Fire Technology

, Volume 52, Issue 5, pp 1343–1368 | Cite as

Autonomous Fire Suppression System for Use in High and Low Visibility Environments by Visual Servoing

  • Joshua G. McNeil
  • Brian Y. Lattimer


An autonomous fire suppression system was developed for localized fire suppression in high and low visibility environments. The system contains a multispectral sensor suite, including UV sensors and infrared stereovision, to detect and target a fire for suppression. The UV sensor provides an alert to the system to begin fire detection. IR imagery is used to segment fire from the field of view and target the base of the fire and IR stereovision to determine the 3D coordinates of the fire. IR tracking provides continuously updated information on the size and intensity of the fire before and during suppression and alerts the system when to cease suppression activity. Visual servoing is used to correctly position a nozzle based on feedback of changes in the fire location and size. The autonomous system was used to suppress wood crib fires (40 kW to 50 kW) in high and low visibility environments and at varying distances (2.8 m to 5.5 m) and elevations (0.4 m to 1.3 m). The suppression time in clear conditions was 3.72 s ± 1.51 s and 4.49 s ± 1.62 s in low visibility conditions. To simulate wind effects and inaccurate initial target coordinates, forced offsets were input to the system to show effectiveness of the feedback control algorithm when an initial estimate of spray trajectory does not accurately spray the center base of the fire. System performance with a forced offset resulted in suppression times of 4.11 s ± 0.84 s.


Autonomous fire suppression Fire detection Suppression systems IR stereovision Visual servoing 



We would like to acknowledge our funding through the Office of Naval Research Grant No. N00014-11-1-0074, scientific monitor Dr. Thomas McKenna.


