Optimal Control of a Swarm of Unmanned Aerial Vehicles for Traffic Flow Monitoring in Post-disaster Conditions

该研究项目将率先使用无人机(UAV)的移动无线传感器网络，以感知中断事件后的交通状况和道路扰动。交通状况通常使用固定传感器或众包数据来感知。这样的测量数据通常是稀疏的，因此不能直接用于生成可用的交通地图。使用交通传感器测量数据和关于通常交通模式的过去信息(例如，拥堵模式或历史行驶时间信息)的组合来生成这样的地图。因此，这些地图在大多数情况下都是准确的，但在严重干扰事件中除外。改进当前交通监控系统的一个廉价选择是安装移动传感器，例如一群无人机，它们可以在需要时获得有关中断及其影响的额外数据。该奖项支持对实施和运行这样一个系统的理论基础的研究。本研究解决的传感器最优布置问题将允许系统自动计算每架无人机应采用的最佳路径来感知交通状况，从而能够快速更新交通状况。这项研究将为美国经济带来好处，因为它提供了一种廉价的方法，可以根据需要感知交通情况，以应对中断情况，而不需要部署额外的固定交通传感器，也不需要花费成本。这种多学科的方法将有助于对工程教育产生积极影响，并扩大代表不足的人的参与范围。交通流中的最优移动传感器布置问题对于在中断事件期间实现有效的交通监控至关重要。在这种情况下，交通网络中的中断或容量损失是事先不知道的，可以通过移动传感器(例如无人机)产生的交通测量来估计。通过该项目解决的基本问题将是：给定关于可能的网络中断的先前信息，以及可能给定的交通流量传感器数据(可能包括众包数据)，如何引导一群携带交通传感器的无人机通过交通网络来最小化交通状态估计中的不确定性，并在一定时间范围内提高态势感知？解决这一问题需要同时解决最优配置和交通状态估计问题，这将为更广泛的移动感知系统开辟新的视野。该研究小组将基于交通流的一阶模型，为网络开发一个高效的正向模拟框架。在这个框架的基础上，该团队将提出在交通网络上对一组无人机(具有运动学约束)进行最佳布线的问题，以最大限度地减少有限时间范围内交通状态估计的剩余不确定性，同时估计当前交通状态。该团队还将研究具有部分交通传感器信息的最优路线问题。

This research project will pioneer the use of mobile wireless sensor networks of Unmanned Aerial Vehicles (UAVs), to sense traffic conditions and roadway perturbations following a disruption event. Traffic conditions are commonly sensed using either fixed sensors, or crowdsourced data. Such measurement data is usually sparse, and, as a result, cannot be used directly to generate usable traffic maps. Such maps are generated using a combination of both traffic sensor measurement data and past information about usual traffic patterns (for example congestion patterns, or historical travel time information). These maps are therefore accurate for most situations, except in severe disruption events. An inexpensive option for improving current traffic monitoring systems is to have mobile sensors, for example a swarm of UAVs, which can obtain additional data on disruptions and their impacts when needed. This award supports research on the theoretical foundations for implementing and operating such a system. The optimal sensor placement problem solved by this research will allow the system to automatically compute the best path that each UAV should take to sense the traffic conditions, enabling quick updates on the traffic situation. This research will benefit the U.S. economy by providing an inexpensive means to sense traffic, on demand, for disruption scenarios, without the need and the cost to deploy additional fixed traffic sensors. The multi-disciplinary approach will help positively impact engineering education and broaden participation of underrepresented persons.The optimal mobile sensor placement problem in traffic flow is critical to enable efficient traffic monitoring during disruptions events. In such events, the disruptions or capacity losses in the transportation network are not known beforehand, and can be estimated from traffic measurements generated by mobile sensors (for example UAVs). The fundamental question addressed through this project will be: given prior information on likely network disruptions, and possibly given traffic flow sensor data (which can include crowdsourced data), how can a swarm of UAVs carrying traffic sensors over the transportation network be directed to minimize uncertainty in traffic state estimates and improve situational awareness over some time horizon? Addressing this question requires the simultaneous solution of the optimal placement and traffic state estimation problem, and will open new horizons for mobile sensing systems more generally. The research team will develop an efficient forward simulation framework for networks, based on a first order model of traffic flow. Building on this framework, the team will pose the problem of optimally routing a set of UAVs (with kinematic constraints) over the transportation network to minimize the residual uncertainty of traffic state estimation over a finite time horizon, while simultaneously estimating the current state of traffic. The team will also investigate the problem of optimal routing with partial traffic sensor information.