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，使车辆环境的近乎最佳的近似率得以推导。该理论框架将作为推导高带宽移动传感器网络所必需的编解码方案的基础。该方法依赖于几个不同技术领域的技术的综合，包括控制理论、估计理论、近似理论和统计学习理论。这项工作扩展了最近研究分布自由学习理论中近似的保真度的努力，该理论是统计学习理论的一个子问题，包括分散的和相关的测量过程的类。在该项目下开发的算法将在托管三维激光测距传感器的多车辆网络中进行验证，以绘制未知环境的地图。研究的成功完成将使分散、自主的机器人车辆团队能够在复杂环境中绘制未知区域的新型高效算法。这项研究的成功完成将使大规模、多车辆传感器网络能够应用于一系列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。这一研究项目将通过创建新的研究生课程，处理估计、控制和近似理论的综合，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项目，让K-12年级的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陈述和发表以及通过一流的同行评议期刊来传播。这项研究的传播将使工业能够利用机器人车辆传感器网络实施和推广可扩展的通用算法，以进行分散的地图绘制。

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.