Journal of Marine Science and Engineering

Research

29 pages, 8441 KiB

Open AccessArticle

Optimizing Multi-Vessel Collision Avoidance Decision Making for Autonomous Surface Vessels: A COLREGs-Compliant Deep Reinforcement Learning Approach

by Weidong Xie, Longhui Gang, Mingheng Zhang, Tong Liu and Zhixun Lan

J. Mar. Sci. Eng. 2024, 12(3), 372; https://doi.org/10.3390/jmse12030372 - 22 Feb 2024

Cited by 11 | Viewed by 2776

Abstract

Automatic collision avoidance decision making for vessels is a critical challenge in the development of autonomous ships and has become a central point of research in the maritime safety domain. Effective and systematic collision avoidance strategies significantly reduce the risk of vessel collisions, [...] Read more.

Automatic collision avoidance decision making for vessels is a critical challenge in the development of autonomous ships and has become a central point of research in the maritime safety domain. Effective and systematic collision avoidance strategies significantly reduce the risk of vessel collisions, ensuring safe navigation. This study develops a multi-vessel automatic collision avoidance decision-making method based on deep reinforcement learning (DRL) and establishes a vessel behavior decision model. When designing the reward function for continuous action spaces, the criteria of the “Convention on the International Regulations for Preventing Collisions at Sea” (COLREGs) were adhered to, taking into account the vessel’s collision risk under various encounter situations, real-world navigation practices, and navigational complexities. Furthermore, to enable the algorithm to precisely differentiate between collision avoidance and the navigation resumption phase in varied vessel encounter situations, this paper incorporated “collision avoidance decision making” and “course recovery decision making” as state parameters in the state set design, from which the respective objective functions were defined. To further enhance the algorithm’s performance, techniques such as behavior cloning, residual networks, and CPU-GPU dual-core parallel processing modules were integrated. Through simulation experiments in the enhanced Imazu training environment, the practicality of the method, taking into account the effects of wind and ocean currents, was corroborated. The results demonstrate that the proposed algorithm can perform effective collision avoidance decision making in a range of vessel encounter situations, indicating its efficiency and robust generalization capabilities. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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20 pages, 5062 KiB

Open AccessArticle

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

by He Huang, Xialu Wen, Mingbo Niu, Md Sipon Miah, Huifeng Wang and Tao Gao

J. Mar. Sci. Eng. 2024, 12(1), 88; https://doi.org/10.3390/jmse12010088 - 1 Jan 2024

Cited by 7 | Viewed by 1930

Abstract

Efficient navigation of multiple autonomous underwater vehicles (AUVs) plays an important role in monitoring underwater and off-shore environments. It has encountered challenges when AUVs work in complex underwater environments. Traditional swarm intelligence (SI) optimization algorithms have limitations such as insufficient path exploration ability, [...] Read more.

Efficient navigation of multiple autonomous underwater vehicles (AUVs) plays an important role in monitoring underwater and off-shore environments. It has encountered challenges when AUVs work in complex underwater environments. Traditional swarm intelligence (SI) optimization algorithms have limitations such as insufficient path exploration ability, susceptibility to local optima, and difficulty in convergence. To address these issues, we propose an improved multi-objective manta ray foraging optimization (IMMRFO) method, which can improve the accuracy of trajectory planning through a comprehensive three-stage approach. Firstly, basic model sets are established, including a three-dimensional ocean terrain model, a threat source model, the physical constraints of AUV, path smoothing constraints, and spatiotemporal coordination constraints. Secondly, an innovative chaotic mapping technique is introduced to initialize the position of the manta ray population. Moreover, an adaptive rolling factor “S” is introduced from the manta rays’ rolling foraging. This allows the collaborative-vehicle population to jump out of local optima through “collaborative rolling." In the processes of manta ray chain feeding and manta ray spiral feeding, Cauchy reverse learning is integrated to broaden the search space and enhance the global optimization ability. The optimal Pareto front is then obtained using non-dominated sorting. Finally, the position of the manta ray population is mapped to the spatial positions of multi-AUVs, and cubic spline functions are used to optimize the trajectory of multi-AUVs. Through detailed analysis and comparison with five existing multi-objective optimization algorithms, it is found that the IMMRFO algorithm proposed in this paper can significantly reduce the average planned path length by 3.1~9.18 km in the path length target and reduce the average cost by 18.34~321.872 in the cost target. In an actual off-shore measurement process, IMMRFO enables AUVs to effectively bypass obstacles and threat sources, reduce risk costs, and improve mobile surveillance safety. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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14 pages, 3917 KiB

Open AccessArticle

Research on Obstacle Avoidance Planning for UUV Based on A3C Algorithm

by Hongjian Wang, Wei Gao, Zhao Wang, Kai Zhang, Jingfei Ren, Lihui Deng and Shanshan He

J. Mar. Sci. Eng. 2024, 12(1), 63; https://doi.org/10.3390/jmse12010063 - 26 Dec 2023

Cited by 4 | Viewed by 1663

Abstract

Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and [...] Read more.

Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and improve training efficiency by synchronously training Actor-Critic in multiple threads. Inspired by the excellent performance of the A3C algorithm, this paper uses the A3C algorithm to solve the UUV (Unmanned Underwater Vehicle) collision avoidance planning problem in unknown environments. This collision avoidance planning algorithm can have the ability to plan in real-time while ensuring a shorter path length, and the output action space can meet the kinematic constraints of UUVs. In response to the problem of UUV collision avoidance planning, this paper designs the state space, action space, and reward function. The simulation results show that the A3C collision avoidance planning algorithm can guide a UUV to avoid obstacles and reach the preset target point. The path planned by this algorithm meets the heading constraints of the UUV, and the planning time is short, which can meet the requirements of real-time planning. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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24 pages, 5304 KiB

Open AccessArticle

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

by Xiuling Wang, Yong Yin and Qianfeng Jing

J. Mar. Sci. Eng. 2023, 11(12), 2320; https://doi.org/10.3390/jmse11122320 - 7 Dec 2023

Cited by 4 | Viewed by 1835

Abstract

Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover [...] Read more.

Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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25 pages, 11069 KiB

Open AccessArticle

AUV Collision Avoidance Planning Method Based on Deep Deterministic Policy Gradient

by Jianya Yuan, Mengxue Han, Hongjian Wang, Bo Zhong, Wei Gao and Dan Yu

J. Mar. Sci. Eng. 2023, 11(12), 2258; https://doi.org/10.3390/jmse11122258 - 29 Nov 2023

Cited by 2 | Viewed by 1958

Abstract

Collision avoidance planning has always been a hot and important issue in the field of unmanned aircraft research. In this article, we describe an online collision avoidance planning algorithm for autonomous underwater vehicle (AUV) autonomous navigation, which relies on its own active sonar [...] Read more.

Collision avoidance planning has always been a hot and important issue in the field of unmanned aircraft research. In this article, we describe an online collision avoidance planning algorithm for autonomous underwater vehicle (AUV) autonomous navigation, which relies on its own active sonar sensor to detect obstacles. The improved particle swarm optimization (I-PSO) algorithm is used to complete the path planning of the AUV under the known environment, and we use it as a benchmark to improve the fitness function and inertia weight of the algorithm. Traditional path-planning algorithms rely on accurate environment maps, where re-adapting the generated path can be highly demanding in terms of computational cost. We propose a deep reinforcement learning (DRL) algorithm based on collision avoidance tasks. The algorithm discussed in this paper takes into account the relative position of the target point and the rate of heading change from the previous timestep. Its reward function considers the target point, running time and turning angle at the same time. Compared with the LSTM structure, the Gated Recurrent Unit (GRU) network has fewer parameters, which helps to save training time. A series of simulation results show that the proposed deep deterministic policy gradient (DDPG) algorithm can obtain excellent results in simple and complex environments. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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32 pages, 8120 KiB

Open AccessArticle

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

by Xi Lyu, Yushan Sun, Lifeng Wang, Jiehui Tan and Liwen Zhang

J. Mar. Sci. Eng. 2023, 11(9), 1796; https://doi.org/10.3390/jmse11091796 - 14 Sep 2023

Cited by 5 | Viewed by 2103

Abstract

This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-based end-to-end perception–planning–execution method to overcome the challenges [...] Read more.

