ITR: Collaborative Research: -\(NHS+ASE)-\(int+dmc\): Networks of Robots and Sensors for First Responders

ITR:合作研究:-(NHS ASE)-(int dmc):急救人员的机器人和传感器网络

基本信息

  • 批准号:
    0426945
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2004
  • 资助国家:
    美国
  • 起止时间:
    2004-09-01 至 2009-08-31
  • 项目状态:
    已结题

项目摘要

ABSTRACTITR: Collaborative Research (NHS + ASE) (int + dmc):Networks of Robots and Sensors for First RespondersPI: Daniela Rus, MITCo-PI: Vijay Kumar, U. PennsylvaniaCo-PI: Sanjiv Singh, CMUThis collaborative ITR project addresses the development of proactive networks of sensors and robots that perceive their environment and respond to it, anticipating information needs by the network and by users of the network, repositioning and organizing themselves to best acquire and deliver the information. Such networked systems combine the most advanced concepts in perception, communication, and control to create computational systems capable of interacting in meaningful ways with the physical environment, extending individual capabilities of each component and user to encompass a much wider area and range ofdata. The PIs envision a physical analog to the Internet-- networks of computers that can actively sense, physically interact with, and reason about the world. While the Internet allows transparent access to information already online, this research will extend the paradigm by allowing users to "google" for physical information, setting into motion robots and sensors that team together to acquire information and act on it. Intellectual MeritThe proposed research program will be make significant contributions to networked multi-agent systems: control, self-organization, adaptation, and perception. The work will focus on: (1) Control for communication and sensing: the control of robotic agents to maintain communication links or establish new ones, while obtaining the required sensory information and tracking sources; (2) Communication for sensing and perception: the fusion of information from heterogeneous sensors over the network, providing the required information for each agent to plan and control its mobility and providing remotely located human rescue workers with information through immersive displays; and (3) Communication networks for sensing and control: the grouping, scheduling and routing of nodes to adapt to changing, adverse conditions while maintaining guarantees for control of mobility and for sensor fusion and integration.Broader ImpactThe research will enhance national and homeland security by providing first responder with information about areas that are unsafe and hard to reach for humans in three ways. First, it will speed up the response time by helping in assessment, by augmenting human perception for command and control. Second, it will help in suppression and containment. Third, it will play a role in recovery, as in identification of victims and location of responding personnel. The collaboration with practitioners at the Allegheny Fire Training Academy will ensure that the research is grounded in the real world and has impact in the emergency response community.
摘要:合作研究(NHS + ASE)(int + dmc):机器人和传感器网络的第一响应PI:Daniela罗斯,麻省理工学院合作PI:维杰库马尔,美国。宾夕法尼亚州Co-PI:Sanjiv Singh,CMU这个合作ITR项目致力于开发感知环境并对其做出响应的传感器和机器人的主动网络,预测网络和网络用户的信息需求,重新定位和组织自己以最好地获取和提供信息。这种网络系统联合收割机了感知、通信和控制方面的最先进概念,创造出能够以有意义的方式与物理环境进行交互的计算系统,扩展了每个组件和用户的个人能力,以涵盖更广泛的数据区域和范围。PI设想了一个物理模拟互联网-计算机网络,可以主动感知,物理交互,并对世界进行推理。虽然互联网允许透明的访问信息已经在线,这项研究将扩展的范式,允许用户“谷歌”的物理信息,设置成运动机器人和传感器的团队一起获取信息,并采取行动it. Intellectual MeritThe建议的研究计划将作出重大贡献网络多智能体系统:控制,自组织,适应和感知。这项工作将侧重于:(1)通信和感知控制:控制机器人主体保持通信链路或建立新的通信链路,同时获得所需的感知信息和跟踪源;(2)感知和感知通信:通过网络融合来自异构传感器的信息,为每个代理提供所需的信息以计划和控制其移动性,并通过沉浸式显示器向远程定位的人类救援人员提供信息;(3)用于传感和控制的通信网络:节点的分组、调度和路由,以适应不断变化的不利条件,同时保持对移动性的控制以及传感器融合和集成的保证。首先,它将通过帮助评估,通过增强人类对指挥和控制的感知来加快反应时间。第二,有助于遏制和遏制。第三,它将在恢复工作中发挥作用,如查明受害者和确定应对人员的位置。 与阿勒格尼消防培训学院的从业人员合作将确保研究立足于真实的世界,并在应急响应界产生影响。

项目成果

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Sanjiv Singh其他文献

Improving Orchard Efficiency with Autonomous Utility Vehicles
利用自主多用途车提高果园效率
  • DOI:
    10.13031/2013.29902
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Bradley Hamner;Sanjiv Singh;M. Bergerman
  • 通讯作者:
    M. Bergerman
Neurological repercussions of neonatal nicotine exposure: A review
新生儿接触尼古丁对神经系统的影响:综述
Correction to: Two-Pore Domain Potassium Channel in Neurological Disorders
  • DOI:
    10.1007/s00232-022-00229-x
  • 发表时间:
    2022-03-22
  • 期刊:
  • 影响因子:
    2.900
  • 作者:
    Punita Aggarwal;Sanjiv Singh;V. Ravichandiran
  • 通讯作者:
    V. Ravichandiran
Enabling aggressive motion estimation at low-drift and accurate mapping in real-time
实现低漂移的主动运动估计和实时准确的映射
Learning to predict resistive forces during robotic excavation

Sanjiv Singh的其他文献

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{{ truncateString('Sanjiv Singh', 18)}}的其他基金

STTR Phase I: Rapid and Efficient Scene Modeling for Law Enforcement and Disaster Response
STTR 第一阶段:快速高效的执法和灾难响应场景建模
  • 批准号:
    1346457
  • 财政年份:
    2014
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
NRI: Large: Collaborative Research: Fast and Accurate Infrastructure Modeling and Inspection with Low-Flying Robots
NRI:大型:协作研究:使用低空飞行机器人进行快速准确的基础设施建模和检查
  • 批准号:
    1328930
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant
Workshop on the challenges in Vertical Farming
垂直农业挑战研讨会
  • 批准号:
    1152110
  • 财政年份:
    2011
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
JIETSSP: Coordination of Robotic Teams for Space Solar Power Assembly Operations
JIETSSP:空间太阳能发电组装作业机器人团队的协调
  • 批准号:
    0233698
  • 财政年份:
    2002
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant
Extended Environmental Monitoring via Intelligent, Autonomous Airships
通过智能自主飞艇扩展环境监测
  • 批准号:
    0086931
  • 财政年份:
    2000
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Summer Institute in Japan for U.S. Graduate Students in Science and Engineering
美国科学与工程研究生日本暑期学院
  • 批准号:
    9211767
  • 财政年份:
    1992
  • 资助金额:
    --
  • 项目类别:
    Standard Grant

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