课题基金 / 基金详情

EAGER: Shared Visual Common Ground in Human-Robot Interaction for Small Unmanned Aerial Systems

EAGER: Shared Visual Common Ground in Human-Robot Interaction for Small Unmanned Aerial Systems
EAGER:小型无人机系统人机交互中的共享视觉共同点
批准号:
1143713
负责人:
Robin Murphy
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2014-07-31

项目摘要

项目成果

Robin Murphy的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
This project will create a computational theory of visual common ground, allowing users to give directives to a robot (or other team members) and receive confirmation or constraints through visual communication over a shared visual display. The motivating example is an urban search and rescue (US&R) professional tapping, sketching, and annotating on an iPad in order to direct a small unmanned aerial system (sUAS) without training. Previous work in human-robot interaction with common ground has been limited to natural language, but recent work has shown that having all team members see the robot's eye view in unmanned ground robots significantly improved performance and situation awareness. The proposed work populate the computational theory using the Shared Roles Model to represent the inputs (directives, notations), outputs (display viewpoint, form, size, location, content, etc.), and transformations (visual communication engine). The computational theory will be prototyped, refined, and tested by US&R practitioners flying realistic sUAS missions at Texas A&M's Disaster City.Intellectual merit: The project will create a computational theory of visual common ground that will enable two-way human-robot interaction using visual communication mechanisms such as tapping, sketching, and annotation on shared visual displays on mobile devices such as iPads, smartphones, and tablet PCs. The results will advance the fields of human-robot interaction, artificial intelligence, and cognitive science. Broader impacts: The results could revolutionize how people use mobile devices to interact with robots (and with each other) using naturalistic visual mechanisms, bypassing extensive training. The project will actively recruit women, Hispanics, and persons with disabilities to participate through REU programs. An open source visual communication toolkit for HRI researchers will be produced. The results will improve robots for public safety, remote medicine, and telecommuting, and could also immediately help save lives through incorporation into Texas Task Force 1.
期刊论文(0)
专著(0)
科研奖励(0)
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
海外基金