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Early Fire Detection Mobile Robotic System With Hybrid Locomotion

Yıl 2025, Cilt: 8 Sayı: 4, 1111 - 1120, 15.07.2025
https://doi.org/10.34248/bsengineering.1680411

Öz

In this study, a mobile robotic structure with fire detection and hybrid locomotion capabilities designed and developed. The hybrid locomotion system is an adaptive three-wheeled structure, and it has been structured to provide obstacle climbing and linear motion. The paper puts forward a structure for obstacle avoidance and path planning named "Direction Based Angle Computation". The system is designed to categorize obstacles as either negligible or surmountable, with this classification determined by the height and shape of the obstacles. The objective of the "Fire Search and Find" and "Fire Detection" systems is to identify potential fire locations and calculate the associated probabilities. Experimental tests are conducted for the mechanical structure and architecture of robotic systems. The experimental test results demonstrated that the motion systems have proficiency in both rolling-climbing and linear motions. The Direction Based Angle Computation approach is a proper methodology for the tasks path planning and obstacle avoidance. The proposed fire detection algorithm with the usage of Faster R-CNN machine learning model, has been shown to determine the probability of a fire source with 93% accuracy.

Kaynakça

  • Alqourabah H, Muneer A, Fati SM. 2021. A smart fire detection system using IoT technology with automatic water sprinklers. Int J Electr Comput Eng, 11(4).
  • Bertram C, Evans MH, Javaid M, Stafford T, Prescott T. 2013. Sensory augmentation with distal touch: The tactile helmet project. In: Biomimetic and Biohybrid Systems, Proc Second Int Conf Living Machines, London, UK, pp: 24-35.
  • Bruzzone L, Nodeh SE, Fanghella P. 2022. Tracked locomotion systems for ground mobile robots: A review. Machines, 10(8): 648.
  • Buriboev AS, Rakhmanov K, Soqiyev T, Choi AJ. 2024. Improving fire detection accuracy through enhanced convolutional neural networks and contour techniques. Sensors, 24(16): 5184.
  • Cetin AE, Dimitropoulos K, Gouverneur B, Grammalidis N, Günay O, Habiboğlu YH, Verstockt S. 2013. Video fire detection – review. Digit Signal Process, 23(6): 1827-1843.
  • Dampage U, Bandaranayake L, Wanasinghe R, Kottahachchi K, Jayasanka B. 2022. Forest fire detection system using wireless sensor networks and machine learning. Sci Rep, 12(1): 46.
  • Fonollosa J, Solórzano A, Marco S. 2018. Chemical sensor systems and associated algorithms for fire detection: A review. Sensors, 18(2): 553.
  • Haukur I, Heimo T, Anders L. 2010. Industrial fires: An overview. Brandforsk Project, SP Report 2010:17, SP Tech Res Inst Sweden, Borås, Sweden, pp: 15-26.
  • He Y, Zhang H, Arens E, Merritt A, Huizenga C, Levinson R, Alvarez-Suarez A. 2023. Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning. Build Environ, 228: 109811.
  • Ibitoye OT, Ojo AO, Bisirodipe IO, Ogunlade MA, Ogbodo NI, Adetunji OJ. 2024. A deep learning-based autonomous fire detection and suppression robot. In: Proc 2024 IEEE 5th Int Conf Electro-Computing Technologies for Humanity (NIGERCON), Abuja, Nigeria, pp: 1-4.
  • Khan A, Hassan B, Khan S, Ahmed R, Abuassba A. 2022. DeepFire: A novel dataset and deep transfer learning benchmark for forest fire detection. Mob Inf Syst, 5358359, pp: 52-56.
  • Lee CH, Lee WH, Kim SM. 2023. Development of IoT-based real-time fire detection system using Raspberry Pi and fisheye camera. Appl Sci, 13(15): 8568.
  • Li X, Chen G, Amyotte P, Alauddin M, Khan F. 2023. Modeling and analysis of domino effect in petrochemical storage tank farms under the synergistic effect of explosion and fire. Process Saf Environ Prot, 176: 706-715.
  • Luo Y, Zhao L, Liu P, Huang D. 2018. Fire smoke detection algorithm based on motion characteristic and convolutional neural networks. Multimed Tools Appl, 77: 15075-15092.
  • Manchester IR, Mettin U, Iida F, Tedrake R. 2011. Stable dynamic walking over uneven terrain. Int J Robot Res, 30(3): 265-279.
  • Mishra KB, Wehrstedt KD, Krebs H. 2013. Lessons learned from recent fuel storage fires. Fuel Process Technol, 107: 166-172.
  • Nguyen AP, Nguyen NX. 2024. Control autonomous mobile robot for firefighting task. In: Proc 2024 Int Conf Control, Robotics and Informatics (ICCRI), Hanoi, Vietnam, pp: 37-41.
  • Pandian DS. 2025. Optimized deep learning approach for automated fault diagnosis in mobile robot used for fire-fighting application. Evol Syst, 16(2): 36.
  • Park S, Han KW, Lee K. 2020. A study on fire detection technology through spectrum analysis of smoke particles. In: Proc 2020 Int Conf Inf Commun Technol Converg (ICTC), Jeju, South Korea, pp: 1563-1565.
  • Raibert MH. 1986. Legged Robots That Balance. MIT Press, Cambridge, MA, USA, pp:45-46.
  • Rehman A, Qureshi MA, Ali T, Irfan M, Abdullah S, Yasin S, Węgrzyn M. 2021. Smart fire detection and deterrent system for human savior by using internet of things (IoT). Energies, 14(17): 5500.
  • Sowah RA, Ofoli AR, Krakani SN, Fiawoo SY. 2016. Hardware design and web-based communication modules of a real-time multisensor fire detection and notification system using fuzzy logic. IEEE Trans Ind Appl, 53(1): 559-566.
  • Sucuoglu HS, Bogrekci I, Demircioglu P. 2019. Real time fire detection using Faster R-CNN model. Int J 3D Print Technol Digit Ind, 3(3): 220-226.
  • Sucuoglu HS. 2020. Development of a robotic system with hybrid locomotion for both indoor and outdoor fire detection operations. PhD thesis, Aydin Adnan Menderes University, Institute of Science, Aydin, Türkiye, pp: 176.
  • Sulthana SF, Wise CTA, Ravikumar CV, Anbazhagan R, Idayachandran G, Pau G. 2023. Review study on recent developments in fire sensing methods. IEEE Access, 11: 90269-90282.
  • Zhang R, Cheng Y, Li Y, Zhou D, Cheng S. 2019. Image-based flame detection and combustion analysis for blast furnace raceway. IEEE Trans Instrum Meas, 68(4): 1120-1131.
  • Zhou Y, Pang Y, Chen F, Zhang Y. 2020. Three-dimensional indoor fire evacuation routing. ISPRS Int J Geo-Inf, 9(10): 558.

