KaydedilenlerDerginizi ekleyin

Dergiler / Black Sea Journal of Engineering and Science / 2026 / Cilt: 8 Sayı: 5

Design and Development of Topologically Optimized Early Fire Detection Mobile Robot

Hilmi Saygın Sucuoğlu(AYDIN ADNAN MENDERES ÜNİVERSİTESİ)0000-0002-2136-6015
Sayfa
1605–1616
Yayın Tarihi
2025-09-15
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The FEA and power analysis results confirmed that the optimized structure achieved a 25% weight reduction and an 11% decrease in energy consumption, demonstrating improved efficiency in the mobile fire detection robot.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this study, the comprehensive design, development, topology optimization, and power analysis of a mobile early fire detection robot were conducted.
📊
Temel Bulgular & Veri:Electronic hardware-including RGB and thermal cameras, Raspberry Pi, and motor drivers-was selected, and corresponding mounting parts were designed to integrate the components into the structure.
💡
Sonuç & Katkı:The FEA and power analysis results confirmed that the optimized structure achieved a 25% weight reduction and an 11% decrease in energy consumption, demonstrating improved efficiency in the mobile fire detection robot.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:CAD-based fire detection robot · Computer-aided engineering · Energy-efficient mobile robot

Özet

In this study, the comprehensive design, development, topology optimization, and power analysis of a mobile early fire detection robot were conducted. All subcomponents and the full assembly model were created using computer-aided design (CAD) tools. Electronic hardware-including RGB and thermal cameras, Raspberry Pi, and motor drivers-was selected, and corresponding mounting parts were designed to integrate the components into the structure. Finite element analyses (FEA) were performed to evaluate structural strength and stability. Topology optimization was applied to reduce the overall weight and energy consumption of the system. A specialized power analysis tool was developed to compare the energy usage of the non-optimized and optimized designs. The FEA and power analysis results confirmed that the optimized structure achieved a 25% weight reduction and an 11% decrease in energy consumption, demonstrating improved efficiency in the mobile fire detection robot.

Abstract

In this study, the comprehensive design, development, topology optimization, and power analysis of a mobile early fire detection robot were conducted. All subcomponents and the full assembly model were created using computer-aided design (CAD) tools. Electronic hardware-including RGB and thermal cameras, Raspberry Pi, and motor drivers-was selected, and corresponding mounting parts were designed to integrate the components into the structure. Finite element analyses (FEA) were performed to evaluate structural strength and stability. Topology optimization was applied to reduce the overall weight and energy consumption of the system. A specialized power analysis tool was developed to compare the energy usage of the non-optimized and optimized designs. The FEA and power analysis results confirmed that the optimized structure achieved a 25% weight reduction and an 11% decrease in energy consumption, demonstrating improved efficiency in the mobile fire detection robot.

🔖

Bu Makaleye Atıf Yap

Sucuoğlu, H. S. (2025). Design and Development of Topologically Optimized Early Fire Detection Mobile Robot. Black Sea Journal of Engineering and Science, Cilt: 8 Sayı: 5, 1605–1616. https://doi.org/10.34248/bsengineering.1710797

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (30)

