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

气象预报中多个移动的观测平台的协调控制麻省理工学院的Jonathan How、Nicholas Roy和Jim Hansen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，提前几天预测加州的风暴需要良好的天气发展模型和精确准确的天气变量知识，如太平洋分布区域的温度和压力。 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，但是后续预测的质量是可用的总传感器资源（传感器的数量和种类）以及传感器如何使用（传感器的时空分布）两者的强函数。 目前的传感器系统飞行几架具有长时间视野的载人飞机，从少数可能的飞行路径中选择。虽然有用，但由此产生的测量策略基本上是不协调的，预先编程的，不容易修改。在数据同化、集合构造（蒙特卡罗抽样）和目标确定方法中的假设之间往往存在不一致，从而导致数据收集的次优计划。 无人机已经开发出来，以减少载人飞行的成本，我们设想的情况下，多个无人机使用分布式传感技术，以显着减少计划响应时间。我们的研究将导致一个新的框架，用于协调这个团队的移动的传感资产，提供更有效的测量策略和更准确的手段，捕捉天气系统动态的空间相关性。我们还将证明使用一致的数据同化，合奏建设和目标的战略的重要性。 这项工作的关键一步将是利用天气系统动力学固有的结构，开发一种独特的混合中央/分布式规划策略，使用多分辨率优化技术来解决全局任务分配和强化学习来解决局部任务规划问题。从这项工作中产生的规划算法将适用于广泛的系统，也表现出强耦合，通过信息（由一个影响世界模型的测量）和任务（标准的冲突避免）。这项研究将促进非线性天气预报和规划/控制社区之间的紧密联系。通过协同结合来自两个不同领域的技术，我们在DDDAS计划中的研究工作将开发一种新的天气预测测量策略，将模型预测和自适应观测紧密联系起来。我们的工作将扩展和应用新的协调技术，用于最近为其他（主要是国防部）应用开发的无人机团队。这代表了主动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使用移动的传感器网络来优化数据收集，从而推动天气等自然现象的预测。我们的研究结果可以大大改善天气预报，这在未来可以挽救生命和金钱。

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.