Early Fire Detection Mobile Robotic System With Hybrid Locomotion

Yıl 2025, Cilt: 8 Sayı: 4, 1111 - 1120, 15.07.2025
https://doi.org/10.34248/bsengineering.1680411

Öz

In this study, a mobile robotic structure with fire detection and hybrid locomotion capabilities designed and developed. The hybrid locomotion system is an adaptive three-wheeled structure, and it has been structured to provide obstacle climbing and linear motion. The paper puts forward a structure for obstacle avoidance and path planning named "Direction Based Angle Computation". The system is designed to categorize obstacles as either negligible or surmountable, with this classification determined by the height and shape of the obstacles. The objective of the "Fire Search and Find" and "Fire Detection" systems is to identify potential fire locations and calculate the associated probabilities. Experimental tests are conducted for the mechanical structure and architecture of robotic systems. The experimental test results demonstrated that the motion systems have proficiency in both rolling-climbing and linear motions. The Direction Based Angle Computation approach is a proper methodology for the tasks path planning and obstacle avoidance. The proposed fire detection algorithm with the usage of Faster R-CNN machine learning model, has been shown to determine the probability of a fire source with 93% accuracy.

Kaynakça

  • Alqourabah H, Muneer A, Fati SM. 2021. A smart fire detection system using IoT technology with automatic water sprinklers. Int J Electr Comput Eng, 11(4).
  • Bertram C, Evans MH, Javaid M, Stafford T, Prescott T. 2013. Sensory augmentation with distal touch: The tactile helmet project. In: Biomimetic and Biohybrid Systems, Proc Second Int Conf Living Machines, London, UK, pp: 24-35.
  • Bruzzone L, Nodeh SE, Fanghella P. 2022. Tracked locomotion systems for ground mobile robots: A review. Machines, 10(8): 648.
  • Buriboev AS, Rakhmanov K, Soqiyev T, Choi AJ. 2024. Improving fire detection accuracy through enhanced convolutional neural networks and contour techniques. Sensors, 24(16): 5184.
  • Cetin AE, Dimitropoulos K, Gouverneur B, Grammalidis N, Günay O, Habiboğlu YH, Verstockt S. 2013. Video fire detection – review. Digit Signal Process, 23(6): 1827-1843.
  • Dampage U, Bandaranayake L, Wanasinghe R, Kottahachchi K, Jayasanka B. 2022. Forest fire detection system using wireless sensor networks and machine learning. Sci Rep, 12(1): 46.
  • Fonollosa J, Solórzano A, Marco S. 2018. Chemical sensor systems and associated algorithms for fire detection: A review. Sensors, 18(2): 553.
  • Haukur I, Heimo T, Anders L. 2010. Industrial fires: An overview. Brandforsk Project, SP Report 2010:17, SP Tech Res Inst Sweden, Borås, Sweden, pp: 15-26.
  • He Y, Zhang H, Arens E, Merritt A, Huizenga C, Levinson R, Alvarez-Suarez A. 2023. Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning. Build Environ, 228: 109811.
  • Ibitoye OT, Ojo AO, Bisirodipe IO, Ogunlade MA, Ogbodo NI, Adetunji OJ. 2024. A deep learning-based autonomous fire detection and suppression robot. In: Proc 2024 IEEE 5th Int Conf Electro-Computing Technologies for Humanity (NIGERCON), Abuja, Nigeria, pp: 1-4.