This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-based end-to-end perception–planning–execution method to overcome the challenges associated with training and learning in end-to-end approaches that directly output control forces. In this approach, the state set is established based on the environment information, the action set is established based on the motion characteristics of the AUV, and the control execution force set is established based on the control constraints. The mapping relations between each set are trained using deep reinforcement learning, enabling the AUV to perform the corresponding action in the current state, thereby accomplishing tasks in an end-to-end manner. Furthermore, we introduce the hindsight experience replay (HER) method in the perception planning mapping process to enhance stability and sample efficiency during training. Finally, we conduct simulation experiments encompassing planning, execution, and end-to-end performance evaluation. Simulation training demonstrates that our proposed method exhibits improved decision-making capabilities and real-time obstacle avoidance during planning. Compared to global planning, the end-to-end algorithm comprehensively considers constraints in the AUV planning process, resulting in more realistic AUV actions that are gentler and more stable, leading to controlled tracking errors. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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18 pages, 5009 KiB

Open AccessArticle

Research on Ship Trajectory Prediction Method Based on Difference Long Short-Term Memory

by Xiaobin Tian and Yongfeng Suo

J. Mar. Sci. Eng. 2023, 11(9), 1731; https://doi.org/10.3390/jmse11091731 - 1 Sep 2023

Cited by 9 | Viewed by 2565

Abstract

This study proposes a solution to the problem of inaccurate and time-consuming ship trajectory prediction caused by frequent ship maneuvering in complex waterways. The proposed solution is a ship trajectory prediction model that uses a difference long short-term memory neural network (D-LSTM). To [...] Read more.

This study proposes a solution to the problem of inaccurate and time-consuming ship trajectory prediction caused by frequent ship maneuvering in complex waterways. The proposed solution is a ship trajectory prediction model that uses a difference long short-term memory neural network (D-LSTM). To improve prediction performance and reduce time dependence, the model combines the other variables of dynamic time features in the ship’s Automatic Identification System (AIS) data with nonlinear elements in the sequence data. The effectiveness of this method is demonstrated by comparing its accuracy to other commonly used time series modeling techniques. The results show that the proposed model significantly reduces training time and improves prediction accuracy. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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17 pages, 4578 KiB

Open AccessFeature PaperArticle

A Benchmark for Maritime Object Detection with Centernet on an Improved Dataset, ABOships-PLUS

by Bogdan Iancu, Jesper Winsten, Valentin Soloviev and Johan Lilius

J. Mar. Sci. Eng. 2023, 11(9), 1638; https://doi.org/10.3390/jmse11091638 - 22 Aug 2023

Cited by 6 | Viewed by 2617

Abstract

Object detection from waterborne imagery is an essential aspect in maritime traffic management, navigation safety and coastal security. Building efficient autonomous systems, which can take decisions in critical situations, requires an integration of complex object detectors in real time. Object detectors trained on [...] Read more.

Object detection from waterborne imagery is an essential aspect in maritime traffic management, navigation safety and coastal security. Building efficient autonomous systems, which can take decisions in critical situations, requires an integration of complex object detectors in real time. Object detectors trained on generic datasets often give unsatisfactory results in complex scenarios like the maritime environment, since only a fraction of their images contain maritime vessels. Publicly available domain-specific datasets are scarce, and they are limited in the number of images and annotations. Compared to object detection in applications such as autonomous vehicles, maritime vessel detection is considerably reduced in computer vision research. This creates a deficit in exhaustive benchmarking studies for maritime detection datasets. To bridge this gap, we relabel the ABOships dataset and benchmark a state-of-the-art center-based detector, Centernet, on the newly relabeled dataset, ABOships-PLUS. We explore its performance under different feature extractors, and investigate the effect of object size and inter-class variation on detection accuracy. The reported benchmarking illustrates that the ABOships-PLUS dataset is adequate to use in supervised domain adaptation. The experimental results show that Centernet with DLA (Deep Layer Aggregation) as a feature extractor achieved the highest accuracy in detecting maritime objects overall (with mean average precision 74.4%). Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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21 pages, 24210 KiB

Open AccessArticle

Target Tracking from Weak Acoustic Signals in an Underwater Environment Using a Deep Segmentation Network

by Won Shin, Da-Sol Kim and Hyunsuk Ko

J. Mar. Sci. Eng. 2023, 11(8), 1584; https://doi.org/10.3390/jmse11081584 - 12 Aug 2023

Cited by 4 | Viewed by 1997

Abstract

In submarine warfare systems, passive SONAR is commonly used to detect enemy targets while concealing one’s own submarine. The bearing information of a target obtained from passive SONAR can be accumulated over time and visually represented as a two-dimensional image known as a [...] Read more.

In submarine warfare systems, passive SONAR is commonly used to detect enemy targets while concealing one’s own submarine. The bearing information of a target obtained from passive SONAR can be accumulated over time and visually represented as a two-dimensional image known as a BTR image. Accurate measurement of bearing–time information is crucial in obtaining precise information on enemy targets. However, due to various underwater environmental noises, signal reception rates are low, which makes it challenging to detect the directional angle of enemy targets from noisy BTR images. In this paper, we propose a deep-learning-based segmentation network for BTR images to improve the accuracy of enemy detection in underwater environments. Specifically, we utilized the spatial convolutional layer to effectively extract target objects. Additionally, we propose novel loss functions for network training to resolve a strong class imbalance problem observed in BTR images. In addition, due to the difficulty of obtaining actual target bearing data as military information, we created a synthesized BTR dataset that simulates various underwater scenarios. We conducted comprehensive experiments and related discussions using our synthesized BTR dataset, which demonstrate that the proposed network provides superior target segmentation performance compared to state-of-the-art methods. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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16 pages, 6515 KiB

Open AccessArticle

Research on Multi-Target Path Planning for UUV Based on Estimated Path Cost

by Shuai Zhou, Zheng Wang, Longmei Li and Houpu Li

J. Mar. Sci. Eng. 2023, 11(8), 1582; https://doi.org/10.3390/jmse11081582 - 12 Aug 2023

Viewed by 1465

Abstract

The precision and efficiency of multi-target path planning are crucial factors influencing the performance of anti-mine operations using unmanned underwater vehicles (UUVs). Addressing the inadequacies in computation time and solution quality present in existing path planning algorithms, this study proposes a novel path [...] Read more.

The precision and efficiency of multi-target path planning are crucial factors influencing the performance of anti-mine operations using unmanned underwater vehicles (UUVs). Addressing the inadequacies in computation time and solution quality present in existing path planning algorithms, this study proposes a novel path cost estimation strategy based on neural networks. This strategy swiftly generates an accurate cost matrix, ensuring the attainment of high-quality traversal orders when utilized as input for the traveling salesman problem, thereby yielding a globally optimal path. Simulation experiments demonstrate that while maintaining high-quality solutions, the proposed strategy significantly enhances the computational efficiency of the algorithm. Furthermore, the practical application and effectiveness of the proposed algorithm have been demonstrated through an actual UUV prototype experiment in a lake environment. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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25 pages, 12038 KiB

Open AccessArticle

Autonomous Navigation Decision-Making Method for a Smart Marine Surface Vessel Based on an Improved Soft Actor–Critic Algorithm

by Zhewen Cui, Wei Guan, Xianku Zhang and Cheng Zhang

J. Mar. Sci. Eng. 2023, 11(8), 1554; https://doi.org/10.3390/jmse11081554 - 5 Aug 2023

Cited by 6 | Viewed by 2539

Abstract

In this study, an intelligent hybrid algorithm based on deep-reinforcement learning (DRL) is proposed to achieve autonomous navigation and intelligent collision avoidance for a smart autonomous marine surface vessel (SMASV). First, the kinematic model of the SMASV is used, and clauses 13 to [...] Read more.

In this study, an intelligent hybrid algorithm based on deep-reinforcement learning (DRL) is proposed to achieve autonomous navigation and intelligent collision avoidance for a smart autonomous marine surface vessel (SMASV). First, the kinematic model of the SMASV is used, and clauses 13 to 17 of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are introduced. Then, the electronic chart is rasterized and used for path planning. Next, states, actions, and reward functions are designed, and collision avoidance strategies are formulated. In addition, a temperature factor and a constrained loss function are used to improve the soft actor–critic (SAC) algorithm. This improvement reduces the challenges of hyperparameter adjustment and improves sampling efficiency. By comparing the improved SAC algorithm with other deep-reinforcement learning (DRL) algorithms based on strategy learning, it is proved that the improved SAC algorithm converges faster than the other algorithms. During the experiment, some unknown obstacles are added to the simulation environment to verify the collision-avoidance ability of the trained SMASV. Moreover, eight sea areas are randomly selected to verify the generalization ability of the intelligent-navigation system. The results show that the proposed method can plan a path for the SMASV accurately and effectively, and the SMASV decision-making behavior in the collision-avoidance process conforms to the COLREGs in both unknown and dynamic environments. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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20 pages, 8881 KiB

Open AccessArticle

Illumination Adaptive Multi-Scale Water Surface Object Detection with Intrinsic Decomposition Augmentation

by Zhiguo Zhou, Zeming Li, Jiaen Sun, Limei Xu and Xuehua Zhou

J. Mar. Sci. Eng. 2023, 11(8), 1485; https://doi.org/10.3390/jmse11081485 - 25 Jul 2023

Cited by 3 | Viewed by 1643

Abstract

Visual object detection is an essential task for the intelligent navigation of an Unmanned Surface Vehicle (USV), which can sense the obstacles while navigating. However, the harsh illumination conditions and large scale variation of the objects significantly harm the performance of object detection [...] Read more.

