RI: Small: Providing Quality of Information in Robot Sensor Networks

RI：小型：在机器人传感器网络中提供高质量的信息

• 批准号: 1116010
• 负责人: Eric Frew
• 金额: $ 50万
• 依托单位: University of Colorado at Boulder
• 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
• 起止时间: 2011-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116010&HistoricalAwards=false
• 关键词: RI; Small; Providing; Quality; Information

This project develops distributed control algorithms for robot sensor networks using information dynamics to unify network mobility and communication. The new framework moves beyond location-based mobility control algorithms and packet-based communication protocols to a single information-theoretic approach. The framework is developed through: (1) formulation of a new quality of service of information metric and characterization of feasible quality of service demands, (2) adaptive receding horizon control that adjusts planning horizon and sample time based on information dynamics, (3) distributed calculation and optimization of team quality of service, and (4) experimental validation using a heterogeneous indoor robot sensor network and outdoor unmanned aircraft systems.This research enables the collection of in situ data over large spatiotemporal scales. When deployed in remote or hazardous locations the data collected with these networks leads to a direct improvement to model or understand complex environments, which can help save lives. Students will benefit from the multi-disciplinary activities involved, including embedded computing, miniature sensor devices, wireless networking, and automatic control.

该项目使用信息动态为机器人传感器网络开发分布式控制算法，以统一网络移动性和通信。 新框架超越了基于位置的移动性控制算法和基于数据包的通信协议，转变为单个信息理论方法。 该框架是通过以下方式开发的：（1）提出新的信息指标服务质量和可行质量需求的表征，（2）根据信息动态调整计划的视野和样本时间的自适应后退视野控制，（3）团队服务质量的分布式计算和优化，以及（4）使用异质的室内机器人传感器网络和室外无人飞机系统的实验验证。这项研究可以通过大型时空尺度收集原位数据。 当部署在远程或危险位置时，使用这些网络收集的数据会导致模型或了解复杂环境的直接改进，这可以帮助挽救生命。 学生将受益于所涉及的多学科活动，包括嵌入式计算，微型传感器设备，无线网络和自动控制。