Extended Environmental Monitoring via Intelligent, Autonomous Airships
Extended Environmental Monitoring via Intelligent, Autonomous Airships
批准号:
0086931
负责人:
Sanjiv Singh
金额:
$11.98万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2000
资助国家:
美国
项目状态:
已结题
起止时间:
2000-10-01 至 2001-12-31
中文摘要
环境科学家制定政策,有效地管理人类活动,经常因缺乏有关环境状况的准确信息而受挫。在某些情况下,正确的政策是不明确的,因为人类活动和环境之间的关系没有得到很好的理解。更多的环境数据可以改善这种理解,并为制定有效的政策提供手段。在其他情况下,有效的政策已经提出,但需要更多的数据来证明实施的政治和经济成本。然而,更多的数据本身并不是解决方案。解析和分析数据的人力资源比现有数据更稀缺。什么是必要的是intelligence-gence积极采样的方式,以最大限度地利用数据中的有用内容。拟议的跨学科计划将调查手段,长期环境监测。具体而言,该项目旨在赋予太阳能飞艇进行延长时间的自主环境采样的智能。我们将探讨配置和设计问题,在天气模式存在的路径规划和控制,以及智能环境监测所需的算法。预期的结果是一个有效收集准确标记的环境数据的指令平台,可以无限期地停留在空中。飞艇非常适合环境监测任务,但它们在这方面的潜力尚未得到充分发挥。作为空中监测平台,它们表现出许多优于固定翼飞机和直升机的优点。它们更安全,更稳定,更便宜。它们不消耗功率,并能够延长运行时间。考虑到自动操作的智能和适当的传感硬件,飞艇提供了一个传感平台,这将成为寻求了解,修复和管理环境的各种环境科学家的资产。这项技术的潜在应用包括森林和农作物人为活动、湿地调查、土地利用评估、危险废物场地监测和侵蚀量化。通过与卡内基梅隆大学的费用分摊规定,我们将在一个原型上展示我们的结果,仪表飞艇产生空气污染的特征。该项目将与匹兹堡最近资助的EPA Supersite合作,利用飞艇生成空气污染和环境颗粒物的3-D轮廓。由此产生的数据集将为空气污染的垂直分布和传输机制提供新的见解。新技术不能逆转生态时钟,但它们可以帮助环境科学家推动知识前沿,帮助普通公民认识到保护自然世界的紧迫性。现在可以使用以前从间谍卫星上获得的分类图像,这些图像显示了宏观尺度上的环境退化程度。这一提议将创造一种以更高的分辨率、更有效的方式收集密集环境数据的方法。
英文摘要
Environmental scientists who develop policies for the effective management of human activity areoften thwarted by a lack of accurate information about the state of the environment. In somecases, the correct policy is not clear because the relationship between human activity and theenvironment is not well understood. Additional environmental data could improve such under-standingand provide the means to develop effective policies. In other cases effective policieshave been proposed but additional data is required to justify the political and economic costs ofimplementation. However, more data by itself is not the solution. The human resources neces-saryto parse and analyze the data are scarcer than available data. What is necessary is intelli-genceto actively sample in such a way as to maximize the useful content in the data.The proposed inter-disciplinary program will investigate means of extended-term environmentalmonitoring. Specifically, this project seeks to endow a solar-powered airship with the intelligenceto conduct extended-duration, autonomous environmental sampling. We will explore issues ofconfiguration and design, path planning and control in the presence of weather patterns, andalgorithms necessary for intelligent environmental monitoring. The intended result is an instru-mentedplatform for efficiently gathering accurately tagged environmental data that can stay aloftindefinitely.Airships are well suited for environmental monitoring tasks, however their potential in this capac-ityremains unfulfilled. As aerial monitoring platforms, they exhibit numerous advantages overfixed wing aircraft and helicopters. They are safer, more stable, and less expensive. They con-sumeless power and are capable of extended operation. Given the intelligence to operate auton-omouslyand the proper sensing hardware, an airship provides a sensing platform that would bean asset to a diverse range of environmental scientists who seek to understand, remediate, andmanage the environment. Potential applications of this technology include forest and crop man-agement,wetland surveys, land use assessment, monitoring of hazardous waste sites, and ero-sionquantification.Through a cost-sharing provision with Carnegie Mellon University, we will demonstrate ourresults on a prototype, instrumented airship to generate a characterization of air pollution. Work-ingtogether with a recently funded EPA Supersite in Pittsburgh, this project will employ an airshipto generate 3-D profiles of air pollution and ambient particulate matter. The resulting data set willprovide new insights into the vertical distribution and transport mechanisms of air pollution.New technologies cannot turn back the ecological clock but they can help environmental scien-tistspush back the frontiers of knowledge and help ordinary citizens grasp the urgency of pre-servingour natural world. It is now possible to use previously classified images from spysatellites that show the extent of environmental degradation at a macro scale. This proposal willcreate a means of gathering dense environmental data at a much finer resolution, in a more effi-cientmanner.
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STTR Phase I: Rapid and Efficient Scene Modeling for Law Enforcement and Disaster Response
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批准号:1346457
-
项目类别:Standard Grant
-
资助金额:$22.49万
-
财政年份:2014
-
负责人:Sanjiv Singh
-
依托单位:
NRI: Large: Collaborative Research: Fast and Accurate Infrastructure Modeling and Inspection with Low-Flying Robots
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批准号:1328930
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项目类别:Continuing Grant
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资助金额:$198.88万
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财政年份:2013
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负责人:Sanjiv Singh
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依托单位:
Workshop on the challenges in Vertical Farming
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批准号:1152110
-
项目类别:Standard Grant
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资助金额:$4.87万
-
财政年份:2011
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负责人:Sanjiv Singh
-
依托单位:
ITR: Collaborative Research: -\(NHS+ASE)-\(int+dmc\): Networks of Robots and Sensors for First Responders
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批准号:0426945
-
项目类别:Continuing Grant
-
资助金额:$0.0万
-
财政年份:2004
-
负责人:Sanjiv Singh
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依托单位:
JIETSSP: Coordination of Robotic Teams for Space Solar Power Assembly Operations
-
批准号:0233698
-
项目类别:Continuing Grant
-
资助金额:$35.0万
-
财政年份:2002
-
负责人:Sanjiv Singh
-
依托单位:
Summer Institute in Japan for U.S. Graduate Students in Science and Engineering
-
批准号:9211767
-
项目类别:Standard Grant
-
资助金额:$0.0万
-
财政年份:1992
-
负责人:Sanjiv Singh
-
依托单位:
国内基金
海外基金
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