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

该项目致力于开发可自行部署的空中机器人系统，以实现新的现场大气科学应用。固定翼空中机器人技术已经发展到可以在远离远程操作员的地方执行持续采样任务的程度。同样，复杂的大气现象也可以近实时模拟，逼真度不断提高。此外，云计算技术可以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通道上的环境模型相结合，能够收集对检查大气现象的基本行为至关重要的信息。这里提出的空中机器人系统将缩小传统平台收集回答广泛科学问题所需数据的能力方面的重大能力差距。该项目利用机器人技术、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，实现将云机器人技术带到云端的愿景。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：多个固定翼无人驾驶飞机、可部署的拉格朗日漂移器、移动的多普勒雷达、移动的指挥和控制站、现场和实验室中的分布式计算节点、连接分散元件的网络中心中间件、以及一个自主决策架构，它在实地感知和新的在线数值天气预报工具之间建立了闭环。

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.