such as resource exploration, hydrological data acquisition, rescue operations, and military missions. In contrast to single AUV deployment, multi-AUV formations exhibit higher efficiency and improved task completion rates. Recently, multi-agent reinforcement learning (MARL) has emerged as a promising technique for AUV formation control. Nevertheless, conventional MARL approaches often suffer from instability in formation shapes, especially when managing a large number of AUVs. Additionally, communication delay and information dropout can further compromise formation performance. In this paper, we propose a novel method called Policy Compensate Multi-agent Twin Delayed Deep Deterministic Policy Gradient (PC-MATD3), which integrates imitation learning (IL) with MARL to improve formation stability. The proposed framework is designed to alleviate adverse effects caused by communication interruptions or information delays. We define distance and angular errors as key performance metrics and evaluate our method through two distinct simulation scenarios. Experimental results show that, under ideal communication conditions, our approach substantially reduces formation errors and improves overall stability. Additionally, in scenarios involving communication dropouts, the proposed method effectively predicts the positions of neighboring AUVs, enabling the restoration of the desired formation geometry.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122854"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Environmental contour correction using Bayesian inference for areas with limited metocean data 基于贝叶斯推理的海洋气象数据有限地区环境等高线校正

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.123000

Kyungrok Kwon , Jinhyuk Lee , Yangrok Choi , Jong Gyun Paik , Youngjin Choi , Jung-Sik Kong

{"title":"Environmental contour correction using Bayesian inference for areas with limited metocean data","authors":"Kyungrok Kwon , Jinhyuk Lee , Yangrok Choi , Jong Gyun Paik , Youngjin Choi , Jung-Sik Kong","doi":"10.1016/j.oceaneng.2025.123000","DOIUrl":"10.1016/j.oceaneng.2025.123000","url":null,"abstract":"<div><div>Wind speed–wave height contours are crucial for evaluating the extreme metocean conditions of offshore wind structures. To construct reliable contours, long-term buoy data are essential. However, in Korea, the limited observation period of simultaneous wind and wave data poses a challenge, resulting in low reliability in estimating extreme environmental loads. Therefore, in this study, we proposed a method for refining the distribution of metocean data using Bayesian inference. Our comparison of extreme metocean conditions based on different observation periods revealed significant variations in the estimated conditions over short observation periods. To address this issue, the wave buoy data distribution was defined as a prior distribution, and the wave height distribution for the target region was corrected using a Bayesian inference approach. In addition, the wind speed distribution was improved by considering the correlation between wind speed and wave height. Subsequently, extreme metocean conditions were evaluated using the environmental contour approach based on the IFORM method. The results confirmed that the distribution of metocean data was improved, allowing for the derivation of more reliable extreme environmental loads than with conventional environmental contours. Therefore, the methodology presented in this study can be applied for constructing reasonable and reliable environmental contours, even when observation periods are limited.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 123000"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement learning-based automated target motion analysis in underwater environments 基于强化学习的水下目标运动自动分析

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122946

Seunghwan Jang , Junha Shin , Dasol Kim , Juhyun Lee , Hyunsuk Ko

{"title":"Reinforcement learning-based automated target motion analysis in underwater environments","authors":"Seunghwan Jang , Junha Shin , Dasol Kim , Juhyun Lee , Hyunsuk Ko","doi":"10.1016/j.oceaneng.2025.122946","DOIUrl":"10.1016/j.oceaneng.2025.122946","url":null,"abstract":"<div><div>This study presents an automated target motion analysis (TMA) framework that leverages deep reinforcement learning (DRL) to enhance the accuracy and reliability of target state estimation from SONAR-derived bearing-only measurements in underwater environments. Traditional TMA methods-such as the manual 10-point divider and batch estimation-rely heavily on operator expertise and are susceptible to inaccuracies due to environmental noise and human error. To address these limitations, we employ a Proximal Policy Optimization (PPO)-based agent to automatically and robustly estimate the target speed. A customized TMA simulator was developed to generate diverse underwater scenarios, incorporating variations in target motion and noise levels to ensure the model’s generalization capability. The PPO agent learns to infer target speed directly from sequential bearing data, achieving a strong balance between exploration and exploitation. Experimental results demonstrate that the trained agent provides highly accurate and robust speed estimates, even under realistic noise conditions. This work contributes to the advancement of autonomous maritime surveillance and defense systems by significantly reducing human dependency and improving operational reliability.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122946"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of drag-reducing additives on particle collection performance in hydraulic collectors 减阻添加剂对液压集尘器颗粒收集性能的影响