  1. 1.
    Karter Jr MJ, Molis JL (2014) US firefighter injuries-2013. National Fire Protection Association, QuincyGoogle Scholar
  2. 2.
    Fahy R, Leblanc P, Molis J (2014) Firefighter fatalities in the United States, 2013. National Fire Protection Association, QuincyGoogle Scholar
  3. 3.
    Kim J-H, Starr JW, Lattimer BY (2014) Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through smoke. Fire Technol 51(4):823–845. doi: 10.1007/s10694-014-0413-6 CrossRefGoogle Scholar
  4. 4.
    Starr, JW, Lattimer BY (2013) Application of thermal infrared stereo vision in fire environments. Paper presented at the IEEE/ASME international conference on advanced intelligent mechatronics (AIM), Wollongong, Australia, 2013Google Scholar
  5. 5.
    Kim J-H, Lattimer BY (2013) Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot. Paper presented at the IEEE/ASME international conference on advanced intelligent mechatronics (AIM), Wollongong, Australia, 2013Google Scholar
  6. 6.
    Starr JW, Lattimer BY (2013) Evaluation of navigation sensors in fire smoke environments. Fire Technol 50(6):1459–1481. doi: 10.1007/s10694-013-0356-3 CrossRefGoogle Scholar
  7. 7.
    Starr JW, Lattimer BY (2012) A comparison of IR stereo vision and LIDAR for use in fire environments. In: 2012 IEEE Sensors. IEEE, pp 1–4Google Scholar
  8. 8.
    McNeil JG, Starr JW, Lattimer BY (2013) Autonomous fire suppression using multispectral sensors. Paper presented at the 2013 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), Wollongong, Australia, 9–12 July 2013Google Scholar
  9. 9.
    Penders J, Alboul L, Witkowski U, Naghsh A, Saez-Pons J, Herbrechtsmeier S, El-Habbal M (2011) A robot swarm assisting a human fire-fighter. Adv Robot 25(1–2):93–117CrossRefGoogle Scholar
  10. 10.
    Kim Y-D, Kim Y-G, Lee S-H, Kang J-H, An J (2009) Portable fire evacuation guide robot system. Paper presented at the 2009 IEEE/RSJ international conference on intelligent robots and systems, St. Louis, USAGoogle Scholar
  11. 11.
    Liljeback P, Stavdahl O, Beitnes A (2006) SnakeFighter—development of a water hydraulic fire fighting snake robot.Google Scholar
  12. 12.
    Pack DJ (2004) Fire-fighting mobile robotics and interdisciplinary design-comparative perspectives. IEEE Trans Educ 47(3):369–376CrossRefGoogle Scholar
  13. 13.
    Miyazawa K (2002) Fire robots developed by the Tokyo Fire Department. Adv Robot 16(6):553–556MathSciNetCrossRefGoogle Scholar
  14. 14.
    Dearie S, Fisher K, Rajala B, Wasson S Design and construction of a fully autonomous fire fighting robot. In: Electrical insulation conference and electrical manufacturing; coil winding conference, 2001 proceedings. IEEE, 2001Google Scholar
  15. 15.
    Rehman A, Masood N, Arif S, Shahbaz U, Sarwar F, Maqsood K, Imran M, Pasha M (2012) Autonomous fire extinguishing system. In: 2012 international conference on robotics and artificial intelligence (ICRAI)Google Scholar
  16. 16.
    Chen T, Yuan H, Su G, Fan W (2004) An automatic fire searching and suppression system for large spaces. Fire Saf J 39(4):297–307. doi: 10.1016/j.firesaf.2003.11.007 CrossRefGoogle Scholar
  17. 17.
    Yuan F (2010) An integrated fire detection and suppression system based on widely available video surveillance. Mach Vis Appl 21(6):941–948. doi: 10.1007/s00138-010-0276-x CrossRefGoogle Scholar
  18. 18.
    De Santis A, Siciliano B, Villani L (2007) A unified fuzzy logic approach to trajectory planning and inverse kinematics for a fire fighting robot operating in tunnels. Intell Serv Robot 1(1):41–49. doi: 10.1007/s11370-007-0003-2 CrossRefGoogle Scholar
  19. 19.
    Qureshi WS, Ekpanyapong M, Dailey MN, Rinsurongkawong S, Malenichev A, Krasotkina O (2015) QuickBlaze: early fire detection using a combined video processing approach. Fire Technol. doi: 10.1007/s10694-015-0489-7 Google Scholar
  20. 20.
    Töreyin BU, Dedeoğlu Y, Güdükbay U, Çetin AE (2006) Computer vision based method for real-time fire and flame detection. Pattern Recogn Lett 27(1):49–58. doi: 10.1016/j.patrec.2005.06.015 CrossRefGoogle Scholar
  21. 21.
    Phillips Iii W, Shah M, da Vitoria Lobo N (2002) Flame recognition in video. Pattern Recogn Lett 23(1):319–327CrossRefMATHGoogle Scholar
  22. 22.
