Robotic Based Fire Detection in Smart Manufacturing Facilities

被引:8
|
作者
Rehman, Adeel Ur [1 ]
Necsulescu, Dan-Sorin [1 ]
Sasiadek, Jurek [2 ]
机构
[1] Univ Ottawa, Dept Mech Engn, Ottawa, ON K1N 6N5, Canada
[2] Carleton Univ, Dept Mech & Aerosp Engn, Ottawa, ON K1S 5B6, Canada
来源
IFAC PAPERSONLINE | 2015年 / 48卷 / 03期
关键词
Robot; Modified Voting Logic; fire detection; sensor fusion; increasing gradient navigation;
D O I
10.1016/j.ifacol.2015.06.321
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A smart fire detection system is a necessary part of a smart manufacturing facility. Autonomous robots may be deployed to seek out a potential source of fire in the industrial environment, approach, investigate and declare the presence or absence of fire based on several sensor fusion techniques. A novel approach of the above mentioned topic is described in this paper. A new technique, introduced as "Modified Voting Logic" is explained. The robot uses sensor readings comparison to approach the source and a smart obstacle avoidance system to avoid obstacles together with Modified Voting Logic to declare a fire threat. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:1640 / 1645
页数:6
相关论文
共 50 条
  • [41] Smart grid-based manufacturing by nanoparticle analysis with evolutionary optimization probability detection
    Wang, Jiyong
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2023,
  • [42] A robotic grasp detection method based on auto-annotated dataset in disordered manufacturing scenarios
    Zhang, Tongjia
    Zhang, Chengrui
    Hu, Tianliang
    ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2022, 76
  • [43] A Review of Robotic Fish Based on Smart Materials
    Ma, Shiwei
    Zhao, Quanliang
    Ding, Meixi
    Zhang, Mengying
    Zhao, Lei
    Huang, Can
    Zhang, Jie
    Liang, Xu
    Yuan, Junjie
    Wang, Xingtao
    He, Guangping
    BIOMIMETICS, 2023, 8 (02)
  • [44] A Smart Glove Based Robotic Hand Control
    Cui, Ziang
    Chu, Meng
    Hu, Yuhong
    Gao, Shuo
    PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON FLEXIBLE AND PRINTABLE SENSORS AND SYSTEMS (FLEPS), 2021,
  • [45] Sharp Feature Detection as a Useful Tool in Smart Manufacturing
    Prochazkova, Jana
    Prochazka, David
    Landa, Jaromir
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (07)
  • [46] NFV-driven intrusion detection for smart manufacturing
    Behnke, Daniel
    Mueller, Marcel
    Boek, Patrick-Benjamin
    Schneider, Stefan
    Peuster, Manuel
    Karl, Holger
    Rocha, Alberto
    Mesquita, Miguel
    Bonnet, Jose
    2019 IEEE CONFERENCE ON NETWORK FUNCTION VIRTUALIZATION AND SOFTWARE DEFINED NETWORKS (IEEE NFV-SDN), 2019,
  • [47] Learning with supervised data for anomaly detection in smart manufacturing
    He, Meiling
    Petering, Matthew
    LaCasse, Phillip
    Otieno, Wilkistar
    Maturana, Francisco
    INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2023, 36 (09) : 1331 - 1344
  • [48] Anomaly detection in Smart-manufacturing era: A review
    Elia, Inaki
    Pagola, Miguel
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 139
  • [49] Innovative pre-fire alert smart detection system-based embedded system
    Al-Chalabi, Sadeem Marouf
    Al-Chalabi, Atheer Marouf
    Al-Khafaji, Rami Ali
    AIP ADVANCES, 2024, 14 (05)
  • [50] Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach
    Avazov, Kuldoshbay
    Mukhiddinov, Mukhriddin
    Makhmudov, Fazliddin
    Cho, Young Im
    ELECTRONICS, 2022, 11 (01)