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.123061

Xian Zhang , Qingqing Lu , Xuguang Chen , Chaoqun Liu , Shenpeng Tian , Xixi Liu

{"title":"Effect of drag-reducing additives on particle collection performance in hydraulic collectors","authors":"Xian Zhang , Qingqing Lu , Xuguang Chen , Chaoqun Liu , Shenpeng Tian , Xixi Liu","doi":"10.1016/j.oceaneng.2025.123061","DOIUrl":"10.1016/j.oceaneng.2025.123061","url":null,"abstract":"<div><div>In hydraulic collectors, the properties of the jet fluid play a crucial role in both operational efficiency and nodule collection performance. To explore the relationship between fluid characteristics and the transport behavior of nodule particles, this study introduces a surfactant as a drag-reducing additives (DRA) into the jet fluid and investigates its effects on particle detachment and collection performance. The results show that under the same jet conditions, adding a DRA significantly reduces nodule collection time, and a DRA concentration of 1000 mg/L is a suitable choice based on overall considerations. Under the coupled effect of jet velocity and travel velocity, DRA enhances jet energy to improve nodule detachment efficiency and effectively reduces nodule aggregation caused by vortices, facilitating collection. In addition, the addition of DRA to the jet does not significantly promote the suspension of sediment particles. Finally, a neural network model optimized by a genetic algorithm is employed to further predict the nodule collection performance under DRA jetting conditions, providing a scientific basis for efficient and intelligent deep-sea mining.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 123061"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the reliability of marine pipeline transportation systems: A flow safety monitoring method for sand-carrying churn flows via multi-migration collision behavioral responses 提高海上管道运输系统的可靠性：基于多迁移碰撞行为响应的携沙搅拌流流动安全监测方法

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122942

Kai Wang , Jiaqi Tian , Peng Cai , Zhiyuan Wang , Ziang Chang , Jiaqi Lu , Zibiao Wang , Yi Lv , Botao Gou , Yunpeng He

{"title":"Enhancing the reliability of marine pipeline transportation systems: A flow safety monitoring method for sand-carrying churn flows via multi-migration collision behavioral responses","authors":"Kai Wang , Jiaqi Tian , Peng Cai , Zhiyuan Wang , Ziang Chang , Jiaqi Lu , Zibiao Wang , Yi Lv , Botao Gou , Yunpeng He","doi":"10.1016/j.oceaneng.2025.122942","DOIUrl":"10.1016/j.oceaneng.2025.122942","url":null,"abstract":"<div><div>Marine pipeline transportation systems frequently encounter sand-carrying churn flows, wherein persistent sand particle-wall collisions lead to structural degradation of pipelines. This paper proposes a flow safety monitoring method for sand-carrying churn flows based on multi-migration collision behavior responses. Based on the Robust Empirical Mode Decomposition (REMD) algorithm, this study first establishes a multi-frequency scale vibration response characterization method of sand particles for sand-carrying churn flow. Then, a lightweight deep learning architecture based on Depthwise Separable Convolution (DSC) is constructed, achieving an average recognition accuracy of 87.17 % for sand features with contents ranging from 0g to 20g (in 5g increments) across three distinct datasets. Furthermore, the Bidirectional Long Short-Term Memory (BiLSTM) module and Self-Adaptive Temporal Transformer (SATT) module into the DSC framework, thereby enhancing bidirectional full-sequence time-delay feature extraction capability and adaptive weight-matching capacity for holistic particle characteristic information. The DSC-BiLSTM-SATT recognition model improves the average recognition accuracy by 8 %, achieving a final accuracy of 95.17 %. The model shows excellent generalization capability even on low signal-to-noise ratio (SNR) datasets, and the average recognition accuracy for three low SNR datasets reaches 89.73 %. The framework with high accuracy significantly contributes to improve the flow safety and reliability of marine pipeline transportation systems.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122942"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A behavior-informed adaptive algorithm for hierarchical compression of ship trajectories 基于行为的船舶轨迹分层压缩自适应算法

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122999

Hongfeng Chen, Dechang Pi

{"title":"A behavior-informed adaptive algorithm for hierarchical compression of ship trajectories","authors":"Hongfeng Chen, Dechang Pi","doi":"10.1016/j.oceaneng.2025.122999","DOIUrl":"10.1016/j.oceaneng.2025.122999","url":null,"abstract":"<div><div>The Automatic Identification System (AIS) plays a pivotal role in maritime monitoring, yet its high-frequency data often cause redundancy, affecting storage and downstream analysis. Existing compression algorithms often fail to capture vessel behavior semantics, making it difficult to balance compression rate and semantic preservation in complex scenarios. To address this, we propose a behavior-informed adaptive framework for hierarchical trajectory compression. The framework integrates stay region identification, behavior-oriented segmentation, and multi-feature adaptive compression, enabling differentiated compression across various navigation phases. Stay regions are identified using motion features and spatial density. Navigational behavior patterns are constructed from course sequences, and segmentation is performed using a combination of discrete wavelet transform and entropy-based techniques. Furthermore, introduce multi-dimensional deviation factors and trajectory bending factors, while dynamically setting the compression threshold through a baseline scale adjustment mechanism. In experiments across three representative maritime regions, our method achieves an average compression rate of 78.4 % with a mean spatial error of only 57.8 m, while also maintaining low speed error (0.154 kn) and course error (26.9°). Compared with the benchmark and six advanced algorithms, it consistently delivers the best overall performance, and tests on four typical trajectories further validate its adaptability and robustness.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122999"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A ship trajectory prediction model integrating ship-shore speech communication for early prediction at waterway intersections 一种融合船岸语音通信的船舶轨迹预测模型，用于航道交叉口的早期预测