    Çelik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Saf J 44(2):147–158. doi: 10.1016/j.firesaf.2008.05.005 CrossRefGoogle Scholar
  23. 23.
    Ko B, Cheong K-H, Nam J-Y (2010) Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks. Fire Saf J 45(4):262–270. doi: 10.1016/j.firesaf.2010.04.001 CrossRefGoogle Scholar
  24. 24.
    Xin Y et al (2014) An experimental study of automatic water cannon systems for fire protection of large open spaces. Fire Technol 50(2): 233–248.CrossRefGoogle Scholar
  25. 25.
    Amon F, Benetis V, Kim J, Hamins A (2004) Development of a performance evaluation facility for fire fighting thermal imagers. In: Defense and security, 2004. International Society for Optics and Photonics, pp 244–252Google Scholar
  26. 26.
    Amon F, Bryner N, Hamins A (2004) Evaluation of thermal imaging cameras used in fire fighting applications. In: Defense and security, 2004. International Society for Optics and Photonics, pp 44–53Google Scholar
  27. 27.
    Amon F, Ducharme A (2008) Image frequency analysis for testing of fire service thermal imaging cameras. Fire Technol 45(3):313–322. doi: 10.1007/s10694-008-0060-x CrossRefGoogle Scholar
  28. 28.
    Maxwell FD (1971) A portable IR system for observing fire thru smoke. Fire Technol 7(4):321–331CrossRefGoogle Scholar
  29. 29.
    Lasaponara R, Cuomo V, Macchiato M, Simoniello T (2003) A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection. Int J Remote Sens 24(8):1723–1749CrossRefGoogle Scholar
  30. 30.
    Thomas PJ, Nixon O (1993) Near-infrared forest fire detection concept. Appl Optics 32(27):5348–5355CrossRefGoogle Scholar
  31. 31.
    Wieser D, Brupbacher T (2001) Smoke detection in tunnels using video images. NIST Special Publication SP, Gaithersburg, pp 79–90Google Scholar
  32. 32.
    Sentenac T (2002) Evaluation of a charge-coupled-device-based video sensor for aircraft cargo surveillance. Opt Eng 41(4):796. doi: 10.1117/1.1459450 CrossRefGoogle Scholar
  33. 33.
    Bertozzi M, Broggi A, Caraffi C, Del Rose M, Felisa M, Vezzoni G (2007) Pedestrian detection by means of far-infrared stereo vision. Comput Vis Image Underst 106(2–3):194–204. doi: 10.1016/j.cviu.2006.07.016 CrossRefGoogle Scholar
  34. 34.
    Hajebi K, Zelek JS (2006) Sparse disparity map from uncalibrated infrared stereo images. In: The 3rd Canadian conference on computer and robot vision, 2006. IEEE, pp 17–17Google Scholar
  35. 35.
    Grant G, Brenton J, Drysdale D (2000) Fire suppression by water sprays. Prog Energy Combust Sci 26(2):79–130CrossRefGoogle Scholar
  36. 36.
    DiNenno P (1995) SFPE handbook of fire protection engineering, 3rd edn. National Fire Protection Association, QuincyGoogle Scholar
  37. 37.
    Walker D, Zhang X, Kung P, Saxler A, Javadpour S, Xu J, Razeghi M (1996) AlGaN ultraviolet photoconductors grown on sapphire. Appl Phys Lett 68(15):2100. doi: 10.1063/1.115597 CrossRefGoogle Scholar
  38. 38.
    Zhang S, Wang W, Shtau I, Yun F, He L, Morkoc H, Zhou X, Tamargo M, Alfano R (2002) Backilluminated GaN/AlGaN heterojunction ultraviolet photodetector with high internal gain. Appl Phys Lett 81:4862CrossRefGoogle Scholar
  39. 39.
    Zhang JP, Hu X, Bilenko Y, Deng J, Lunev A, Shur MS, Gaska R, Shatalov M, Yang JW, Khan MA (2004) AlGaN-based 280 nm light-emitting diodes with continuous-wave power exceeding 1 mW at 25 mA. Appl Phys Lett 85(23):5532. doi: 10.1063/1.1831557 CrossRefGoogle Scholar
  40. 40. Hamamatsu Flame Sensor UVTRON.
  41. 41.
    Kim J-H, Lattimer BY (2015) Real-time probabilistic classification of fire and smoke using thermal imagery for intelligent firefighting robot. Fire Saf J 72:40–49. doi: 10.1016/j.firesaf.2015.02.007 CrossRefGoogle Scholar
  42. 42.
    Miyashita T, Sugawa O, Imamura T, Kamiya K, Kawaguchi Y (2014) Modeling and analysis of water discharge trajectory with large capacity monitor. Fire Safety J 63:1–8. doi: 10.1016/j.firesaf.2013.09.028 CrossRefGoogle Scholar
  43. 43.
    Nomura K, Koshizuka S, Oka Y, Obata H (2001) Numerical analysis of droplet breakup behavior using particle method. J Nucl Sci Technol 38(12):1057–1064. doi: 10.1080/18811248.2001.9715136 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentVirginia TechBlacksburgUSA

Personalised recommendations