Abstract
The objective was to design and implement a remotely controlled mobile robot prototype for the preliminary detection of methane gas (CH4) leaks, intended for use in confined or industrial risk environments to reduce human exposure. The project was developed under a descriptive–demonstrative technological approach, with a controlled experimental design conducted in the laboratory and framed within applied research. A real-time data detection and transmission system was implemented using the MQ-4 sensor and the ESP8266-01 module, connected to an Arduino UNO R3 board with an ATmega328P microcontroller. The motion control system was implemented and verified through the H-bridge L293D and commands issued from a mobile application. The stable operation of the MQTT communication system was confirmed, and its operational autonomy was evaluated using lithium batteries. The system showed an effective response to motion commands programmed in C++, as well as successful remote detection of simulated CH4 variations. The resulting prototype proved to be functional, low-cost, and replicable. Future improvements are proposed regarding sensor calibration, energy autonomy optimization, and the integration of autonomous navigation algorithms.
References
Adafruit Industries. (s. f.). DC gearbox motor "TT Motor" 200RPM 3–6VDC (Product ID: 3777) [PDF]. Recuperado el 05 de Septiembre de 2025, https://www.digikey.com/htmldatasheets/production/3190302/0/0/1/3777.pdf
AG Electrónica SAPI de CV. (2024). Módulo transceptor inalámbrico Bluetooth HC-05 [PDF]. https://agelectronica.lat/pdfs/textos/H/HC-05-BT-MODULE-COMPATIBL.PDF
AI-Thinker team. (2017). ESP-01 WiFi module: Technical data [PDF]. Shenzhen Anxinke echnology Co., Ltd. https://aithinker-static.oss-cn-shenzhen.aliyuncs.com/docs/_media_old/esp-01_product_specification_en.pdf
Andi.Co. (2024). Bluetooth RC Car APK para Android. Softonic. https://bluetooth-rc-car.softonic.com/android
Arain, M. A., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2020). Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot. The International Journal of Robotics Research, 40(4–5), 782–814. https://doi.org/10.1177/0278364920954907
Arduino. (2025). Arduino® UNO R3 user manual (SKU: A000066) [PDF]. http://docs.arduino.cc/resources/datasheets/A000066-datasheet.pdf
Carrillo-Amado, Y. R., Califa-Urquiza, M. A. & Ramón-Valencia, J. A. (2020). Calibración y estandarización de mediciones de calidad del aire usando sensores MQ. Respuestas, 25(1), 70–77. https://doi.org/10.22463/0122820X.2408
Castro Maldonado, J. J., Gómez Macho, L. K. & Camargo Casallas, E. (2023). La investigación aplicada y el desarrollo experimental en el fortalecimiento de las competencias de la sociedad del siglo XXI. Tecnura, 27(75), 140–174. https://doi.org/10.14483/22487638.19171
Çalık, F. (2025). The Evaluation of Methane Gas Explosion Risk in Confined Spaces – A Case Study in the Ship Building Industry. International Journal of Advances in Engineering and Pure Sciences, 37(2), 144–152. https://doi.org/10.7240/jeps.1600904
Global Methane Hub. (s. f.). Meet the moment on methane. Recuperado el 01 de septiembre de 2025, de https://www.globalmethanehub.org/
Hanwei Electronics. (s. f.). Technical data MQ-4 gas sensor [PDF]. Recuperado el 01 de septiembre de 2025 https://cdn.sparkfun.com/assets/e/f/c/7/d/MQ-4.pdf
Hasan Shuvo, M. & Chowdhury, S. (2024). IoT Based Fire Detector System Using MQ-4 and LM35 Sensor. 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), IEEE, 400–403. https://doi.org/10.1109/ICSCSS60660.2024.10625187
Iwaszenko, S., Kalisz, P., Słota, M. & Rudzki, A. (2021). Detection of Natural Gas Leakages Using a Laser-Based Methane Sensor and UAV. Remote Sensing, 13(3), 510. https://doi.org/10.3390/rs13030510
Jotham, A. F., Jimananda, A. S., Adhiwangsa, F., Jati, A. N. & Shiddieqy, H. A. (2024). Gas Density Sensors Integration on Heterogeneous Mobile Robots. 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, 250–255. https://doi.org/10.1109/ICITISEE63424.2024.10730001
Kamarudin, K., Shakaff, A. Y., Bennetts, V. H., Mamduh, S. M., Zakaria, A., Visvanathan, R., Ali Yeon, A. S. & Kamarudin, L. M. (2018). Integración de SLAM y mapeo de distribución de gas (SLAM-GDM) para la localización en tiempo real de fuentes de gas. Advanced Robotics, 32(17), 903–917. https://doi.org/10.1080/01691864.2018.1516568
Li, Y., Qian, X., Zhang, S., Sheng, J., Hou, L. & Yuan, M. (2023). Assessment of gas explosion risk in underground spaces adjacent to a gas pipeline. Tunnelling and Underground Space Technology, 131, 104785. https://doi.org/10.1016/j.tust.2022.104785
Magableh, M., Marie, Z., Mohamed, R.R., Bin Ibrahim, M.H., Jusoh, J.A. & Kumar, R. (2023). Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System. International Conference on Computer Science and Emerging Technologies (CSET), IEEE. 1-6. https://doi.org/10.1109/CSET58993.2023.10346624
Pavith Ashwin, G. K., Ramasamy, A. H. H., Raj, P. P., Manikandan, M. & Kumar, K. (2024). Mine Guard: Manually Controlled Surveillance Robot with Gas Detection for Mine Safety. 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), IEEE, 830–835. https://doi.org/10.1109/ICUIS64676.2024.10866498
Peng, Y. (2024). Design of Indoor Environment Monitoring System Based on Wireless Sensor Network. 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC), IEEE, 1149–1153. https://doi.org/10.1109/ICFTIC64248.2024.10913063
Qiao, J., Guo, J. & Li, Y. (2024). Simultaneous localization and mapping (SLAM)-based robot localization and navigation algorithm. Applied Water Science, 14(151). https://doi.org/10.1007/s13201-024-02183-6
Rahma, M. A., Suweni Muntini, M. & Sugriwan, I. (2024). Methane (CH4) Detection System Using The TGS2611 Sensor and MQ-4 Sensor. Journal of Physics: Conference Series, 3rd International Symposium on Physics and Applications 2023 (ISPA 2023). 2780. https://iopscience.iop.org/article/10.1088/1742-6596/2780/1/012034
Raj, R. & Kos, A. A. (2022). A comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives. Applied Sciences, 12(14), 6951. https://doi.org/10.3390/app12146951
República de China. (2011). GB/T 26125-2011 [PDF]. https://www.chinesestandard.net/PDF-EN/GBT26125-2011EN-P09P-H7528H-132738.pdf
Shan, Y., Tian, T., Li, R., Guan, Y., Ou, J., Guan, D. & Hubacek, K. (2025). Global methane footprints growth and drivers 1990-2023. Springer Nature, 16(8184). https://doi.org/10.1038/s41467-025-63383-5
Shindell, D., Sadavarte, P., Aben, I., Bredariol, T. d. O., Dreyfus, G., Höglund-Isaksson, L., Poulter, B., Saunois, M., Schmidt, G. A., Szopa, S., Rentz, K., Parsons, L., Qu, Z., Faluvegi, G. & Maasakkers, J. D. (2024). The methane imperative. Frontiers in Science, 2, 1349770. https://doi.org/10.3389/fsci.2024.1349770
Texas Instruments. (2016). L293, L293D quadruple half-H drivers (SLRS008D) [PDF]. https://www.ti.com/lit/ds/symlink/l293d.pdf?ts=1763062891487&ref_url=https%253A%252F%252Fwww.ti.com%252Fproduct%252FL293D
Tutak, M., Krenicky, T., Pirník, R., Brodny, J. & Grebski, W. W. (2024). Predicting Methane Concentrations in Underground Coal Mining Using a Multi-Layer Perceptron Neural Network Based on Mine Gas Monitoring Data. Sustainability, 16(19), 8388. https://doi.org/10.3390/su16198388
Wang, X., Hou, T., Gao, W., Yu, K., Zhang, T. & Tan, Y. (2024). Experimental study on the diffusion process of natural gas from buried pipelines to underground confined spaces. Natural Gas Industry B, 11(5), 603–615. https://doi.org/10.1016/j.ngib.2024.09.002
Yue, C., Chen, L., Li, Z., Feng, B. & Xu, R. (2025). Research on the hazards of gas leakage and explosion in a full-scale residential building. Defence Technology, 43, 168–181. https://doi.org/10.1016/j.dt.2024.06.014

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
