Learning-Based Mobile Robotic Sensor Networks

基于学习的移动机器人传感器网络

• 批准号: 0921810
• 负责人: Chun-Sing Lee
• 金额: $ 30万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0921810&HistoricalAwards=false
• 关键词: Learning; Based; Mobile; Robotic; Sensor

Dynamic mobile sensor networks are a distributed collection of mobile robots, each of which has sensing, computation, wireless communication and mobility capabilities. In a traditional wireless sensor network, although mobile robots are being used as sensor nodes, their dynamics and mobility are not fully exploited to improve the quality of collaborative sensing. This project explores research challenges in learning-based robotic wireless sensor networks by exploiting techniques in multi-robot research and wireless sensor networks with mobility taken into consideration. The proposed research aims to develop distributed learning algorithms and adaptive sensing coordination strategies for monitoring properties of mobile wireless sensor networks and adjusting locations of mobile sensor nodes to achieve better quality of collaborative sensing and environmental inference. The impact of this research lies in the novelty and synergism among networking, machine learning, signal processing and control to adaptively control the network structure, data routing, and signal processing in the mobile robotic sensor network to achieve better collaborative sensing. Expected results from this research will find applications in homeland security, environmental monitoring and sampling, and autonomous mobile robots for better collaborative sensing and information processing. The proposed project will serve as an excellent vehicle to recruit graduate students, especially those from underrepresented populations, to pursue their doctoral degree, and to recruit high-school students and encourage them to select engineering/computer science for their choice of career.

动态移动的传感器网络是一个分布式的移动的机器人集合，每个机器人都具有感知、计算、无线通信和移动能力。 在传统的无线传感器网络中，虽然移动的机器人被用作传感器节点，但它们的动态性和移动性没有被充分利用来提高协作感知的质量。 本项目通过利用多机器人研究和考虑移动性的无线传感器网络技术，探索基于学习的机器人无线传感器网络的研究挑战。 该研究旨在开发分布式学习算法和自适应感知协调策略，用于监测移动的无线传感器网络的性能，并调整移动的传感器节点的位置，以实现更好的协作感知和环境推理质量。 这项研究的影响在于网络，机器学习，信号处理和控制之间的新奇和协同作用，以自适应地控制移动的机器人传感器网络的网络结构，数据路由和信号处理，以实现更好的协作感测。 这项研究的预期结果将应用于国土安全、环境监测和采样以及自主移动的机器人，以实现更好的协作传感和信息处理。 拟议的项目将成为一个极好的工具，以招募研究生，特别是来自代表性不足人口的研究生，攻读博士学位，并招募高中生，鼓励他们选择工程/计算机科学作为职业。