RII Track-2 FEC: Computational Methods and Autonomous Robotics Systems for Modeling and Predicting Harmful Cyanobacterial Blooms

RII Track-2 FEC:用于建模和预测有害蓝藻水华的计算方法和自主机器人系统

基本信息

  • 批准号:
    1923004
  • 负责人:
  • 金额:
    $ 598.93万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Cooperative Agreement
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-08-01 至 2024-07-31
  • 项目状态:
    已结题

项目摘要

The world's freshwater lakes are a crucial source of water for human use, for drinking, irrigation, cooling, recreation, and food production. However, provision of these essential lake services is threatened by the increased incidence of harmful cyanobacterial blooms in lakes worldwide. Harmful blooms decrease lake water quality, clarity, and aesthetics, negatively impact property values, and can threaten human and animal health through the production of potent toxins that can damage multiple organ systems. This project aims to unravel the drivers of where, when, and how cyanobacterial blooms develop and spread, by combining robotics and big data technologies with traditional water sampling. The project will advance the ability to evaluate and predict cyanobacterial blooms, potentially allowing earlier public health interventions in recreational lakes and in lakes that supply drinking water. Interventions can enable improved water treatment and distribution. The project's workforce development activities will train next generation professionals to work and communicate across disciplines and communities in order to address complex scientific problems that have major societal implications, though use of big data tools and technology. Using the tools of big data jointly with robotics, sensor networks, and limnological sampling, the project develops strategies for real-time, adaptive, autonomous environmental data collection and processing to enhance the ability to predict the development of harmful cyanobacterial blooms in lakes with incipient blooms. Specifically, autonomous surface vehicles equipped with a suite of sensors measuring physical, chemical, and biological parameters and unmanned aerial vehicles equipped with hyper-spectral, multispectral, and visible-light cameras will generate large volumes of data on lakes during the onset and succession of cyanobacterial blooms. Post-acquisition processing and model development will examine controls on the genesis and spread of blooms in near-real time. The project brings together an interdisciplinary group of investigators with expertise in big data, environmental science, ecology, human demography, instrumentation, and robotics from four EPSCoR jurisdictions: Maine, New Hampshire, Rhode Island, and South Carolina. Partnerships with the participating institutions, local lake associations, municipal water providers, and state agencies will produce large-scale datasets of physical, chemical, and biological factors influencing water quality from lakes in all four states, which will then be used to create new models to predict harmful cyanobacterial blooms. Over a dozen early career scientists will be trained in interdisciplinary research. Community partners will be engaged in data collection, data interpretation, and implementation of monitoring and management strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
世界上的淡水湖是人类饮用、灌溉、冷却、娱乐和粮食生产的重要水源。然而,这些基本湖泊服务的提供受到世界各地湖泊中有害蓝藻繁殖发生率增加的威胁。有害的水华会降低湖水的质量、清晰度和美观性,对财产价值产生负面影响,并通过产生能损害多个器官系统的强效毒素威胁人类和动物的健康。该项目旨在通过将机器人技术和大数据技术与传统的水采样相结合,揭示蓝藻华在何时何地以及如何发展和传播的驱动因素。该项目将提高评估和预测蓝藻繁殖的能力,可能允许在休闲湖泊和供应饮用水的湖泊进行早期公共卫生干预。干预措施可以改善水的处理和分配。该项目的劳动力发展活动将培训下一代专业人员,通过使用大数据工具和技术,跨学科和社区工作和交流,以解决具有重大社会影响的复杂科学问题。本项目利用大数据工具,结合机器人技术、传感器网络和湖泊采样,制定实时、自适应、自主的环境数据收集和处理策略,以提高对早期藻华湖泊有害藻华发展的预测能力。具体来说,配备一套测量物理、化学和生物参数传感器的自主水面车辆,以及配备超光谱、多光谱和可见光相机的无人驾驶飞行器,将在蓝藻华开始和演为期间生成大量湖泊数据。采集后处理和模型开发将在近实时的情况下检查对花的发生和传播的控制。该项目汇集了一个跨学科的研究小组,他们在大数据、环境科学、生态学、人口统计学、仪器仪表和机器人技术方面具有专业知识,来自四个EPSCoR管辖区:缅因州、新罕布什尔州、罗德岛州和南卡罗来纳州。与参与机构、当地湖泊协会、市政供水供应商和州机构的合作将产生影响所有四个州湖泊水质的物理、化学和生物因素的大规模数据集,然后将用于创建新的模型来预测有害的蓝藻繁殖。十几名早期职业科学家将接受跨学科研究方面的培训。社区伙伴将参与数据收集、数据解释以及监测和管理战略的实施。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(15)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Towards a Reliable Heterogeneous Robotic Water Quality Monitoring System: An Experimental Analysis
建立可靠的异构机器人水质监测系统:实验分析
  • DOI:
    10.1007/978-3-030-71151-1_13
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Roznere, M.;Jeong, M.;Maechling, L.;Ward, N.K.;Brentrup, J.A.;Steele, B.;Bruesewitz, D.A.;Ewing, H.A.;Weathers, K.C.;Cottingham, K.L.
  • 通讯作者:
    Cottingham, K.L.
A Trifacacking System for Dynamic Subset Targets using Probability Hypothesis Filtering
使用概率假设过滤的动态子集目标的 Trifacacking 系统
  • DOI:
    10.1016/j.ifacol.2022.11.206
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Perera, R.A. Thivanka;Phillips, Andrew;Yuan, Chengzhi;Stegagno, Paolo
  • 通讯作者:
    Stegagno, Paolo
Efficient LiDAR-based In-water Obstacle Detection and Segmentation by Autonomous Surface Vehicles in Aquatic Environments
Multi-Robot Adaptive Sampling based on Mixture of Experts Approach to Modeling Non-Stationary Spatial Fields
A PHD Filter Based Localization System for Robotic Swarms
  • DOI:
    10.1007/978-3-030-92790-5_14
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    R. T. Perera;C. Yuan;P. Stegagno
  • 通讯作者:
    R. T. Perera;C. Yuan;P. Stegagno
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Alberto Quattrini Li其他文献

