Citation :

Md. Shawkut Ali Khan, 2024. "Introducing Firefighting Robot Utilizing GSM Technology" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 2, Issue 1: 27-38.

Abstract :

A fire incident is a catastrophic event that has the potential to result in fatalities, destruction of property, and long-term physical impairment for the individuals involved. Additionally, they may have enduring psychological trauma. Firefighters have the main responsibility of managing fire emergencies, however they frequently face increased dangers while putting out fires, particularly in perilous settings like nuclear power plants, petroleum refineries, and gas tanks. In addition, they encounter additional challenges, especially when dealing with fires in confined and constricted areas. This requires thorough exploration of the debris and obstructions within the buildings in order to extinguish the fire and rescue any individuals in danger. Technological improvements can be employed to aid firefighting operations, which are characterized by significant obstacles and hazards. Hence, this study outlines the creation of a firefighting robot named FFR, which has the capability to suppress fires without requiring firefighters to face avoidable hazards. The FFR is specifically built to have a smaller size compared to other traditional fire-fighting robots. This is done to facilitate easy entry into small locations and allow for a more effective smothering of fires in limited spaces. The FFR is additionally outfitted with an ultrasonic sensor to avert collisions with obstructions or nearby objects, while a flame sensor is affixed for the purpose of fire detection. As a result, FFR showcased its capacity to autonomously detect fire spots and extinguish fires from a specific distance. The FFR is designed to autonomously detect the position of the fire and come to a halt at a maximum distance of 40 cm from the fire.

References :

[1] Jeelani, S., et al., Robotics and medicine: A scientific rainbow in hospital. Journal of Pharmacy &Bioallied Sciences, 2015. 7(Suppl 2): p. S381-S383.

[2] Aliff, M., S. Dohta, and T. Akagi, Simple Trajectory Control Method of Robot Arm Using Flexible Pneumatic Cylinders. Journal of Robotics and Mechatronics, 2015. 27(6): p. 698-705.

[3] Aliff M, D.S., and Akagi T, Control and analysis of simple-structured robot arm using flexible pneumatic cylinders. International Journal of Advanced and Applied Sciences, 2017. 4(12): p. 151-157.

[4] Aliff, M., S. Dohta, and T. Akagi, Control and analysis of robot arm using flexible pneumatic cylinder. Mechanical Engineering Journal, 2014. 1(5): p. DR0051-DR0051.

[5] M. Aliff, S. Dohta and T. Akagi, Trajectory controls and its analysis for robot arm using flexible pneumatic cylinders," IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), 2015, pp. 48-54.

[6] M. Aliff, S. Dohta and T. Akagi, Trajectory control of robot arm using flexible pneumatic cylinders and embedded controller, IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2015, pp. 1120-1125.

[7] C. Xin, D. Qiao, S. Hongjie, L. Chunhe and Z. Haikuan, Design and Implementation of Debris Search and Rescue Robot System Based on Internet of Things, International Conference on Smart Grid and Electrical Automation (ICSGEA), 2018, pp. 303-307.

[8] Yusof, M., and Dodd, T., Pangolin: A Variable Geometry Tracked Vehicle With Independent Track Control, Field Robotics, pp. 917-924.

[9] Day, C.-P., Robotics in Industry—Their Role in Intelligent Manufacturing. Engineering, 2018. 4(4): p. 440-445.

[10] J. Lee, G. Park, J. Shin and J. Woo, Industrial robot calibration method using denavit — Hatenberg parameters, 17th International Conference on Control, Automation and Systems (ICCAS), 2017, pp. 1834-1837.

[11] Sani, N. S., Shamsuddin, I. I. S., Sahran, S., Rahman, A. H. A and Muzaffar, E. N, Redefining selection of features and classification algorithms for room occupancy detection, International Journal on Advanced Science, Engineering and Information Technology, 2018, 8(4- 2), pp. 1486-1493.

[12] Holliday, J. D., Sani, N., and Willett, P., Calculation of substructural analysis weights using a genetic algorithm, Journal of Chemical Information and Modeling, 2015, 55(2), pp. 214-221.

[13] Holliday, J. D., N. Sani, and P. Willett, Ligand-based virtual screening using a genetic algorithm with data fusion, Match: Communications in Mathematical and in Computer Chemistry, 80, pp. 623-638.

[14] SamsiahSani, N., Shlash, I., Hassan, M., Hadi, A., and Aliff, M, Enhancing malaysia rainfall prediction using classification techniques, J. Appl. Environ. Biol. Sci, 2017, 7(2S), pp. 20-29.

[15] Sani, N.S., Rahman, M.A., Bakar, A.A., Sahran, S. and Sarim, H.M, Machine learning approach for bottom 40 percent households (B40) poverty classification, International Journal on Advanced Science, Engineering and Information Technology, 2018, 8(4-2), pp.1698-1705.

[16] Kim, J.-H., S. Jo, and B.Y. Lattimer, Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images. Journal of Sensors, 2016. 2016: p. 13.

[17] Tanaka, F., et al., Telepresence robot helps children in communicating with teachers who speak a different language, in Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction. 2014, ACM: Bielefeld, Germany. p. 399-406.

[18] J. Ahn and G. J. Kim, Remote collaboration using a tele-presence mobile projector robot tele-operated by a smartphone, IEEE/SICE International Symposium on System Integration (SII), 2016, pp. 236- 241.

[19] Harik, E.H. and A. Korsaeth, Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse. Robotics, 2018. 7(2): p. 22.

[20] Acosta Calderon, C.A., E.R. Mohan, and B.S. Ng, Development of a hospital mobile platform for logistics tasks. Digital Communications and Networks, 2015. 1(2): p. 102-111.

[21] H. Hyung, B. Ahn, B. Cruz and D. Lee, Analysis of android robot lipsync factors affecting communication, 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016, pp. 441-442.

[22] R. N. Haksar and M. Schwager, Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 1067-1074.

[23] J. Raju, S. S. Mohammed, J. V. Paul, G. A. John and D. S. Nair, Development and implementation of arduino microcontroller based dual mode fire extinguishing robot, IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 2017, pp. 1-4.

[24] TusharNandkishorSatbhai, R.M.K., Anant Vijay Patil, Manish Patil, Fire Fighting Robot. International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), 2016. 4(4): p. 799-803.

[25] Nuţă, I., O. Orban, and L. Grigore, Development and Improvement of Technology in Emergency Response. Procedia Economics and Finance, 2015. 32: p. 603-609.

Keywords :

Fire Incident, Fire Fighters, Firefighting Robot, Sensors.