Estimation and Control Using Sensor Vehicle Networks for Approximation and Learning Problems

该奖项的研究目标是推导，开发，实施和测试传感器网络控制和估计方法的理论框架，从而能够推导出车辆环境的近似接近最佳速率。理论框架将作为基础，推导出编码和解码方案，是必不可少的高带宽移动的传感器网络。该方法依赖于从几个不同的技术领域，包括控制理论，估计理论，近似理论和统计学习理论的技术的合成。 这项工作扩展了最近的努力，研究的保真度近似分布自由学习理论，统计学习理论的一个子问题，包括类的分散和依赖的测量过程。 该项目开发的算法将使用多车辆网络进行验证，该网络承载三维激光测距传感器以绘制未知环境。该研究的成功完成将使分散的自主机器人车辆团队能够在复杂环境中绘制未知领域的新颖高效算法。该研究的成功完成将使大规模多车辆传感器网络能够用于许多环境监测和测绘应用，包括海洋环境中的污染物扩散，城市环境中的化学羽流扩散，森林中的野火演变以及自动化农业综合企业中的作物密度。 该研究项目将通过创建新的研究生课程，处理估计，控制和近似理论的合成，为科学和技术建立人力和机构基础设施。 该研究计划将创建一个外展计划，让K-12学生参与机器人和环境测绘。 这项研究的结果将通过在国家和国际会议上的介绍和出版，以及通过一流的同行评审期刊传播。 该研究的传播将使工业界能够使用机器人车辆传感器网络实施和现场可扩展的通用算法，用于分散式映射。

The research objective of this award is to derive, develop, implement and test a theoretical framework for control and estimation methods for sensor networks that enables the derivation of near optimal rates of approximation of the vehicles' environment. The theoretical framework will serve as the foundation for deriving encoding and decoding schemes that are essential to high-bandwidth mobile sensor networks. The approach relies on the synthesis of techniques from several disparate technical fields including control theory, estimation theory, approximation theory and statistical learning theory. The work extends recent efforts that study the fidelity of approximation in distribution free learning theory, a sub-problem of statistical learning theory, to include classes of decentralized and dependent measurement processes. The algorithms developed under this project will be validated using multi-vehicle network that host three dimensional laser-ranging sensors to map unknown environments.The successful completion of the research will enable novel and efficient algorithms for the mapping of unknown fields over complex environments by decentralized, autonomous robotic vehicle teams. The successful completion of the research will enable large-scale, multivehicle sensor networks to be employed in a host of environmental monitoring and mapping applications including contaminant dispersal in a marine environment, chemical plume dispersal in urban environments, wildfire evolution in forests, and crop density in automated agribusiness. This research project will build human and institutional infrastructure for science and technology via the creation of new graduate courses that treat the synthesis of estimation, control and approximation theory. The research program will create an outreach program that engages K-12 students in robotics and environmental mapping. The results of this research will be disseminated through presentation and publication at national and international conferences, as well as through top-notch peer-reviewed journals. The dissemination of the research will enable industry to implement and field scalable and general algorithms for decentralized mapping using robotic vehicle sensor networks.