Adaptive Zigzag Mapping of a Patchy Field by a Long-Range Autonomous Underwater Vehicle

IF 3.8 2区 工程技术 Q1 ENGINEERING, CIVIL IEEE Journal of Oceanic Engineering Pub Date : 2024-02-20 DOI:10.1109/JOE.2023.3338694
Yanwu Zhang;Brett W. Hobson;Brian Kieft;Michael A. Godin;Thomas Ravens;Michael Ulmgren
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Abstract

Spatial heterogeneity and temporal evolution are intrinsic features of some important oceanic processes, e.g., harmful algal blooms and oil spills, where aggregations of organisms or materials are localized and noncontinuous. In a sparse patchy field, routine lawnmower-mode or zigzag surveys by ships or autonomous platforms are not efficient since a large proportion of the survey time is spent on no-patch areas. We developed an adaptive zigzag algorithm for an autonomous underwater vehicle (AUV) to map patchy fields more efficiently than routine zigzag surveys. An AUV autonomously detects the peak and the edge of a patch, and accordingly determines when to turn onto the next zigzag leg. The AUV sweeps through the field on successive zigzag legs. Using an oil spill model data set, the performance of adaptive zigzag surveys is compared with that of routine zigzag surveys. In April 2022, the algorithm was tested on a long-range AUV through a 16-h survey in Monterey Bay, CA, USA, by reading the oil spill model data as the virtual measurement in real time.
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远距离自主潜水器对斑块区域进行自适应之字形测绘
空间异质性和时间演化是某些重要海洋过程(如有害藻华和溢油)的固有特征,在这些过程中,生物或物质的聚集是局部的、非连续的。在稀疏的斑块区域,由船舶或自主平台进行的常规割草机模式或之字形勘测并不高效,因为勘测时间的很大一部分都花在了无斑块区域。我们为自动潜航器(AUV)开发了一种自适应之字形算法,可比常规之字形勘测更高效地绘制斑块区域图。自动潜航器可自动检测斑块的峰值和边缘,并据此决定何时转入下一个之字形航段。自动潜航器在连续的之字形航段上掠过油田。利用溢油模型数据集,比较了自适应之字形勘测与常规之字形勘测的性能。2022 年 4 月,通过实时读取溢油模型数据作为虚拟测量值,在美国加利福尼亚州蒙特雷湾对长距离 AUV 进行了 16 小时的勘测,对该算法进行了测试。
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来源期刊
IEEE Journal of Oceanic Engineering
IEEE Journal of Oceanic Engineering 工程技术-工程:大洋
CiteScore
9.60
自引率
12.20%
发文量
86
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.
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