Dynamic Detecting Based Trajectory Planning for AUV to Collect Data from Underwater Sensors

Cheng, Mingyue; Ji, Fei; Guan, Quansheng

doi:10.1109/icc42927.2021.9500249

Cited by 2 publications

(2 citation statements)

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“…This strategy empowers AUVs to dynamically select data collectors to visit, with the objective of maximizing the value of information (VoI). Addressing the issue of low location accuracy associated with sensor nodes, the work in [99] proposed a grouping-based dynamic trajectory planning (GDTP) approach. This method detects sensor nodes' presence and orientations, subsequently grouping them based on a proposed common communication area model.…”

Section: Online Path-planning Techniquesmentioning

confidence: 99%

“…This method detects sensor nodes' presence and orientations, subsequently grouping them based on a proposed common communication area model. Building on this foundation, authors of [98] extended the GDTP [99] concept to encompass tracking sensors mounted on turtles. In this adaptation, the AUV's cruising direction is dynamically determined to achieve the maximum expected payoff, considering both data collection and energy consumption despite inaccurate detection.…”

Section: Online Path-planning Techniquesmentioning

confidence: 99%

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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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Section: Online Path-planning Techniquesmentioning

confidence: 99%

Section: Online Path-planning Techniquesmentioning

confidence: 99%