A Study on the Effectiveness and Economic Evaluation of Disaster Safety Robot Deployment for Integrated Security

doi:10.15683/kosdi.2026.3.31.029

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2026 Vol.22, Issue 1 Preview Page Next Page

Original Article

A Study on the Effectiveness and Economic Evaluation of Disaster Safety Robot Deployment for Integrated Security 통합 보안을 위한 재난안전로봇 배치의 효과성 및 경제성 평가 연구: 박남권*
Nam-Kwun Park*; Expert Committeeman, Urban Safety & Construction Committee, Seoul Metropolitan Council, Seoul, Republic of Korea

31 March 2026. pp. 29-37

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Abstract

Purpose and Method: It intends to establish an integrated security system for various disasters. As part of that, it first targeted traditional markets. An analysis of the effectiveness and economic feasibility of deploying disaster safety robots was conducted to cope with disasters such as fire. Results: The effectiveness of robot operation has been quantitatively verified. The B/C ratio, an economic indicator, is 1.42, and the introduction of robots is judged to be financially valid. Conclusion: In order to improve disaster safety in traditional markets, it is necessary to system the regular deployment of disaster safety robots. In addition, it is necessary to establish an "intelligent disaster safety platform" integrated with the existing fire fighting facilities and management system.

Keywords

Integrated Security

Traditional Markets

Disaster Safety Robots

Economic Evaluation

연구목적과 방법: 각종 재난에 대한 통합 보안시스템을 구축하기 위한 일환으로 우선 전통시장을 대상으로 화재 등의 재난에 대응하기 위한 재난안전로봇 배치의 효과성 및 경제성 분석을 실시하였다. 연구결과: 로봇 운영의 효과성은 정량적으로 검증이 되었으며, 경제성 지표인 B/C 비율는 1.42로 나타나 로봇 도입은 재정적으로 타당하다고 판단된다. 결론: 재난 및 안전에 대한 효과적인 예방, 대비 등을 위해 재난안전로봇의 상시 배치를 제도화하고, 기존 보안 및 소방시설·관리체계와 통합한 ‘지능형 재난안전 플랫폼’을 구축하는 것이 필요하다.

키워드

통합보안

전통시장

재난안전로봇

경제성 평가

References

Eom B-Y. (2024). “Fire hazards and safety measures in traditional markets.” Korea Fire Protection Association (KFPA) WEBZINE Vol. 107, https://www.kfpa.or.kr/webzine/202404/disaster5.html.
Halder, S., Afsari, K. (2023). “Robots in inspection and monitoring of buildings and infrastructure: A systematic review.” Journal of Applied Sciences Vol. 13, No. 4: 2304, pp. 1-21. 10.3390/app13042304
Kim, T-K. (2020). “Analysis of fire intensity according to the zones classification in traditional market stores.” Journal of the Korea Academia-Industrial Cooperation Society, Vol. 21, No. 6, pp. 154-160. 10.5762/KAIS.2020.21.6.154
Korea Industrial Development Institute (2007). Research for the Improvement of Electrical Facilities in Traditional Markets, GOVP1200947608..
Kumar, V., Rus, D., Singh, S. (2004). “Robot and sensor networks for first responders.” Journal of IEEE Pervasive Computing, Vol. 3, No. 4, pp. 24-33. 10.1109/MPRV.2004.17
Lee Y-S. (2017). “Fire characteristics of traditional markets and measures to strengthen safety management.” Korea Fire Safety Institute, Webzine Fire Safety Plus, Vol.02, https://www.kfsi.or.kr/contents/webzine/201703/sub03-02.html.
Li, S., Wang, Y., Feng, C., Zhang, D., Li, H., Huang, W., Shi, L. (2022). “A thermal imaging flame-detection model for firefighting robot based on YOLOv4-F model.” Journal of Fire, Vol. 5, No. 5: 172, pp. 1-18. 10.3390/fire5050172
Li, S., Yun, J., Feng, C., Gao, Y.,Yang, J., Sun, G., Zhang, D. (2023). “An indoor autonomous inspection and firefighting robot based on SLAM and flame image recognition.” Journal of Fire, Vol. 6, No. 3: 93, pp. 1-21. 10.3390/fire6030093
National Fire Data System (NFDS) (2025). https://www.nfds.go.kr
Roldán-Gómez, J-J., González-Gironda, E., Barrientos, A. (2021). “A survey on robotic technologies for forest firefighting: Applying drone swarms to improve firefighters’ efficiency and safety.” Journal of Applied Sciences, Vol. 11, No. 1: 363, pp. 1-18. 10.3390/app11010363
Seoul Metropolitan Fire Disaster Headquarters (2025). Status of Emergency Vehicle Possession at the Seoul Fire and Disaster Headquarters (as of 2025)
Shin, M-S., Kim, B-C., Hwang, S-M., Ko, M-C. (2016). “Design and implementation of IoT-based intelligent surveillance robot.” Studies in Informatics and Control, Vol. 25, No. 4, pp. 421-432. 10.24846/v25i4y201603
Small Enterprise and Market Service (2015). Comprehensive Results of Fire Safety Inspections in Traditional Markets.
Zhang, J-J., Ye, Z-Y., Li, K-F. (2020). “Multi-sensor information fusion detection system for fire robot through back propagation neural network.” PloS One, Vol. 15, No. 7, pp. 1-13. 10.1371/journal.pone.0236482 32706794 PMC7380588

