DDDAS-SMRP: Coordinated Control of Multiple Mobile Observing Platforms for Weather Forecast Improvement

改进天气预报的多个移动观测平台的协调控制Jonathan How、Nicholas Roy和Jim Hansen麻省理工学院对自然现象的准确预测依赖于准确的物理模型、良好的计算机模拟和对系统当前状态的准确估计。例如，提前几天预测加州上空的风暴需要良好的天气发展模式，以及对太平洋分散地区的温度和压力等天气变量的精确和准确的知识。获取当前系统状态的这种精确知识通常需要大量的传感器测量，但后续预测的质量与可用的总传感器资源(传感器的数量和种类)以及传感器的使用方式(传感器的时空分布)密切相关。目前的传感器系统从几条可能的飞行路线中选择，以长时间视野飞行几架有人驾驶的飞机。虽然有用，但由此产生的测量策略本质上是不协调的、预先编程的，并且不容易修改。数据同化、集合构建(蒙特卡罗抽样)和目标确定方法中的假设往往不一致，导致数据收集计划不够理想。无人机的开发是为了降低载人飞行的成本，我们设想了一种使用分布式传感技术的多无人机场景，以显著缩短规划者的响应时间。我们的研究将导致一个新的框架，以协调这一移动传感资产团队，提供更有效的测量策略和更准确的方法来捕获天气系统动态中的空间相关性。我们还将演示使用一致的策略进行数据同化、集合构建和目标确定的重要性。这项工作的关键步骤将是利用天气系统动力学固有的结构来开发一种独特的中央/分布式混合规划策略，该策略既使用多分辨率优化技术来解决全局任务分配问题，又使用强化学习来解决局部任务规划问题。这项工作产生的规划算法将适用于广泛的系统，这些系统通过信息(由一个人影响所有世界模型的测量)和任务(标准冲突避免)表现出强耦合。这项研究将促进非线性天气预报和规划/控制社区之间的强大联系。通过协同结合两个不同领域的技术，我们在DDDAS计划内的研究工作将开发一种新的天气预报测量战略，将模式预报和适应性观测紧密联系在一起。我们的工作将为最近为其他(主要是国防部)应用开发的无人机团队扩展和应用新的协调技术。这代表着活跃的天气传感模型的范式转变，因为该项目将首次展示如何有效地利用移动传感器网络来优化数据收集，从而推动对天气等自然现象的预测。我们的结果可能会带来更好的天气预报，这在未来可能会拯救生命和金钱。

Coordinated Control of Multiple Mobile Observing Platforms for Weather Forecast ImprovementJonathan How, Nicholas Roy, and Jim HansenMassachusetts Institute of TechnologyAccurate predictions of natural phenomena rely on accurate physical models, good computer simulations, and accurate estimates of the current state of the system. For example, predicting storms over California several days in advance requires good models of the weather development and precise and accurate knowledge of weather variables such as temperature and pressure in distributed regions of the Pacific. Acquiring this precise knowledge of the current system state typically requires a large number of sensor measurements, but the quality of the subsequent prediction is a strong function of both the total sensor resources available (number and kind of sensors), and also how the sensors are used (the spatio-temporal distribution of the sensors). Current sensor systems fly a few manned planes with long time horizons, selecting from a handful of possible flight paths. While useful, the resulting measurement strategies are essentially uncoordinated, preprogrammed and not easily modified. There are often inconsistencies between the assumptions in the data assimilation, ensemble construction (Monte Carlo sampling), and targeting methodologies, which lead to sub-optimal plans for data gathering. UAVs have been developed to reduce the costs of manned flights, and we envisage a scenario with multiple UAVs using distributed sensing techniques to significantly reduce the planner response times. Our research will lead to a new framework for coordinating this team of mobile sensing assets that provides more efficient measurement strategies and a more accurate means of capturing spatial correlations in weather system dynamics. We will also demonstrate the importance of using consistent strategies for the data assimilation, ensemble construction, and targeting. The key step in this work will be to exploit the structure inherent in weather system dynamics to develop a unique hybrid central/distributed planning strategy that uses both multi-resolution optimization techniques to solve the global task assignment and reinforcement learning to solve the local task planning problems. The planning algorithms resulting from this work will be applicable to a wide range of systems that also exhibit strong coupling through both the information (measurements taken by one influence the world models of all) and the tasks (standard conflict avoidance).This research will facilitate a strong linkage between the nonlinear weather prediction and planning/control communities. By synergistically combining technologies from two diverse fields, our research effort within the DDDAS program will develop a new measurement strategy for weather prediction that closely links model predictions and adaptive observations. Our work will extend and apply new coordination techniques for teams of UAVs that have been recently developed for other (mainly DoD) applications. This represents a paradigm shift in active weather sensing models, in that this project will be the first to show how a network of mobile sensors can be used efficiently to optimize the data gathering that drives predictions of natural phenomena such as weather. Our results could lead to substantially better weather predictions, which in the future could save lives and money.