The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지
DOI QR코드

DOI QR Code

A Sensor Module Overcoming Thick Smoke through Investigation of Fire Characteristics

화재 특성 고찰을 통한 농연 극복 센서 모듈

  • Received : 2018.09.04
  • Accepted : 2018.11.21
  • Published : 2018.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we describe a sensor module that monitors fire environment by analyzing fire characteristics. We analyzed the smoke characteristics of indoor fire. Six different environments were defined according to the type of smoke and the flame, and the sensors available for each environment were combined. Based on this analysis, the sensors were selected from the perspective of firefighter. The sensor module consists of an RGB camera, an infrared camera and a radar. It is designed with minimum weight to fit on the robot. the enclosure of sensor is designed to protect against the radiant heat of the fire scene. We propose a single camera mode, thermal stereo mode, data fusion mode, and radar mode that can be used depending on the fire scene. Thermal stereo was effectively refined using an image segmentation algorithm, SLIC (Simple Linear Iterative Clustering). In order to reproduce the fire scene, three fire test environments were built and each sensor was verified.

Keywords

References

  1. National Fire Agency, e-Fire statistics, [Online], http://nfds.go.kr/rdPage.jsf, Accessed: October 31, 2018.
  2. J.-H. Cha, "Study on building room fire development stages," Korea Society of Hazard Mitigation Conference, Seoul, Korea, pp. 169-172, 2008.
  3. E. Hwang , J. Choi, and D. Choi, "A Study on the Effective Methods of Securing the Golden Time of Fire Engine Move Out," Journal of the Korean Society of Hazard Mitigation, vol. 18, no. 5, pp. 119-126, August, 2018. https://doi.org/10.9798/kosham.2018.18.5.119
  4. J.-H. Kim, J. W. Starr, and B. Y. Lattimer, "Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through Fire Smoke," Fire Technology, vol. 51, no. 4, pp. 823-845, July, 2015. https://doi.org/10.1007/s10694-014-0413-6
  5. F. Amon, N. Bryner, A. Lock, and A. Hamins, "Performance Metrics for Fire Fighting Thermal Imaging Cameras - Small- and Full-Scale Experiments," National Institute of Standards and Technology, Gaithersburg, USA, Tech. Note 1499, July, 2008.
  6. J. W. Starr and B. Y. Lattimer, "Evaluation of Navigation Sensors in Fire Smoke Environments," Fire Technology, vol. 50, no. 6, pp. 1459-1481, November, 2014. https://doi.org/10.1007/s10694-013-0356-3
  7. Fire Tech and safety, The Future of Thermal Imaging - Bullard Thermal Imagers VS. ISG E380, FLIR, Drager & EV2 Mi Tic, [Online], https://www.youtube.com/watch?v=hdPDv2S911I, Accessed: October 31, 2018.
  8. L.H. Hu, R. Huo, Y.Z. Li, H.B. Wang, and W.K. Chow, "Full-scale burning tests on studying smoke temperature and velocity along a corridor," Tunnelling and Underground Space Technology, vol. 20, no. 3, pp. 223-229, May, 2005. https://doi.org/10.1016/j.tust.2004.08.007
  9. J.-Y. Bouguet, Camera Calibration Toolbox For Matlab, [Online], http://www.vision.caltech.edu/bouguetj/calib_doc/, Accessed: October 31, 2018.
  10. H. Hirschmuller, "Stereo Processing by Semiglobal Matching and Mutual Information," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 328-341, February, 2008. https://doi.org/10.1109/TPAMI.2007.1166
  11. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, November, 2012. https://doi.org/10.1109/TPAMI.2012.120
  12. C. Y. Ren, V. A. Prisacariu and I. D Reid, gSLICr: SLIC superpixels at over 250 Hz, [Online], https://arxiv.org/abs/1509.04232, Accessed: October 31, 2018.
  13. D. Hernandez-Juarez, A. Chacon, A. Espinosa, D. Vazquez, J. C. Moure, and A. M. Lopez, "Embedded real-time stereo estimation via semi-global matching on the GPU," Procedia Computer Science, vol. 80, pp. 143-153, 2016. https://doi.org/10.1016/j.procs.2016.05.305

Cited by

  1. A Multi-Sensor Module of Snake Robot for Searching Survivors in Narrow Space vol.16, pp.4, 2018, https://doi.org/10.7746/jkros.2021.16.4.291