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

灾后交通流量监测中无人机群的优化控制

• 批准号: 1636154
• 负责人: Christian Claudel
• 金额: $ 38万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636154&HistoricalAwards=false
• 关键词: Optimal; Control; Swarm; Unmanned; Aerial

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.

该研究项目将率先使用无人机（UAV）的移动无线传感器网络来感知中断事件后的交通状况和道路扰动。通常使用固定传感器或众包数据来感知交通状况。此类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。此类地图是使用交通传感器测量数据和有关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）的组合来生成的。因此，除了严重破坏事件外，这些地图在大多数情况下都是准确的。改进当前交通监控系统的一个廉价选择是拥有移动传感器，例如一群无人机，它们可以在需要时获取有关中断及其影响的额外数据。该奖项支持对实施和操作此类系统的理论基础的研究。这项研究解决的最佳传感器放置问题将使系统能够自动计算每架无人机感知交通状况应采取的最佳路径，从而能够快速更新交通状况。这项研究将通过提供一种廉价的方法来按需感知交通中断场景，从而使美国经济受益，而无需部署额外的固定交通传感器，也无需花费成本。多学科方法将有助于对工程教育产生积极影响，并扩大代表性不足人群的参与。交通流中的最佳移动传感器放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。在此类事件中，运输网络中的中断或容量损失事先是未知的，可以根据移动传感器（例如无人机）生成的流量测量结果进行估计。该项目解决的基本问题是：给定有关可能的网络中断的先前信息，以及可能给定的交通流传感器数据（可以包括众包数据），如何引导一群在交通网络上携带交通传感器的无人机最大限度地减少交通状态估计的不确定性并在一段时间内提高态势感知？解决这个问题需要同时解决最佳布局和交通状态估计问题，并将为更广泛的移动传感系统开辟新的视野。研究团队将基于交通流的一阶模型开发高效的网络正向仿真框架。在此框架的基础上，该团队将提出在交通网络上优化一组无人机（具有运动学约束）的路线问题，以最大限度地减少有限时间范围内交通状态估计的残余不确定性，同时估计当前的交通状态。该团队还将研究利用部分交通传感器信息的最优路由问题。

项目成果

A Stochastic Formulation of the Optimal Boundary Control Problem Involving the Lighthill Whitham Richards Model

涉及Lighthill Whitham Richards模型的最优边界控制问题的随机表述

• DOI: 10.1016/j.ifacol.2018.07.055
• 发表时间: 2024-09-14
• 期刊: IFAC-PapersOnLine
• 作者: Hao Liu;C. Claudel;R. Machemehl
• 通讯作者: R. Machemehl

Semianalytical Solutions to the Lighthill-Whitham-Richards Equation With Time-Switched Triangular Diagrams: Application to Variable Speed Limit Traffic Control

带时间切换三角图的 Lighthill-Whitham-Richards 方程的半解析解：在变速限制交通控制中的应用

• DOI: 10.1109/tase.2020.3039836
• 发表时间: 2020-12
• 期刊: IEEE Transactions on Automation Science and Engineering
• 影响因子: 5.6
• 作者: Shao, Yang;Levin, Michael W.;Boyles, Stephen D.;Claudel, Christian G.
• 通讯作者: Claudel, Christian G.