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Autonomous Techniques for anthropogenic Structure Ecological Assessment (AT-SEA)

Autonomous Techniques for anthropogenic Structure Ecological Assessment (AT-SEA)
人为结构生态评估自主技术(AT-SEA)
批准号:
NE/T010592/1
负责人:
Blair Thornton
金额:
$21.3万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --

项目摘要

项目成果

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中文摘要
翻译
海洋中数以千计的石油和天然气行业结构正在接近生命的尽头。此时,通常需要移除它们,并将环境恢复到安全状态。这个过程被称为退役。由于这些地点中的许多都很老(通常是20多年),而且最初是在现行环境法规存在之前进行钻探的,因此这些地点周围的海床经常受到一些污染。为了确保不会发生有害影响,退役作业需要得到环境评估和随后的监测的支持。可能需要在退役后多年进行监测,特别是在某些结构保留不变的情况下。近海环境的监测调查既昂贵又耗时,需要船只和许多专业的海上人员。这一要求虽然至关重要,但会给工业和公众带来相当大的成本。海洋机器人使用计算机系统自行执行调查任务,经常被用于对环境进行详细的科学评估。由于它们快速收集非常高质量的数据,这类机器人最近已被行业采用用于一些任务,但这些仍然需要昂贵的支持船,因为它们不能执行远程任务。最近的技术发展降低了成本,并将这些机器人的射程扩大到数千公里,使从海岸发射的机器人能够对多个地点进行远程评估。这将有许多好处,提高环境信息的质量和数量,同时降低对测量船和船员的昂贵要求。开展首个完全自主的多个退役场地环评。Autosub远程海洋机器人潜艇将从设得兰海岸下水,参观北海北部的三个退役地点并进行环境评估,大约10天后带着船上的详细调查信息返回。机器人将拍摄海床的照片,这些照片将自动拼接在一起,绘制出海底、现有结构和生活在那里的动物的地图。已建立的传感器系统将测量水的一系列属性,包括石油和天然气的存在。除了退役地点,机器人还将访问一个特殊的海洋保护区,我们知道那里有天然气的自然泄漏,以检查机器人是否能够可靠地检测到确实发生的泄漏。在返回岸上时,该项目将检查所有获得的数据,并将其与使用标准测量船方法收集的数据进行比较。我们将测试是否可以从这两个数据集中识别出相同的环境趋势,以确定自动方法是否适合取代标准的测量船作业。该项目还将编写一份完整记录的案例研究报告,其中包括关于成本和效益的详细信息、关于部署的实用信息以及降低风险和提高业务效率的办法。这将被工业、科学家和政府监管机构用来演示这些技术,并将向潜在用户提供必要的信息,以帮助他们采用这些技术。该项目的总体目标是以更低的成本改善北海的环境保护,并展示这项领先的英国机器人技术如何在全球范围内使用。
英文摘要
Thousands of Oil & Gas industry structures in the sea are approaching the end of their lives. At this time, they typically need to be removed and the environment returned to a safe state. This process is known as decommissioning. As many of these sites are old (typically 20+ years) and originally were drilled before the current environmental regulations existed, there has often been some contamination of the seabed around these sites. To ensure that no harmful effects will occur, decommissioning operations need to be supported by an environmental assessment and subsequent monitoring. Monitoring may be required over many years after decommissioning, especially if some structures are left in place. Monitoring surveys in the offshore environment are expensive and time-consuming, requiring ships and many specialist seagoing personnel. This requirement, although vital, will have a considerable cost for industry and the public.Ocean robots, which use computer systems to carry out survey missions by themselves, are regularly used in detailed scientific assessments of the environment. As they collect very high-quality data quickly, such robots have recently been adopted for some tasks by industry but these still require an expensive support ship as they are not capable of long-range missions. Recent technological developments have cut the cost and expanded the range of these robots to thousands of kilometres, making it possible for long-range assessments of multiple sites to be undertaken with a robot launched from the shore. This would have many advantages, improving the quality and quantity of environmental information while cutting the costly requirement for a survey ship and crew. We will carry out the first fully autonomous environmental assessment of multiple decommissioning sites. The Autosub long-range ocean robot submarine ("Boaty McBoatface") will be launched from the shore in Shetland, visit and carry out an environmental assessment at three decommissioning sites in the northern North Sea, before returning around 10 days later with the detailed survey information onboard. The robot will take photographs of the seabed, and these will be automatically stitched together to make a map of the seafloor, structures present, and the animals that live there. Established sensor systems will measure a range of properties of the water, including the presence of oil and gas. As well as the decommissioned sites, the robot will visit a special marine protected area where we know there are natural leaks of gas, to check the robot can reliably detect a leak if it did occur.On return to shore, the project will examine all the data obtained and compare it to that gathered using standard survey ship methods. We will test if the same environmental trends can be identified from both datasets to determine if the automated approach would be a suitable replacement for standard survey ship operations. The project will also produce a fully documented case study, which includes detailed information on the costs and benefits, practical information on deployments and approaches to reduce the risks and improve the efficiency of operations. This will be used by industry, scientists and government regulators, to demonstrate the techniques and will provide the necessary information to potential users to aid in their adoption. The overall goal of the project is to improve the environmental protection of the North Sea at a reduced cost and to demonstrate how this leading UK robotic technology could be used worldwide.
期刊论文(6)
专著(0)
科研奖励(0)
会议论文
High-resolution visual seafloor mapping and classification using long range capable AUV for ship-free benthic surveys
使用远程 AUV 进行高分辨率视觉海底测绘和分类,以进行无船海底调查
DOI: --
发表时间: 2023
期刊:
影响因子: --
作者: [Bodenmann A]
通讯作者: Bodenmann A
DOI: 10.55417/fr.2022037
发表时间: 2021-08
期刊: ArXiv
影响因子: --
作者: [Takaki Yamada;A. Prügel-Bennett;Stefan B. Williams;O. Pizarro;B. Thornton]
通讯作者: Takaki Yamada;A. Prügel-Bennett;Stefan B. Williams;O. Pizarro;B. Thornton
Leveraging Metadata in Representation Learning With Georeferenced Seafloor Imagery
利用地理参考海底图像的表示学习中的元数据
DOI: 10.1109/lra.2021.3101881
发表时间: 2021
期刊: IEEE Robotics and Automation Letters
影响因子: 5.2
作者: [Yamada T]
通讯作者: Yamada T
Auto-calibration of line-laser structured-light seafloor mapping systems
线激光结构光海底测绘系统的自动校准
DOI: 10.23919/oceans44145.2021.9705873
发表时间: 2021
期刊:
影响因子: --
作者: [Stanley D]
通讯作者: Stanley D
RamaCam - In situ holographic imaging and chemical spectroscopy for long term scalable analysis of marine particles in deep-sea environments
  • 批准号:
    NE/R01227X/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $10.26万
  • 财政年份:
    2018
  • 负责人:
    Blair Thornton
  • 依托单位:
BioCam - Mapping of Benthic Biology, Geology and Ecology with Essential Ocean Variables
  • 批准号:
    NE/P020887/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $74.87万
  • 财政年份:
    2017
  • 负责人:
    Blair Thornton
  • 依托单位:
国内基金
海外基金
EstimatingLarge Demand Systems with MachineLearning Techniques
  • 批准号:
    --
  • 项目类别:
    外国学者研究基金
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    IoshuaAlex
  • 依托单位: