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

协作研究：CPS：中：使用智能协作飞行和地面系统进行荒地火灾观测、管理和疏散

• 批准号: 2039026
• 负责人: Fatemeh Afghah
• 金额: $ 64.99万
• 依托单位: Northern Arizona University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039026&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Wildland

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.

不断增加的野火成本（反映了气候变化和荒地内的发展）促使人们呼吁建立新的国家能力来管理野火。由于缺乏全面、有弹性、灵活且具有成本效益的监控协议，无人机系统（UAS）的巨大潜力尚未在该领域得到充分利用。该项目将开发基于无人机的火灾管理策略，以最佳、高效和安全的方式使用自主无人机（UAV），在火灾探测、管理和疏散阶段协助第一响应人员。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) 的合作成果。该团队与美国林务局 (USFS) 太平洋西北研究站、凯巴布国家森林 (NF) 以及亚利桑那州林业和消防管理部建立了持续合作，在规定和管理的火灾期间进行多次现场测试。该提案的目标是开发一个综合框架，满足未满足的荒地火灾管理需求，通过在火灾管理行动的不同阶段使用低成本小型自主无人机以及地面车辆网络，在科学和工程方法方面取得重大进展，包括：（ i) 使用自主无人机在偏远地区和森林地区进行早期检测； (ii) 在飞行无人机上快速主动绘制火灾热图； (iii) 火势蔓延的实时视频流； (iv) 使用自主无人机寻找最佳疏散路径，引导地面车辆和消防员快速安全疏散。该项目将通过开发以下内容来推进灾害管理的前沿：(i) 一种基于无人机的创新森林火灾探测和监测技术，用于在难以进入的地区快速干预，以最少的人为干预来保护消防员的生命； (ii) 多级火力建模，利用无人机的快速火力测绘提供战略性、事件规模和新的机载低计算策略； (iii) 基于有限推理的规划机制，无人机可以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、最安全的疏散道路。所开发的技术将转化为广泛的应用，例如灾难（洪水、火灾、泥石流、恐怖主义）管理，这些应用需要在有限的人为干预下进行快速搜索、监视和响应。该项目还将为未来的工程课程做出贡献，并追求研究和教育的实质性整合，同时吸引女性和代表性不足的少数族裔学生，为 K-12 学生开发实践研究实验。该项目是为了响应 NSF 网络物理Systems 20-563 征集。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Aerial imagery pile burn detection using deep learning: The FLAME dataset

使用深度学习进行航拍图像桩燃烧检测：FLAME 数据集

• DOI: 10.1016/j.comnet.2021.108001
• 发表时间: 2021-07
• 期刊: Computer Networks
• 影响因子: 5.6
• 作者: Shamsoshoara, Alireza;Afghah, Fatemeh;Razi, Abolfazl;Zheng, Liming;Fulé, Peter Z.;Blasch, Erik
• 通讯作者: Blasch, Erik

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

利用模仿学习在偏远灾区进行无人机辅助通信

• DOI: 10.1109/ojcoms.2021.3067001
• 发表时间: 2021-01
• 期刊: IEEE Open Journal of the Communications Society
• 影响因子: 7.9
• 作者: Shamsoshoara, Alireza;Afghah, Fatemeh;Blasch, Erik;Ashdown, Jonathan;Bennis, Mehdi
• 通讯作者: Bennis, Mehdi

An Exhaustive Study of Using Commercial LTE Network for UAV Communication in Rural Areas

农村地区使用商用 LTE 网络进行无人机通信的详尽研究

• DOI: 10.1109/iccworkshops50388.2021.9473547
• 发表时间: 2021-06
• 期刊: 2021 IEEE International Conference on Communications Workshops (ICC Workshops
• 作者: Gharib, Mohammed;Nandadapu, Shashidhar;Afghah, Fatemeh
• 通讯作者: Afghah, Fatemeh

Aerial images for pile fire detection using drones (UAVs)

使用无人机 (UAV) 进行桩火探测的航空图像

• DOI: 10.21227/qad6-r683
• 发表时间: 2020-01
• 期刊: IEEE DataPort
• 作者: Shamsoshoara, Alireza;Afghah, Fatemeh;Razi, Abolfazl;Zheng, Liming;Fulé, Peter
• 通讯作者: Fulé, Peter