Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (4)

Search Parameters:
Keywords = buoy inspection

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
23 pages, 10158 KB  
Article
Navigation and Obstacle Avoidance for USV in Autonomous Buoy Inspection: A Deep Reinforcement Learning Approach
by Jianhui Wang, Zhiqiang Lu, Xunjie Hong, Zeye Wu and Weihua Li
J. Mar. Sci. Eng. 2025, 13(5), 843; https://doi.org/10.3390/jmse13050843 - 24 Apr 2025
Cited by 4 | Viewed by 2117
Abstract
To address the challenges of manual buoy inspection, this study enhances a previously proposed Unmanned Surface Vehicle (USV) inspection system by improving its navigation and obstacle avoidance capabilities using Proximal Policy Optimization (PPO). For improved usability, the entire system adopts a fully end-to-end [...] Read more.
To address the challenges of manual buoy inspection, this study enhances a previously proposed Unmanned Surface Vehicle (USV) inspection system by improving its navigation and obstacle avoidance capabilities using Proximal Policy Optimization (PPO). For improved usability, the entire system adopts a fully end-to-end design, with an angular deviation weighting mechanism for stable circular navigation, a novel image-based radar encoding technique for obstacle perception and a decoupled navigation and obstacle avoidance architecture that splits the complex task into three independently trained modules. Experiments validate that both navigation modules exhibit robustness and generalization capabilities, while the obstacle avoidance module partially achieves International Regulations for Preventing Collisions at Sea (COLREGs)-compliant maneuvers. Further tests in continuous multi-buoy inspection tasks confirm the architecture’s effectiveness in integrating these modules to complete the full task. Full article
(This article belongs to the Special Issue The Control and Navigation of Autonomous Surface Vehicles)
Show Figures

Figure 1

23 pages, 18143 KB  
Article
Design and Testing of an Autonomous Navigation Unmanned Surface Vehicle for Buoy Inspection
by Zhiqiang Lu, Weihua Li, Xinzheng Zhang, Jianhui Wang, Zihao Zhuang and Cheng Liu
J. Mar. Sci. Eng. 2024, 12(5), 819; https://doi.org/10.3390/jmse12050819 - 14 May 2024
Cited by 2 | Viewed by 3436
Abstract
In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware [...] Read more.
In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections. Full article
Show Figures

Figure 1

24 pages, 2009 KB  
Article
Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods
by Rana Muhammad Adnan Ikram, Xinyi Cao, Kulwinder Singh Parmar, Ozgur Kisi, Shamsuddin Shahid and Mohammad Zounemat-Kermani
Mathematics 2023, 11(14), 3141; https://doi.org/10.3390/math11143141 - 16 Jul 2023
Cited by 9 | Viewed by 2116
Abstract
The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour [...] Read more.
The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error, determination coefficient and new graphical inspection methods (e.g., Taylor and violin charts). On the basis of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) statistics, it was observed that GPR provided the best accuracy in predicting short-term single-time-step and multi-time-step significant wave heights. On the basis of mean RMSE, GPR improved the accuracy of M5RT, MARS, PCR, RF and PLSR by 16.63, 8.03, 10.34, 3.25 and 7.78% (first station) and by 14.04, 8.35, 13.34, 3.87 and 8.30% (second station) for the test stage. Full article
Show Figures

Figure 1

15 pages, 2006 KB  
Article
The Impacts of the Application of the Ensemble Optimal Interpolation Method in Global Ocean Wave Data Assimilation
by Mengmeng Wu, Hui Wang, Liying Wan, Juanjuan Wang, Yi Wang and Jiuke Wang
Atmosphere 2023, 14(5), 818; https://doi.org/10.3390/atmos14050818 - 30 Apr 2023
Cited by 4 | Viewed by 2558
Abstract
The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal [...] Read more.
The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China’s offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China’s ocean satellite data in ocean wave data assimilation and prediction. Full article
(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)
Show Figures

Figure 1

Back to TopTop