Assessing the fidelity of ultrasonic distance sensors in a fire-and-smoke environment

Authors

  • Shaheer Muhammad Department of Computing, Hamdard University, Islamabad Campus, Pakistan. https://orcid.org/0000-0003-1928-5926
  • Hannan Adeel Department of Computing, Hamdard University, Islamabad Campus, Pakistan. https://orcid.org/0000-0002-5348-1237
  • Tahir Saleem Khattak Department of Computing, Hamdard University, Islamabad Campus, Pakistan. https://orcid.org/0000-0001-7828-5382
  • Inamur Rehman Rao Department of Information Technology, Hamdard University, Islamabad Campus, Pakistan. https://orcid.org/0000-0002-9180-9758
  • Shamaila Hayat Department of Computer Science, University of Poonch, Rawlakot, Azad Jammu and Kashmir, Pakistan.

DOI:

https://doi.org/10.47264/idea.ajset/3.1.9

Keywords:

Sonar detection, Sensor, Obstacle, Firefighters, Filter, Robot, Propanol, Kerosene, Range finding sensors, Distance sensors, Firefighting

Abstract

Lack of visibility in fire-and-smoke environments is a major factor that causes operational difficulties, injuries, and loss of life in firefighting. To counter this problem, distance or range-finding sensors are used to detect obstacles or map out the area in fire and smoke environment. These sensors can be mounted on robots assisting firefighting or even firefighters themselves. This paper aims to assess the operational capability of ultrasonic distance sensors in fire-and-smoke environments. Moreover, we investigate how to extract useful information in limiting conditions. Specifically, we design experiments to test Sonar’s range-finding abilities, which are interfered with by burning different types of fuels. The experiments are performed for smoke without flame (smoke pallets), flame without smoke (propanol), and flame with smoke (kerosene) at different distances from Sonar. The results show that Sonar is very effective in smoke because smoke without flame does not increase the air temperature significantly. However, if interference consists of flame, air temperature increases; thus, Sonar outputs erratic data. This study analysed this erratic output of Sonar and provided a filtering algorithm that can eliminate the erratic and stray values from Sonar output and provide valuable information that is helpful in navigation, mapping, or obstacle detection.

References

Abdusalomov, A. B., Islam, B. M. S., Nasimov, R., Mukhiddinov, M., & Whangbo, T. K. (2023). An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors, 23(3), 1512. https://doi.org/10.3390/s23031512

Balen, J., Damjanovic, D., Maric, P., Vdovjak, K., Arlovic, M., & Martinovic, G. (2023). Firebot: An autonomous surveillance robot for fire prevention, early detection, and extinguishing. In 2023 15th International Conference on Computer and Automation Engineering (ICCAE) (pp. 400–405). IEEE. https://doi.org/10.1109/ICCAE56788.2023.10111251

Brooks, P. D., & White, K. M. (2022). The role of robotics in firefighter safety and rescue. Journal of Robotics and Automation, 25(1), 45–55.

Campbell, R. (2018). US firefighter injuries on the fireground, 2010–2014. Fire Technology, 54(2), 461–477. https://link.springer.com/article/10.1007/s10694-017-0692-9

Carr-Pries, N. J., Killip, S. C., & MacDermid, J. C. (2022). Scoping review of the occurrence and characteristics of firefighter exercise and training injuries. International Archives of Occupational and Environmental Health, 95(5), 909–925. https://link.springer.com/article/10.1007/s00420-022-01847-7

Fahy, R. F., LeBlanc, P. R., & Molis, J. L. (2017). Firefighter fatalities in the United States: 2016. National Fire Protection Association (NFPA), Fire Analysis and Research Division. https://miningquiz.com/pdf/Firefighting/osFFF.PDF

Gorbett, G. E., Hopkins, R., & Kennedy, P. (2007). The current knowledge & training regarding backdraft, flashover, and other rapid fire progression phenomena. In Annual meeting of the National Fire Protection Association, Boston.

Green, L. D., Thomas, R. M., & Young, W. C. (2017). Safety in urban firefighting: A study of injury prevention strategies. Fire Safety Review, 15(3), 121–130.