Towards the autonomous underwater construction of cement block structures with free-floating robots
利用自由漂浮机器人进行水泥块结构水下自主施工
Sunflower: locating underwater robots from the air: video
向日葵:从空中定位水下机器人:视频
Vision-based shipwreck mapping: On evaluating features quality and open source state estimation packages
基于视觉的沉船测绘:评估特征质量和开源状态估计包
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alberto Quattrini Li;A. Coskun;S. M. Doherty;S. Ghasemlou;A. S. Jagtap;M. Modasshir;S. Rahman;A. Singh;M. Xanthidis;J. O’Kane;Ioannis M. Rekleitis
  • 通讯作者:
    Ioannis M. Rekleitis
Experimental Analysis of Radio Communication Capabilities of Multiple Autonomous Surface Vehicles
多自主水面车辆无线电通信能力实验分析
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    S. Malebary;Jason Moulton;Alberto Quattrini Li;Ioannis M. Rekleitis
  • 通讯作者:
    Ioannis M. Rekleitis
External Force Field Modeling for Autonomous Surface Vehicles
自主地面车辆的外力场建模

Alberto Quattrini Li的其他文献

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{{ truncateString('Alberto Quattrini Li', 18)}}的其他基金

CAREER: Resilient Low-Cost Robot Teams for Autonomous Aquatic Exploration
职业:用于自主水生探索的有弹性的低成本机器人团队
  • 批准号:
    2144624
  • 财政年份:
    2022
  • 资助金额:
    $ 598.93万
  • 项目类别:
    Continuing Grant
Collaborative Research: NRI: INT: Cooperative Underwater Structure Inspection and Mapping
合作研究:NRI:INT:合作水下结构检查和测绘
  • 批准号:
    2024541
  • 财政年份:
    2020
  • 资助金额:
    $ 598.93万
  • 项目类别:
    Standard Grant
MRI: Track-1: Acquisition of marine multirobot systems for underwater monitoring and construction
MRI:Track-1:采购用于水下监测和施工的海洋多机器人系统
  • 批准号:
    1919647
  • 财政年份:
    2019
  • 资助金额:
    $ 598.93万
  • 项目类别:
    Standard Grant

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