Visual object detection is an essential task for the intelligent navigation of an Unmanned Surface Vehicle (USV), which can sense the obstacles while navigating. However, the harsh illumination conditions and large scale variation of the objects significantly harm the performance of object detection methods. To address the above problems, we propose a robust water surface object detection method named multi-scale feature fusion network with intrinsic decomposition generative adversarial network data augmentation (MFFDet-IDGAN). We introduce intrinsic decomposition as data augmentation for the object detection to achieve illumination adapting. And an intrinsic decomposition generative adversarial network (IDGAN) is proposed to achieve unsupervised intrinsic decomposition. Moreover, the multi-scale feature fusion network (MFFDet) adopts an improved bidirectional feature pyramid network (BiFPN) and spatial pyramid pooling (SPP) blocks to fuse features of different resolution for better multi-scale detection. And an improved weighted stochastic weight averaging (SWA) is proposed and applied in the training process to improve the generalization performance. We conduct extensive experiments on the Water Surface Object Detection Dataset (WSODD), and the results show that the proposed method can achieve 44% improvement over the baseline. And we further test our method on a real USV in the sailing process, the results show that our method can exceeding the baseline by 4.5%. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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25 pages, 7496 KiB

Open AccessArticle

Autonomous Obstacle Avoidance in Crowded Ocean Environment Based on COLREGs and POND

by Xiao Peng, Fenglei Han, Guihua Xia, Wangyuan Zhao and Yiming Zhao

J. Mar. Sci. Eng. 2023, 11(7), 1320; https://doi.org/10.3390/jmse11071320 - 28 Jun 2023

Cited by 3 | Viewed by 1849

Abstract

In crowded waters with unknown obstacle motion information, traditional methods often fail to ensure safe and autonomous collision avoidance. To address the challenges of information acquisition and decision delay, this study proposes an optimized autonomous navigation strategy that combines deep reinforcement learning with [...] Read more.

In crowded waters with unknown obstacle motion information, traditional methods often fail to ensure safe and autonomous collision avoidance. To address the challenges of information acquisition and decision delay, this study proposes an optimized autonomous navigation strategy that combines deep reinforcement learning with internal and external rewards. By incorporating random network distillation (RND) with proximal policy optimization (PPO), the interest of autonomous ships in exploring unknown environments is enhanced. Additionally, the proposed approach enables the autonomous generation of intrinsic reward signals for actions. For multi-ship collision avoidance scenarios, an environmental reward is designed based on the International Regulations for Preventing Collision at Sea (COLREGs). This reward system categorizes dynamic obstacles into four collision avoidance situations. The experimental results demonstrate that the proposed algorithm outperforms the popular PPO algorithm by achieving more efficient and safe collision avoidance decision-making in crowded ocean environments with unknown motion information. This research provides a theoretical foundation and serves as a methodological reference for the route deployment of autonomous ships. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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17 pages, 5227 KiB

Open AccessArticle

Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles

by Sarada Prasanna Sahoo, Bikramaditya Das, Bibhuti Bhusan Pati, Fausto Pedro Garcia Marquez and Isaac Segovia Ramirez

J. Mar. Sci. Eng. 2023, 11(4), 761; https://doi.org/10.3390/jmse11040761 - 31 Mar 2023

Cited by 24 | Viewed by 3093

Abstract

This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic [...] Read more.

This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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16 pages, 4472 KiB

Open AccessArticle

A Novel Intelligent Detection Algorithm of Aids to Navigation Based on Improved YOLOv4

by Rong Zhen, Yingdong Ye, Xinqiang Chen and Liangkun Xu

J. Mar. Sci. Eng. 2023, 11(2), 452; https://doi.org/10.3390/jmse11020452 - 18 Feb 2023

Cited by 6 | Viewed by 1987

Abstract

Aiming at the problem of high-precision detection of AtoN (Aids to Navigation, AtoN) in the complex inland river environment, in the absence of sufficient AtoN image types to train classifiers, this paper proposes an automatic AtoN detection algorithm Aids-to-Navigation-YOLOv4 (AN-YOLOv4) based on improved [...] Read more.

