RI: Information-Theoretic Control of Robotic Sensor Networks

RI：机器人传感器网络的信息论控制

• 批准号: 0713525
• 负责人: Eric Frew
• 金额: $ 26.85万
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713525&HistoricalAwards=false
• 关键词: RI; Information; Theoretic; Control; Robotic

This project will develop an information-theoretic framework for the planning and control of robotic sensor networks. Concepts from distributed sensor networking, cooperative control, networked communication, sensor fusion, and information theory are combined into a single framework that incorporates both communication and sensing objectives. This new framework enables robust intelligence in robot teams through reasoning about the combined effects of sensor uncertainty and wireless networked communication on model-based active sensing. Communication models move beyond line-of-sight or proximity constraints to consider the dependency of channel capacity on relative separation, asynchronous communication, and interference or noise that cannot be modeled. Sensing strategies will perform distributed decision-making over finite planning horizons and will integrate new methods to achieve desired levels of uncertainty rapidly. Research thrusts include: i.) information-theoretic formulation of robot sensor network planning and control; ii.) distributed optimization that combines predefined maneuvers with random search techniques; iii.) robustness to sensor uncertainty and radio noise using receding horizon control and multivariable extremum seeking; and iv.) experimental validation. Education activities will address the leakage of students from the engineering and technology pipeline by focusing on the engineering challenges, scientific questions, and societal benefits associated with the development of robot sensor networks.

该项目将为机器人传感器网络的规划和控制开发信息论框架。来自分布式传感器网络、协作控制、网络通信、传感器融合和信息论的概念被组合到一个包含通信和传感目标的单一框架中。这个新框架通过推理传感器不确定性和无线网络通信对基于模型的主动传感的综合影响，为机器人团队提供强大的智能。通信模型超越了视线或接近度限制，考虑了信道容量对相对分离、异步通信以及无法建模的干扰或噪声的依赖性。传感策略将在有限的规划范围内执行分布式决策，并将集成新方法以快速实现所需的不确定性水平。研究重点包括： i.) 机器人传感器网络规划和控制的信息理论表述； ii.) 分布式优化，将预定义的操作与随机搜索技术相结合； iii.) 使用后退地平线控制和多变量极值搜索来增强传感器不确定性和无线电噪声的鲁棒性； iv.) 实验验证。教育活动将重点关注与机器人传感器网络开发相关的工程挑战、科学问题和社会效益，解决工程和技术人才流失问题。