Haynes, H. J. (2017). Fire loss in the United States during 2016. National Fire Protection Association (NFPA), Fire Analysis and Research Division.

Imdoukh, A., Shaker, A., Al-Toukhy, A., Kablaoui, D., & El-Abd, M. (2017). Semi-autonomous indoor firefighting UAV. In 2017 18th International Conference on Advanced Robotics (ICAR) (pp. 310–315). IEEE. https://doi.org/10.1109/ICAR.2017.8023625

Jones, R. A., Johnson, A. L., & Moser, M. A. (2019). Overview of fire dynamics and firefighter safety in high-rise buildings. Fire Protection Engineering, 70, 56–65.

Karter, M. J. (2009). Fire loss in the United States 2008. National Fire Protection Association (NFPA). http://67.59.135.40/FireReports/USFireLoss2008.pdf

Karter, M. (2013). An analysis of volunteer firefighter injuries, 2009–2011. National Fire Protection Association (NFPA). http://tkolb.net/FireReports/2013/VolunteerFF_Injuries09-11.pdf

Khan, F., Xu, Z., & Sun, J. (2023). Fire detection using machine learning techniques. Journal of Fire Sciences, 41, 210–220.

Meacham, B. J. (2022). A sociotechnical systems framework for performance-based design for fire safety. Fire Technology, 58(3), 1137–1167. https://link.springer.com/article/10.1007/s10694-022-01219-0

Najm, N. M., Hussain, A. K., Mustafa, S. I., Rashit, B., & Lukashenka, V. (2024, April). Design and implementation of a robot firefighter for indoor applications. In 2024 35th Conference of Open Innovations Association (FRUCT) (pp. 482–491). IEEE. https://doi.org/10.23919/FRUCT61870.2024.10516362

Pawer, S., Turcotte, K., Desapriya, E., Zheng, A., Purewal, A., Wellar, A., ... & Pike, I. (2022). Female firefighter work-related injuries in the United States and Canada: An overview of survey responses. Frontiers in public health, 10, 861762. https://doi.org/10.3389/fpubh.2022.861762

Peterson, D. A., McIntyre, S. H., & Mulligan, M. C. (2020). Firefighter safety technologies: emerging trends and future directions. Fire Safety Journal, 120, 103212.

Song, L., Zhu, J., Liu, S., & Qu, Z. (2022). Recent fire safety design of high-rise buildings. Journal of Urban Development and Management, 1(1), 50–57.

Thompson, S. N., & Daniels, L. G. (2018). A review of the effectiveness of smoke control systems in commercial buildings. Fire Safety Science, 9, 75–92.

Wang, Y., Xing, J.-P., Guo, H., & Wang, L.-J. (2017). Key technologies of tunnel firefighting robots. IETE Technical Review, 34(1), 3–10. https://doi.org/10.1080/02564602.2016.1139475

Wang, Z., Wang, Y., Tian, M., & Shen, J. (2023). Hearfire: indoor fire detection via inaudible acoustic sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(4), 1–25. https://dl.acm.org/doi/abs/10.1145/3569500

Wang, M., Chen, X., & Huang, X. (2024). Robotic firefighting: a review and future perspective. In X. Huang, W. C. Tam, (eds), Intelligent building fire safety and smart firefighting (pp. 475-499. Springer. https://link.springer.com/chapter/10.1007/978-3-031-48161-1_20

Zhang, G., Zhu, G., Yuan, G., & Huang, L. (2014). Methods for prediction of temperature distribution in flashover caused by backdraft fire. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/707423

Published

2024-12-31

Issue

Section

Original Research Articles

How to Cite

Muhammad, S., Adeel, H., Khattak, T. S., Rao, I. R., & Hayat, S. (2024). Assessing the fidelity of ultrasonic distance sensors in a fire-and-smoke environment. Asian Journal of Science, Engineering and Technology (AJSET), 3(1), 135-150. https://doi.org/10.47264/idea.ajset/3.1.9

Similar Articles

You may also start an advanced similarity search for this article.