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 22
No :1
Pages :29-37
DOI :https://doi.org/10.15683/kosdi.2026.3.31.029

[1] Eom B-Y. (2024). “Fire hazards and safety measures in traditional markets.” Korea Fire Protection Association (KFPA) WEBZINE Vol. 107, https://www.kfpa.or.kr/webzine/202404/disaster5.html.

[2] Halder, S., Afsari, K. (2023). “Robots in inspection and monitoring of buildings and infrastructure: A systematic review.” Journal of Applied Sciences Vol. 13, No. 4: 2304, pp. 1-21. 10.3390/app13042304

[3] Kim, T-K. (2020). “Analysis of fire intensity according to the zones classification in traditional market stores.” Journal of the Korea Academia-Industrial Cooperation Society, Vol. 21, No. 6, pp. 154-160. 10.5762/KAIS.2020.21.6.154

[4] Korea Industrial Development Institute (2007). Research for the Improvement of Electrical Facilities in Traditional Markets, GOVP1200947608..

[5] Kumar, V., Rus, D., Singh, S. (2004). “Robot and sensor networks for first responders.” Journal of IEEE Pervasive Computing, Vol. 3, No. 4, pp. 24-33. 10.1109/MPRV.2004.17

[6] Lee Y-S. (2017). “Fire characteristics of traditional markets and measures to strengthen safety management.” Korea Fire Safety Institute, Webzine Fire Safety Plus, Vol.02, https://www.kfsi.or.kr/contents/webzine/201703/sub03-02.html.

[7] Li, S., Wang, Y., Feng, C., Zhang, D., Li, H., Huang, W., Shi, L. (2022). “A thermal imaging flame-detection model for firefighting robot based on YOLOv4-F model.” Journal of Fire, Vol. 5, No. 5: 172, pp. 1-18. 10.3390/fire5050172

[8] Li, S., Yun, J., Feng, C., Gao, Y.,Yang, J., Sun, G., Zhang, D. (2023). “An indoor autonomous inspection and firefighting robot based on SLAM and flame image recognition.” Journal of Fire, Vol. 6, No. 3: 93, pp. 1-21. 10.3390/fire6030093

[9] National Fire Data System (NFDS) (2025). https://www.nfds.go.kr

[10] Roldán-Gómez, J-J., González-Gironda, E., Barrientos, A. (2021). “A survey on robotic technologies for forest firefighting: Applying drone swarms to improve firefighters’ efficiency and safety.” Journal of Applied Sciences, Vol. 11, No. 1: 363, pp. 1-18. 10.3390/app11010363

[11] Seoul Metropolitan Fire Disaster Headquarters (2025). Status of Emergency Vehicle Possession at the Seoul Fire and Disaster Headquarters (as of 2025)

[12] Shin, M-S., Kim, B-C., Hwang, S-M., Ko, M-C. (2016). “Design and implementation of IoT-based intelligent surveillance robot.” Studies in Informatics and Control, Vol. 25, No. 4, pp. 421-432. 10.24846/v25i4y201603

[13] Small Enterprise and Market Service (2015). Comprehensive Results of Fire Safety Inspections in Traditional Markets.

[14] Zhang, J-J., Ye, Z-Y., Li, K-F. (2020). “Multi-sensor information fusion detection system for fire robot through back propagation neural network.” PloS One, Vol. 15, No. 7, pp. 1-13. 10.1371/journal.pone.0236482 32706794 PMC7380588

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

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