  • Khan A, Hassan B, Khan S, Ahmed R, Abuassba A. 2022. DeepFire: A novel dataset and deep transfer learning benchmark for forest fire detection. Mob Inf Syst, 5358359, pp: 52-56.
  • Lee CH, Lee WH, Kim SM. 2023. Development of IoT-based real-time fire detection system using Raspberry Pi and fisheye camera. Appl Sci, 13(15): 8568.
  • Li X, Chen G, Amyotte P, Alauddin M, Khan F. 2023. Modeling and analysis of domino effect in petrochemical storage tank farms under the synergistic effect of explosion and fire. Process Saf Environ Prot, 176: 706-715.
  • Luo Y, Zhao L, Liu P, Huang D. 2018. Fire smoke detection algorithm based on motion characteristic and convolutional neural networks. Multimed Tools Appl, 77: 15075-15092.
  • Manchester IR, Mettin U, Iida F, Tedrake R. 2011. Stable dynamic walking over uneven terrain. Int J Robot Res, 30(3): 265-279.
  • Mishra KB, Wehrstedt KD, Krebs H. 2013. Lessons learned from recent fuel storage fires. Fuel Process Technol, 107: 166-172.
  • Nguyen AP, Nguyen NX. 2024. Control autonomous mobile robot for firefighting task. In: Proc 2024 Int Conf Control, Robotics and Informatics (ICCRI), Hanoi, Vietnam, pp: 37-41.
  • Pandian DS. 2025. Optimized deep learning approach for automated fault diagnosis in mobile robot used for fire-fighting application. Evol Syst, 16(2): 36.
  • Park S, Han KW, Lee K. 2020. A study on fire detection technology through spectrum analysis of smoke particles. In: Proc 2020 Int Conf Inf Commun Technol Converg (ICTC), Jeju, South Korea, pp: 1563-1565.
  • Raibert MH. 1986. Legged Robots That Balance. MIT Press, Cambridge, MA, USA, pp:45-46.
  • Rehman A, Qureshi MA, Ali T, Irfan M, Abdullah S, Yasin S, Węgrzyn M. 2021. Smart fire detection and deterrent system for human savior by using internet of things (IoT). Energies, 14(17): 5500.
  • Sowah RA, Ofoli AR, Krakani SN, Fiawoo SY. 2016. Hardware design and web-based communication modules of a real-time multisensor fire detection and notification system using fuzzy logic. IEEE Trans Ind Appl, 53(1): 559-566.
  • Sucuoglu HS, Bogrekci I, Demircioglu P. 2019. Real time fire detection using Faster R-CNN model. Int J 3D Print Technol Digit Ind, 3(3): 220-226.
  • Sucuoglu HS. 2020. Development of a robotic system with hybrid locomotion for both indoor and outdoor fire detection operations. PhD thesis, Aydin Adnan Menderes University, Institute of Science, Aydin, Türkiye, pp: 176.
  • Sulthana SF, Wise CTA, Ravikumar CV, Anbazhagan R, Idayachandran G, Pau G. 2023. Review study on recent developments in fire sensing methods. IEEE Access, 11: 90269-90282.
  • Zhang R, Cheng Y, Li Y, Zhou D, Cheng S. 2019. Image-based flame detection and combustion analysis for blast furnace raceway. IEEE Trans Instrum Meas, 68(4): 1120-1131.
  • Zhou Y, Pang Y, Chen F, Zhang Y. 2020. Three-dimensional indoor fire evacuation routing. ISPRS Int J Geo-Inf, 9(10): 558.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yangın Güvenliği Mühendisliği, Makine Tasarımı ve Makine Elemanları, Malzeme Tasarım ve Davranışları, Makine Mühendisliği (Diğer)
Bölüm Research Articles
Yazarlar