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122934

Yang Chen , Xucun Qi , Dong Yang , Changhai Huang , Jian Zheng

{"title":"A ship trajectory prediction model integrating ship-shore speech communication for early prediction at waterway intersections","authors":"Yang Chen , Xucun Qi , Dong Yang , Changhai Huang , Jian Zheng","doi":"10.1016/j.oceaneng.2025.122934","DOIUrl":"10.1016/j.oceaneng.2025.122934","url":null,"abstract":"<div><div>Early ship trajectory prediction improves traffic coordination but increases the risk of intent misjudgment at waterway intersections, leading to deviations between predicted and actual trajectories. To address this, we propose a ship trajectory prediction model grounded in the International Maritime Organization (IMO) framework and the rule of “intent report - ship maneuver - trajectory change” observed in real-world waterway intersections. Our method enables early intent recognition by leveraging intent information embedded in ship-shore speech communication. Within a defined spatiotemporal range, we associate communication data with observed trajectories to identify reported intentions. The extracted intent labels are integrated with encoded historical trajectory features and fed into a decoder, dynamically constraining predicted directions. This alignment with reported intent advances the prediction timeline without compromising accuracy. Empirical validation at the Wusongkou Estuary (Shanghai, China) demonstrates that our model advances the prediction timeline by 6.94–8.4 min compared to existing models, while maintaining similar accuracy. This work pioneers the integration of ship-shore speech communication into trajectory prediction, highlighting the potential of AI-driven maritime safety systems.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122934"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A direct FEA loading approach for combined failure envelope of foundations in cohesive soil 粘性土中地基复合破坏包络的直接有限元加载方法

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122962

Si-Da Wu, Zhen-Yu Yin, Maozhu Peng

{"title":"A direct FEA loading approach for combined failure envelope of foundations in cohesive soil","authors":"Si-Da Wu, Zhen-Yu Yin, Maozhu Peng","doi":"10.1016/j.oceaneng.2025.122962","DOIUrl":"10.1016/j.oceaneng.2025.122962","url":null,"abstract":"<div><div>Foundations, particularly offshore foundations, are subjected to multidirectional combined loading, making accurate failure envelopes essential for geotechnical design. Traditional methods for constructing these envelopes face significant challenges. This study presents a Direct Displacement Swipe (DDS) method that indirectly steers the displacement-space trajectory to conform to a prescribed load-space trajectory, implemented within a Finite Element Analysis (FEA) framework to improve accuracy and computational efficiency. Different from conventional swipe method, a truss element is used to simulate the rope, with one end connecting to the foundation and the other end for loading, resulting in the same direction of displacement and load. Validation across various types of foundations, including circular surface footings, suction caissons, tripod buckets, and composite pile-bucket foundations, covering a range of shallow to deep foundation categories, under diverse cohesive soil conditions, highlights its robustness. Comparative analysis shows the DDS method matches the accuracy of traditional approaches while significantly reducing computational costs. Additionally, it effectively captures both symmetrical and asymmetrical failure envelopes, where traditional methods often fall short. Therefore, the DDS method emerges as a practical, efficient, and reliable alternative for geotechnical design.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122962"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on AUV underwater path planning based on preference-driven interval multi-objective optimization algorithm 基于偏好驱动区间多目标优化算法的AUV水下路径规划研究

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122923

Chengchang Tong, Yixiang Wang, Weizhe Zhang, Hongbo Wang

{"title":"Research on AUV underwater path planning based on preference-driven interval multi-objective optimization algorithm","authors":"Chengchang Tong, Yixiang Wang, Weizhe Zhang, Hongbo Wang","doi":"10.1016/j.oceaneng.2025.122923","DOIUrl":"10.1016/j.oceaneng.2025.122923","url":null,"abstract":"<div><div>This study investigates the autonomous underwater vehicle (AUV) path-planning problem in complex and uncertain marine environments. Considering various factors such as dynamic ocean currents, terrain complexity, and the uncertainty of hazardous locations, an interval number approach is employed to model ocean current parameters and uncertain hazardous areas, thereby transforming uncertainties into interval constraints and formulating an interval multiobjective optimization problem. Based on this framework, the preference interval multiobjective particle swarm optimization algorithm (P-IMO-PSO) is proposed. The proposed algorithm integrates the decisionmaker’s preference information to guide the optimization process. This approach balances navigation time, path safety, and energy consumption while improving iteration efficiency. The results of MATLAB simulation experiments validate the performance of the proposed algorithm under different ocean current models and uncertain environmental conditions. The results show that, compared with the traditional IMO-PSO algorithm, the proposed P-IMO-PSO significantly enhances path-planning efficiency by reducing the mean navigation time interval by 20.85 %, while also optimizing navigation time, mitigating randomness, and accelerating convergence In addition, the paths generated by the proposed algorithm align better with decision-maker (DM) preferences, leveraging ocean currents advantageously while ensuring safety, thereby enhancing AUV navigation efficiency. These advantages highlight the superior applicability and robustness of the proposed method in complex underwater environments.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122923"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0