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NRI: Collaborative Research: Targeted Observation of Severe Local Storms Using Aerial Robots

NRI: Collaborative Research: Targeted Observation of Severe Local Storms Using Aerial Robots
NRI:合作研究:使用空中机器人对局部严重风暴进行有针对性的观测
批准号:
1527113
负责人:
Adam Houston
金额:
$42.57万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-01-01 至 2018-12-31

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中文摘要
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英文摘要
This project addresses the development of self-deploying aerial robotic systems that will enable new in-situ atmospheric science applications. Fixed-wing aerial robotic technology has advanced to the point where platforms fly persistent sampling missions far from remote operators. Likewise, complex atmospheric phenomena can be simulated in near real-time with increasing levels of fidelity. Furthermore, cloud computing technology enables distributed computation on large, dynamic data sets. Combining autonomous airborne sensors with environmental models dispersed over multiple communication and computation channels enables the collection of information essential for examining the fundamental behavior of atmospheric phenomena. The aerial robotic system proposed here will close significant capability gaps in conventional platform?s abilities to collect the data necessary to answer a wide range of scientific questions. The motivating application for this work is improvement in the accuracy and lead-time of tornado warnings.The proposed project draws on techniques in the areas of robotics, unmanned systems, networked control, wireless communication, active sensing, and atmospheric science to realize the vision of bringing cloud robotics to the clouds. The autonomous self-deploying aerial robotic systems is comprised of multiple robotic sensors and distributed computing nodes including: multiple fixed-wing unmanned aircraft, deployable Lagrangian drifters, mobile Doppler radar, mobile command and control stations, distributed computation nodes in the field and in the lab, a net-centric middleware connecting the dispersed elements, and an autonomous decision-making architecture that closes the loop between sensing in the field and new online numerical weather prediction tools.
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