Hilmi Saygın Sucuoğlu 0000-0002-2136-6015

İsmail Böğrekci 0000-0002-9494-5405

Erken Görünüm Tarihi 9 Temmuz 2025
Yayımlanma Tarihi 15 Temmuz 2025
Gönderilme Tarihi 20 Nisan 2025
Kabul Tarihi 25 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 4

Kaynak Göster

APA Sucuoğlu, H. S., & Böğrekci, İ. (2025). Early Fire Detection Mobile Robotic System With Hybrid Locomotion. Black Sea Journal of Engineering and Science, 8(4), 1111-1120. https://doi.org/10.34248/bsengineering.1680411
AMA Sucuoğlu HS, Böğrekci İ. Early Fire Detection Mobile Robotic System With Hybrid Locomotion. BSJ Eng. Sci. Temmuz 2025;8(4):1111-1120. doi:10.34248/bsengineering.1680411
Chicago Sucuoğlu, Hilmi Saygın, ve İsmail Böğrekci. “Early Fire Detection Mobile Robotic System With Hybrid Locomotion”. Black Sea Journal of Engineering and Science 8, sy. 4 (Temmuz 2025): 1111-20. https://doi.org/10.34248/bsengineering.1680411.
EndNote Sucuoğlu HS, Böğrekci İ (01 Temmuz 2025) Early Fire Detection Mobile Robotic System With Hybrid Locomotion. Black Sea Journal of Engineering and Science 8 4 1111–1120.
IEEE H. S. Sucuoğlu ve İ. Böğrekci, “Early Fire Detection Mobile Robotic System With Hybrid Locomotion”, BSJ Eng. Sci., c. 8, sy. 4, ss. 1111–1120, 2025, doi: 10.34248/bsengineering.1680411.
ISNAD Sucuoğlu, Hilmi Saygın - Böğrekci, İsmail. “Early Fire Detection Mobile Robotic System With Hybrid Locomotion”. Black Sea Journal of Engineering and Science 8/4 (Temmuz 2025), 1111-1120. https://doi.org/10.34248/bsengineering.1680411.
JAMA Sucuoğlu HS, Böğrekci İ. Early Fire Detection Mobile Robotic System With Hybrid Locomotion. BSJ Eng. Sci. 2025;8:1111–1120.
MLA Sucuoğlu, Hilmi Saygın ve İsmail Böğrekci. “Early Fire Detection Mobile Robotic System With Hybrid Locomotion”. Black Sea Journal of Engineering and Science, c. 8, sy. 4, 2025, ss. 1111-20, doi:10.34248/bsengineering.1680411.
Vancouver Sucuoğlu HS, Böğrekci İ. Early Fire Detection Mobile Robotic System With Hybrid Locomotion. BSJ Eng. Sci. 2025;8(4):1111-20.

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