  1. Abdusalomov A, Baratov N, Kutlimuratov A, Whangbo TK. 2021. An improvement of the fire detection and classification method using YOLOv3 for surveillance systems. Sensors, 21(19): 6519.
  2. Ahn Y, Choi H, Kim BS. 2023. Development of early fire detection model for buildings using computer vision-based CCTV. J Build Eng, 65: 105647.
  3. Barbieri L, Muzzupappa M. 2022. Performance-driven engineering design approaches based on generative design and topology optimization tools: a comparative study. Appl Sci. 12(4): 2106.
  4. Biswas A, Ghosh SK, Ghosh A. 2023. Early fire detection and alert system using modified inception-v3 under deep learning framework. Procedia Comput Sci, 218: 2243–2252.
  5. Cavazzuti M, Baldini A, Bertocchi E, Costi D, Torricelli E, Moruzzi P. 2020. High performance automotive chassis design: a topology optimization-based approach. Struct Multidiscip Optim, 62: 45–56.
  6. Demir N, Sucuoglu HS, Bogrekci I, Demircioglu P. 2021. Structural & dynamic analyses and simulation of mobile transportation robot. Int J 3D Print Technol Digit Ind, 5(3): 587–595.
  7. Demir N, Sucuoglu HS, Bogrekci I, Demircioglu P. 2021. Topology optimization of mobile transportation robot. Int J 3D Print Technol Digit Ind, 5(2): 210–219.
  8. Di Biase V, Laneve G. 2018. Geostationary sensor based forest fire detection and monitoring: An improved version of the SFIDE algorithm. Remote Sens, 10(5): 741.
  9. Dimitropoulos K, Barmpoutis P, Grammalidis N. 2014. Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans Circuits Syst Video Technol, 25(2): 339–351.
  10. Frizzi S, Kaabi R, Bouchouicha M, Ginoux JM, Moreau E, Fnaiech F. 2016. Convolutional neural network for video fire and smoke detection. In: IECON 2016 – 42nd Annu Conf IEEE Ind Electron Soc, 877–882.
  11. He X, Feng Y, Xu F, Chen FF, Yu Y. 2022. Smart fire alarm systems for rapid early fire warning: Advances and challenges. Chem Eng J, 450: 137927.
  12. Khan F, Xu Z, Sun J, Khan FM, Ahmed A, Zhao Y. 2022. Recent advances in sensors for fire detection. Sensors, 22(9): 3310.
  13. Li P, Zhao W. 2020. Image fire detection algorithms based on convolutional neural networks. Case Stud Therm Eng, 19: 100625.
  14. Madsen D, Azeem HA, Sandahl M, van Hees P, Husted B. 2018. Levoglucosan as a tracer for smouldering fire. Fire Technol, 54(6): 1871–1885.
  15. Meng L, Zhang W, Quan D, Shi G, Tang L, Hou Y, Gao T. 2020. From topology optimization design to additive manufacturing: Today’s success and tomorrow’s roadmap. Arch Comput Methods Eng, 27(3): 805-830.
  16. Rahayu E, Isnomo YHP, Anshori MA. 2023. Automatic Early Warning System Design with Firefighter Synchronization Based on Internet of Things (IoT). J Telecommun Netw, 13(1): 103–108.
  17. Saeed F, Paul A, Karthigaikumar P, Nayyar A. 2020. Convolutional neural network based early fire detection. Multimed Tools Appl, 79(13): 9083–9099.
  18. Sucuoglu HS. 2015. The development of fire detection robot. Master’s thesis, Adnan Menderes Univ, Fen Bilimleri Enstitusu, Aydın, Türkiye, pp:45-78.
  19. Sucuoglu HS. 2020. Development of a robotic system with hybrid locomotion for both indoor and outdoor fire detection operations. PhD thesis, Adnan Menderes Univ, Fen Bilimleri Enstitusu, Aydın, Türkiye, pp:67-85.
  20. Sucuoglu HS, Bogrekci I, Demircioglu P. 2018. Development of mobile robot with sensor fusion fire detection unit. IFAC-PapersOnLine, 51(30): 430–435.
  21. 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.
  22. Sucuoglu HS, Bogrekci I, Demircioglu P, Turhanlar O. 2020. Design & FEA and Multi Body System Analysis of Human Rescue Robot Arm. In: Adv Mechatronics Solut, Springer, 651–656.
  23. Wei X, Wang Y, Dong Y. 2014. Design of fire detection system in buildings based on wireless multimedia sensor networks. In: Proc 11th World Cong Intell Control Autom, 3008–3012.
  24. Yang M, Qian S, Wu X. 2024. Real-time fire and smoke detection with transfer learning based on cloud-edge collaborative architecture. IET Image Process, pp:47-58.
  25. Yurdem H, Degirmencioglu A, Cakir E, Gulsoylu E. 2019. Measurement of strains induced on a three-bottom moldboard plough under load and comparisons with finite element simulations. Measurement, 136: 594–602.
  26. Zamal MFB, Sayed S, Bhuiyan T, Rahman M. 2017. An Efficient Multi-sensing and GSM Equipped Fire Monitoring System. In: MATEC Web Conf, 140: 01003.
  27. Zhang RC, Du JH. 2010. Fuzzy clustering algorithm of early fire based on process characteristic. Key Eng Mater, 437: 339–343.
  28. Zhao Y, Ban Y. 2022. Early Detection of Wildfires with GOES-R Time Series and Deep GRU-Network.
  29. Zhong Z, Wang M, Shi Y, Gao W. 2018. A convolutional neural network-based flame detection method in video sequence. Signal Image Video Process, 12: 1619–1627.
  30. Zhu JH, Zhang WH, Xia L. 2016. Topology optimization in aircraft and aerospace structures design. Arch Comput Methods Eng, 23: 595–622.