Aiming at the problem of high-precision detection of AtoN (Aids to Navigation, AtoN) in the complex inland river environment, in the absence of sufficient AtoN image types to train classifiers, this paper proposes an automatic AtoN detection algorithm Aids-to-Navigation-YOLOv4 (AN-YOLOv4) based on improved YOLOv4 (You Only Look Once, Yolo). Firstly, aiming at the problem of an insufficient number of existing AtoN datasets, the Deep Convolutional Generative Adversarial Networks (DCGAN) is used to expand and enhance the AtoN image dataset. Then, aiming at the problem of small target recognition accuracy, the image pyramid is used to multi-scale zoom the dataset. Finally, the K-means clustering algorithm is used to correct the candidate box of AN-YOLOv4. The test on the test dataset shows that the improvement effect of AN-YOLOv4 is obvious. The accuracy rate of small targets is 92%, and the average accuracy (mAP) of eight different types of AtoN is 92%, which is 14% and 13% higher than the original YOLOv4, respectively. This research has important theoretical significance and reference value for the intelligent perception of the navigation environment under the intelligent shipping system. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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22 pages, 8714 KiB

Open AccessArticle

Generalized Behavior Decision-Making Model for Ship Collision Avoidance via Reinforcement Learning Method

by Wei Guan, Ming-yang Zhao, Cheng-bao Zhang and Zhao-yong Xi

J. Mar. Sci. Eng. 2023, 11(2), 273; https://doi.org/10.3390/jmse11020273 - 25 Jan 2023

Cited by 19 | Viewed by 3480

Abstract

Due to the increasing number of transportation vessels, marine traffic has become more congested. According to the statistics, 89% to 95% of maritime accidents are related to human factors. In order to reduce marine incidents, ship automatic collision avoidance has become one of [...] Read more.

Due to the increasing number of transportation vessels, marine traffic has become more congested. According to the statistics, 89% to 95% of maritime accidents are related to human factors. In order to reduce marine incidents, ship automatic collision avoidance has become one of the most important research issues in the field of ocean engineering. A generalized behavior decision-making (GBDM) model, trained via a reinforcement learning (RL) algorithm, is proposed in this paper, and it can be used for ship autonomous driving in multi-ship encounter situations. Firstly, the obstacle zone by target (OZT) is used to calculate the area of future collisions based on the dynamic information of ships. Meanwhile, a virtual sensor called a grid sensor is taken as the input of the observation state. Then, International Regulations for Preventing Collision at Sea (COLREGs) is introduced into the reward function to make the decision-making fully comply with COLREGs. Different from the previous RL-based collision avoidance model, the interaction between the ship and the environment only works in the collision avoidance decision-making stage. Finally, 60 complex multi-ship encounter scenarios clustered by the COLREGs are taken as the ship’s GBDM model training environments. The simulation results show that the proposed GBDM model and training method has flexible scalability in solving the multi-ship collision avoidance problem complying with COLREGs in different scenarios. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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22 pages, 1770 KiB

Open AccessArticle

Autonomous Underwater Vehicle Path Planning Method of Soft Actor–Critic Based on Game Training

by Zhuo Wang, Hao Lu, Hongde Qin and Yancheng Sui

J. Mar. Sci. Eng. 2022, 10(12), 2018; https://doi.org/10.3390/jmse10122018 - 16 Dec 2022

Cited by 7 | Viewed by 3008

Abstract

This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) [...] Read more.

This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) path planning method is proposed in this study to improve the adaptive capability of autonomous planning and the reliability of obstacle avoidance in the unknown underwater environment. Considering the influence of the simulation environment, the obstacles in the simulation environment are regarded as agents and play a zero-sum game with the AUV. The zero-sum game problem is solved by improving the strategy of AUV and obstacles, so that the simulation environment evolves intelligently with the AUV path planning strategy. The proposed method increases the complexity and diversity of the simulation environment, enables AUV to train in a variable environment specific to its strategy, and improves the adaptability and convergence speed of AUV in unknown underwater environments. Finally, the Python language is applied to write an unknown underwater simulation environment for the AUV simulation testing. GSAC can guide the AUV to the target point in the unknown underwater environment while avoiding large and small static obstacles, canyons, and small dynamic obstacles. Compared with the soft actor–critic(SAC) and the deep Q-network (DQN) algorithm, GSAC has better adaptability and convergence speed in the unknown underwater environment. The experiments verifies that GSAC has faster convergence, better stability, and robustness in unknown underwater environments. Full article

(This article belongs to the Special Issue AI for Navigation and Path Planning of Marine Vehicles)

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