A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

Cai, Chang; Chen, Jianfeng; Yan, Qingli; Liu, Fen; Zhou, Rongyan

doi:10.1049/rsn2.12256

Cited by 16 publications

(6 citation statements)

References 32 publications

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“…Thomas et al proposed a novel metamodel to estimate the mission success possibility for a SAR task with the support of a supervised learning method [28]. Similar studies can be found in [29][30][31]. We find that the particle swarm optimization (PSO) method is commonly used to determine the optimization deployment strategy of maritime rescue facilities.…”

Section: Introductionmentioning

confidence: 60%

“…We implemented the simulation with the help of accident data samples, main sea routes and an SAR rescue base station. Figure 3 demonstrates the distributions under different numbers of SAR rescue base stations (e.g., 5,10,15,20,25,30). Figure 3a illustrates the distributions of five ship base stations for an SAR task, whilst the x-axis and y-axis denote the latitude and longitude of each of the base stations.…”

Section: Sar Task Performance With Shipmentioning

confidence: 99%

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Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Sun

Ling

Chen

et al. 2022

JMSE

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Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.

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Section: Introductionmentioning

confidence: 60%

Section: Sar Task Performance With Shipmentioning

confidence: 99%

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Sun

Ling

Chen

et al. 2022

JMSE

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show abstract

“…There are available existing methods for this purpose, such as the lawnmower method [41,42], the spanning tree coverage method [43], and so on. However, considering the prior target information, we adopt our previously proposed SAR-A* [33], which is specifically designed for coverage path planning for AUVs equipped with SSS.…”

Section: Single-auv Coverage Path Planning Methodsmentioning

confidence: 99%

“…As for the single-robot coverage path planning method, the SAR-A* method, which we proposed previously [33], is employed to plan the AUV paths. For the discrete task area, the SAR-A* method generates a path through iteratively selecting optimal waypoints.…”

Section: Contributionmentioning

confidence: 99%

A Multi-Robot Coverage Path Planning Method for Maritime Search and Rescue Using Multiple AUVs

et al. 2022

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In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets.

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“…The primary goal of coverage path planning is to devise a path or trajectory for a vehicle or agent that maximizes the coverage of a specified area, minimizing redundancy and ensuring systematic exploration. Particularly, coverage path planning is a critical aspect of marine robotics, encompassing a wide range of applications like bathymetry mapping [122,123], underwater surveys [124,125], search and rescue missions [126,127], and mine-countermeasures [128,129]. By intelligently navigating through designated areas, AMVs can efficiently collect data, perform critical tasks, and contribute to a safer and more informed maritime environment.…”

Section: Coverage Planning Techniquesmentioning

confidence: 99%

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Zhao,

Bai

2024

JMSE

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Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems.

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A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

Cited by 16 publications

References 32 publications

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

A Multi-Robot Coverage Path Planning Method for Maritime Search and Rescue Using Multiple AUVs

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

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