BREEZE-Boundary Red Emission Zone Estimation Using Unmanned Aerial Vehicles

被引:3
作者
Elmakis, Oren [1 ]
Shaked, Tom [1 ]
Fishbain, Barak [2 ]
Degani, Amir [1 ,2 ]
机构
[1] Technion Israel Inst Technol, Technion Autonomous Syst Program, IL-3200003 Haifa, Israel
[2] Technion Israel Inst Technol, Fac Civil & Environm Engn, Dept Environm Water & Agr Engn, IL-3200003 Haifa, Israel
关键词
unmanned aerial vehicles; air pollution; gas mapping; catastrophic event; chemical leakage;
D O I
10.3390/s22145460
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These teams risk their lives by manually mapping the gas dispersion. This process is currently performed using hand-held gas detectors and requires dense and exhaustive monitoring to achieve reliable maps. However, the conventional mapping process is impaired due to limited human mobility and monitoring capacities. In this context, this paper presents a method for gas sensing using unmanned aerial vehicles. The research focuses on developing a custom path planner-Boundary Red Emission Zone Estimation (BREEZE). BREEZE is an estimation approach that allows efficient red zone delineation by following its boundary. The presented approach improves the gas dispersion mapping process by performing adaptive path planning, monitoring gas dispersion in real time, and analyzing the measurements online. This approach was examined by simulating a cluttered urban site in different environmental conditions. The simulation results show the ability to autonomously perform red zone estimation faster than methods that rely on predetermined paths and with a precision higher than ninety percent.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] OS-BREEZE: Oil Spills Boundary Red Emission Zone Estimation Using Unmanned Surface Vehicles
    Elmakis, Oren
    Polinov, Semion
    Shaked, Tom
    Gordon, Gabi
    Degani, Amir
    SENSORS, 2024, 24 (02)
  • [2] ESTIMATION OF MAIZE BIOMASS USING UNMANNED AERIAL VEHICLES
    Calou, Vinicius B. C.
    Teixeira, Adunias dos S.
    Moreira, Luis C. J.
    da Rocha Neto, Odilio C.
    da Silva, Jose A.
    ENGENHARIA AGRICOLA, 2019, 39 (06): : 744 - 752
  • [3] Ensemble Learning for Pea Yield Estimation Using Unmanned Aerial Vehicles, Red Green Blue, and Multispectral Imagery
    Liu, Zehao
    Ji, Yishan
    Ya, Xiuxiu
    Liu, Rong
    Liu, Zhenxing
    Zong, Xuxiao
    Yang, Tao
    DRONES, 2024, 8 (06)
  • [4] INDOOR ALTITUDE ESTIMATION OF UNMANNED AERIAL VEHICLES USING A BANK OF KALMAN FILTERS
    Yang, Liu
    Wang, Hechuan
    El-Laham, Yousef
    Lamas Fonte, Jose Ignacio
    Perez, David Trillo
    Bugallo, Monica F.
    2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 5455 - 5459
  • [5] Survey on Monocular Depth Estimation for Unmanned Aerial Vehicles using Deep Learning
    Florea, Horatiu
    Nedevschi, Sergiu
    2022 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING, ICCP, 2022, : 319 - 326
  • [6] Ship emission monitoring with a joint mode of motherships and unmanned aerial vehicles
    Dan, Zhuge
    Du, Jianhui
    Zhen, Lu
    Wang, Shuaian
    Wu, Peng
    COMPUTERS & OPERATIONS RESEARCH, 2025, 179
  • [7] VOLUME MEASUREMENTS OF ROCKFALLS USING UNMANNED AERIAL VEHICLES
    Car, Marijan
    Kacunic, Danijela Juric
    Libric, Lovorka
    ROAD AND RAIL INFRASTRUCTURE IV, 2016, : 301 - 307
  • [8] Large structures monitoring using unmanned aerial vehicles
    Chiu, W. K.
    Ong, W. H.
    Kuen, T.
    Courtney, F.
    STRUCTURAL HEALTH MONITORING - FROM SENSING TO DIAGNOSIS AND PROGNOSIS, 2017, 188 : 415 - 423
  • [9] Precision farming using Unmanned Aerial and Ground Vehicles
    Vasudevan, Ashwin
    Kumar, Ajith D.
    Bhuvaneswari, N. S.
    2016 IEEE INTERNATIONAL CONFERENCE ON TECHNOLOGICAL INNOVATIONS IN ICT FOR AGRICULTURE AND RURAL DEVELOPMENT (TIAR), 2016, : 146 - 150
  • [10] Localization of Unmanned Aerial Vehicles Using Terrain Classification from Aerial Images
    Masselli, Andreas
    Hanten, Richard
    Zell, Andreas
    INTELLIGENT AUTONOMOUS SYSTEMS 13, 2016, 302 : 831 - 842