Collaborative Research: CPS: Medium: Wildland Fire Observation, Management, and Evacuation using Intelligent Collaborative Flying and Ground Systems

不断增加的野火成本-反映了气候变化和荒野地区的发展-推动了对管理野火的新的国家能力的呼唤。由于缺乏全面、弹性、灵活和成本效益高的监测协议，无人机(UAS)的巨大潜力在这一领域尚未得到充分利用。该项目将开发基于UAS的火灾管理战略，以最佳、高效和安全的方式使用自动无人机(UAV)在火灾检测、管理和疏散阶段协助第一反应人员。该项目是北亚利桑那大学(NAU)、佐治亚理工学院(GaTech)、沙漠研究所(DRI)和国家大气研究中心(NCAR)共同努力的成果。该团队与太平洋西北研究站的美国林业局(USFS)、凯巴布国家森林(NF)和亚利桑那州林业和火灾管理部建立了持续的合作，在规定和管理的火灾期间进行多次实地测试。这项提议的目标是开发一个综合框架，满足未得到满足的荒地火灾管理需求，在科学和工程方法方面取得重大进展，方法是在火灾管理行动的不同阶段使用低成本和小型自动无人机以及地面车辆，包括：(1)使用自动无人机在偏远和森林地区早期发现；(2)在飞行的无人机上快速主动绘制火灾热图；(3)实时播放火灾蔓延的视频；(4)使用自动无人机指导地面车辆和消防员快速安全疏散，找到最佳疏散路径。该项目将通过开发：(I)创新的基于无人机的森林火灾探测和监测技术，在难以进入的地区进行快速干预，将人的干预降至最低，以保护消防员的生命；(Ii)多层次火灾建模，利用无人机的快速火灾映射，提供战略性、事件规模和新的机上低计算战术；(Iii)基于受限推理的规划机制，无人机在高度动态和不确定的危险区域为消防员和消防车确定最快和最安全的疏散路线。开发的技术将转化为广泛的应用，如灾难(洪水、火灾、泥石流、恐怖主义)管理，在这些应用中，需要快速搜索、监视和反应，而人工干预有限。该项目还将为未来的工程课程做出贡献，并寻求研究和教育的实质性整合，同时还将吸引女性和代表性不足的少数族裔学生，为K-12学生开发动手研究实验。该项目是对美国国家科学基金会网络物理系统20-563项目的响应。该奖项反映了美国国家科学基金会的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为是值得支持的。

Increasing wildfire costs---a reflection of climate variability and development within wildlands---drive calls for new national capabilities to manage wildfires. The great potential of unmanned aerial systems (UAS) has not yet been fully utilized in this domain due to the lack of holistic, resilient, flexible, and cost-effective monitoring protocols. This project will develop UAS-based fire management strategies to use autonomous unmanned aerial vehicles (UAVs) in an optimal, efficient, and safe way to assist the first responders during the fire detection, management, and evacuation stages. The project is a collaborative effort between Northern Arizona University (NAU), Georgia Institute of Technology (GaTech), Desert Research Institute (DRI), and the National Center for Atmospheric Research (NCAR). The team has established ongoing collaborations with the U.S. Forest Service (USFS) in Pacific Northwest Research Station, Kaibab National Forest (NF), and Arizona Department of Forestry and Fire Management to perform multiple field tests during the prescribed and managed fires. This proposal's objective is to develop an integrated framework satisfying unmet wildland fire management needs, with key advances in scientific and engineering methods by using a network of low-cost and small autonomous UAVs along with ground vehicles during different stages of fire management operations including: (i) early detection in remote and forest areas using autonomous UAVs; (ii) fast active geo-mapping of the fire heat map on flying drones; (iii) real-time video streaming of the fire spread; and (iv) finding optimal evacuation paths using autonomous UAVs to guide the ground vehicles and firefighters for fast and safe evacuation. This project will advance the frontier of disaster management by developing: (i) an innovative drone-based forest fire detection and monitoring technology for rapid intervention in hard-to-access areas with minimal human intervention to protect firefighter lives; (ii) multi-level fire modeling to offer strategic, event-scale, and new on-board, low-computation tactics using fast fire mapping from UAVs; and (iii) a bounded reasoning-based planning mechanism where the UAVs identify the fastest and safest evacuation roads for firefighters and fire-trucks in highly dynamic and uncertain dangerous zones. The developed technologies will be translational to a broad range of applications such as disaster (flooding, fire, mud slides, terrorism) management, where quick search, surveillance, and responses are required with limited human interventions. This project will also contribute to future engineering curricula and pursue a substantial integration of research and education while also engaging female and underrepresented minority students, developing hands-on research experiments for K-12 students. This project is in response to the NSF Cyber-Physical Systems 20-563 solicitation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.