The Adaptive Sampling of Marine Robots in Ocean Observation: An Overview

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-04 DOI:10.1109/JOE.2025.3529087

Xiaojuan Ma;Yanhui Wang

引用次数: 0

Abstract

It is a costly and time-consuming practice to achieve ocean observation with sufficient spatial and temporal resolution. Luckily, it can be more efficient and effective by applying marine robots with adaptive sampling. The ocean environment and its uncertainties can be predicted during sampling to make planning of autonomous sensing for future operations of the marine robot. This article reviews various methods of adaptive sampling as well as robot path planning, weighing the benefits and drawbacks of each. In addition, three primary aspects of adaptive sampling are summarized: adaptive sampling architecture, multirobot sampling, and the dimensionality problem. The operation practice of adaptive sampling approaches in real applications is also investigated. Future trends for adaptive sampling of marine robots are also discussed to conclude several research directions that are not fully developed or remain unexplored, which will aid future studies.

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海洋观测中海洋机器人的自适应采样研究综述

实现具有足够空间和时间分辨率的海洋观测是一项昂贵和耗时的实践。幸运的是，通过应用具有自适应采样的海洋机器人，可以提高效率和效果。在采样过程中可以对海洋环境及其不确定性进行预测，为海洋机器人未来的作业制定自主感知规划。本文回顾了自适应采样和机器人路径规划的各种方法，并权衡了每种方法的优缺点。此外，总结了自适应采样的三个主要方面：自适应采样结构、多机器人采样和维数问题。研究了自适应采样方法在实际应用中的操作实践。讨论了海洋机器人自适应采样的未来发展趋势，总结了尚未完全开发或尚未探索的几个研究方向，这将有助于未来的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Oceanic Engineering 工程技术-工程：大洋

CiteScore

9.60

自引率

12.20%

发文量

审稿时长

12 months

期刊介绍： The IEEE Journal of Oceanic Engineering (ISSN 0364-9059) is the online-only quarterly publication of the IEEE Oceanic Engineering Society (IEEE OES). The scope of the Journal is the field of interest of the IEEE OES, which encompasses all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.