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EAGER: Documenting and Analyzing Use of Robots for COVID-19

EAGER: Documenting and Analyzing Use of Robots for COVID-19
EAGER:记录和分析机器人在 COVID-19 中的使用情况
批准号:
2032729
负责人:
Robin Murphy
金额:
$6.9万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2021-03-31

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中文摘要
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英文摘要
The robotics community needs to learn and prepare for future infectious diseases and future disasters. Understanding the role of robotics in preventing, responding to, and mitigating the consequences of pandemics could have a profound impact on the future of robotics research and convergence research in general. This understanding could identify applications where robotics are impacting, or could impact, the response to the COVID-19 pandemic disaster. Roboticists could then concentrate on those applications and gaps while the relevant agencies could have confidence in the systems. This project will guide the rapid innovation of robots for the remainder of the COVID-19 pandemic and inform future convergence research on autonomous robots by creating and analyzing a database of press and social media reports on how ground and aerial robots are being used throughout the world for the response. The project has two novel components that distinguish it from simple data gathering and archiving and that will ensure its utility for research. One, by archiving, curating, and analyzing the comprehensive use of robots worldwide for COVID-19 response and creating a sustainable nexus permitting incorporation of new reports during the evolving pandemic and supporting additional analyses. Two, the novel cross-disciplinary framework will provide a standard set of schemas for capturing data on the use of robots for disasters. Not only will the framework and plan of work establish how robots are being used, it is expected to use the experts’ unique domain knowledge to identify missed opportunities for application.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.
期刊论文(3)
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会议论文
RAPID/Collaborative Research: Datasets for Uncrewed Aerial System (UAS) and Remote Responder Performance from Hurricane Ian
SCC-CIVIC-PG Track B: Community-Centric Pre-Disaster Mitigation with Unmanned Aerial and Marine Systems
EAGER: Evidence-Based Model of Adoption of Robotics for Pandemics and Natural Disasters
RAPID/Collaborative Research: Data Collection for Robot-Oriented Disaster Site Modeling at Champlain Towers South Collapse
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