Achieving optimal-dynamic path planning for unmanned surface vehicles: A rational multi-objective approach and a sensory-vector re-planner

Zhao, Liang; Bai, Yong; Paik, Jeom Kee

doi:10.1016/j.oceaneng.2023.115433

Cited by 12 publications

(2 citation statements)

References 58 publications

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“…Mahmoud Zadeh et al [53] presented an uninterrupted collision-free pathplanning system combined with a novel B-spline data frame and PSO, which facilitates the operational performance of USVs in an ocean-sampling mission. Zhao et al [54] proposed the AENSGA-II to solve path planning for USVs in dynamically unforeseen situations, which can simultaneously optimize four objective functions (path length, smoothness, energy consumption, and path safety).…”

Section: Path Planning In the Current Environmentmentioning

confidence: 99%

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Yang,

Pan,

Wei

et al. 2024

JMSE

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Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents.

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Section: Path Planning In the Current Environmentmentioning

confidence: 99%

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Yang,

Pan,

Wei

et al. 2024

JMSE

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“…Secondary to the foregoing, it is pertinent to acknowledge the substantial challenges confronting AMVs when executing turns in underwater environments. Given the complexity of the ocean environment, AMVs struggle to execute precise turning maneuvers [131], often causing deviations from the original path and thus affecting the efficiency of data collection.…”

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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Fuzzy logic-supported building design for low-energy consumption in urban environments

Arun,

Efremov,

Nguyen

et al. 2024

Case Studies in Thermal Engineering

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Achieving optimal-dynamic path planning for unmanned surface vehicles: A rational multi-objective approach and a sensory-vector re-planner

Cited by 12 publications

References 58 publications

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

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

Fuzzy logic-supported building design for low-energy consumption in urban environments

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