NRI: Collaborative Research: Targeted Observation of Severe Local Storms Using Aerial Robots

该项目致力于自行部署空中机器人系统的开发，这将使新的就地大气科学应用成为可能。固定翼空中机器人技术已经发展到了这样一个地步，即平台在远离远程操作员的情况下执行持续的采样任务。同样，随着保真度的提高，复杂的大气现象可以近乎实时地模拟。此外，云计算技术支持在大型动态数据集上进行分布式计算。将自主机载传感器与分散在多个通信和计算渠道上的环境模型相结合，可以收集研究大气现象基本行为所必需的信息。这里提出的空中机器人系统将填补传统平台收集数据回答广泛科学问题所需能力方面的重大能力差距。这项工作的激励应用是提高龙卷风警报的准确性和提前期。拟议的项目利用机器人学、无人系统、网络控制、无线通信、主动传感和大气科学等领域的技术来实现将云机器人送上云的愿景。自主自部署空中机器人系统由多个机器人传感器和分布式计算节点组成，包括：多个固定翼无人机、可部署的拉格朗日漂移器、移动多普勒雷达、移动指挥和控制站、野外和实验室的分布式计算节点、连接分散元素的以网络为中心的中间件，以及关闭野外传感和新的在线数值天气预报工具之间的回路的自主决策体系结构。

This project addresses the development of self-deploying aerial robotic systems that will enable new in-situ atmospheric science applications. Fixed-wing aerial robotic technology has advanced to the point where platforms fly persistent sampling missions far from remote operators. Likewise, complex atmospheric phenomena can be simulated in near real-time with increasing levels of fidelity. Furthermore, cloud computing technology enables distributed computation on large, dynamic data sets. Combining autonomous airborne sensors with environmental models dispersed over multiple communication and computation channels enables the collection of information essential for examining the fundamental behavior of atmospheric phenomena. The aerial robotic system proposed here will close significant capability gaps in conventional platform's abilities to collect the data necessary to answer a wide range of scientific questions. The motivating application for this work is improvement in the accuracy and lead-time of tornado warnings.The proposed project draws on techniques in the areas of robotics, unmanned systems, networked control, wireless communication, active sensing, and atmospheric science to realize the vision of bringing cloud robotics to the clouds. The autonomous self-deploying aerial robotic systems is comprised of multiple robotic sensors and distributed computing nodes including: multiple fixed-wing unmanned aircraft, deployable Lagrangian drifters, mobile Doppler radar, mobile command and control stations, distributed computation nodes in the field and in the lab, a net-centric middleware connecting the dispersed elements, and an autonomous decision-making architecture that closes the loop between sensing in the field and new online numerical weather prediction tools.