CHS: Small: Collaborative Research: Spatio-Temporal Situational Awareness in Large-Scale Disasters Using Low-Cost Unmanned Aerial Vehicles

CHS：小型：合作研究：利用低成本无人机实现大规模灾害中的时空态势感知

• 批准号: 1528137
• 负责人: David Feil-Seifer
• 金额: $ 16.67万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528137&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Spatio

Providing real-time situational awareness is a critical but immensely challenging component in the management of large-scale disasters such as wildfires, where incident managers need to make timely decisions and allocate resources. Yet gathering accurate real-time information is difficult due to the multi-scale spatio-temporal nature of the event, the overwhelming amount of data that has to be processed in near real-time, the heterogeneous nature of the technological resources involved, and the complex interdependencies and interactions between human and technological entities. Recent advances in various areas of cyber-physical and information systems, including sensing, mapping, communication, computing technologies, and unmanned systems, have provided an unprecedented opportunity to revolutionize the acquisition of situational awareness in a large-scale disaster. The objective of this multi-institutional project is to conduct fundamental research aimed at creating user-centered control and algorithmic tools that integrate real-time sensory data from multi-rotor unmanned aerial vehicles (UAVs) into effective fire-predictor software, which will allow a team of UAVs equipped with electro-optical sensors to generate situational awareness in a large-scale wildfire. The PIs will develop a reliable and affordable UAV system that is portable, safe, and easy to operate by first responders (especially firefighters and forestry officials), and which will enable them to make informed decisions which can optimize resource allocation and thereby save both property and lives. With the imminent inclusion of UAVs in the national airspace, the technologies developed in this research will have potential broad applicability to a number of other civilian applications such as law enforcement and border patrol. Project objectives will be met by pursuing specific aims including the design of user-centered analytical and algorithmic tools for robust and safe motion control of UAVs for gathering information, the development of spatio-temporal situational awareness using real-time data and a fire propagation model, and the development of a command, control and communication (C3) framework for emergency management personnel integration. The PIs will create new algorithms for UAV trajectory generation that resourcefully carry out monitoring in a dynamic environment, along with new cooperative control algorithms for a team of UAVs tasked with dynamic perimeter tracking that are robust to addition and/or deletion of UAVs. They will devise distributed filtering methods coupled with reduced order modeling techniques based on real-time spatio-temporal decomposition, to seamlessly generate situational awareness in a computationally efficient manner. Finally, the PIs will develop simulation and field capacity, while studying human-robot interaction issues pertaining to the wildfire application. The PIs' user-centered design approach is novel, in that it will address not only usability issues related to the developed system, but also issues such as deployment, training, and changes in operational paradigms. To ensure project outcomes meet the needs of the target user community, the PI team will be working in collaboration with the City of Cincinnati's Fire Department and the State of West Virginia's Division of Forestry.

在野火等大规模灾难的管理中，提供实时态势感知是一个关键但极具挑战性的组成部分，事件管理人员需要及时做出决策并分配资源。然而，由于事件的多尺度时空性质、必须近实时处理的大量数据、所涉技术资源的异质性以及人和技术实体之间复杂的相互依存和相互作用，收集准确的实时信息是困难的。网络物理和信息系统各个领域的最新进展，包括传感、测绘、通信、计算技术和无人系统，为在大规模灾难中获得态势感知提供了前所未有的机会。这个多机构项目的目标是进行基础研究，旨在创建以用户为中心的控制和算法工具，将来自多旋翼无人机(UAV)的实时传感数据集成到有效的火灾预测器软件中，这将使配备光电传感器的无人机团队能够在大规模野火中产生态势感知。PIS将开发一种可靠且负担得起的无人机系统，该系统便携、安全、易于急救人员(特别是消防员和林业官员)操作，使他们能够做出明智的决策，从而优化资源分配，从而拯救财产和生命。随着无人机即将进入国家空域，这项研究开发的技术将有可能广泛适用于执法和边境巡逻等其他一些民用应用。将通过追求具体目标来实现项目目标，包括设计以用户为中心的分析和算法工具，用于无人机用于收集信息的稳健和安全的运动控制，利用实时数据和火灾传播模型开发时空态势感知，以及为应急管理人员集成开发指挥、控制和通信(C3)框架。PI将为无人机轨迹生成创建新的算法，以便在动态环境中足智多谋地进行监控，以及为负责动态周长跟踪的无人机团队创建新的协作控制算法，这些算法对无人机的添加和/或删除具有健壮性。他们将设计分布式过滤方法，并结合基于实时时空分解的降阶建模技术，以计算高效的方式无缝地生成态势感知。最后，PI将开发模拟和现场能力，同时研究与野火应用有关的人-机器人交互问题。PIS以用户为中心的设计方法是新颖的，因为它不仅将解决与已开发系统相关的可用性问题，而且还将解决部署、培训和操作范例变化等问题。为了确保项目成果满足目标用户社区的需求，PI团队将与辛辛那提市消防局和西弗吉尼亚州林业部合作。