PENGEMBANGAN SISTEM ROBOT PENJELAJAH BERBASIS MQTT MITIGASI BENCANA DENGAN DUKUNGAN IMAGE PROCESSING

Authors

  • Riadi Marta Dinata Institut Sains Dan Teknologi Nasional
  • Muhammad Ikrar Yamin Institut Sains Dan Teknologi Nasional
  • Agus Sofwan Institut Sains Dan Teknologi Nasional
  • Ariman Ariman Institut Sains Dan Teknologi Nasional
  • Niko Purnomo Niko Universitas Nusa Mandiri

DOI:

https://doi.org/10.33480/inti.v20i1.7047

Keywords:

disaster mitigation , explorer robot , Internet of things (IoT), MQTT , image processing

Abstract

Disaster mitigation is a global challenge that requires innovation to enhance the effectiveness of emergency response, particularly in the rapid and safe detection of victims. Although much research focuses on optimizing individual components such as sensors or algorithms, a gap remains in the development of holistically integrated frameworks. This study develops and evaluates an integrated explorer robot system based on Message Queuing Telemetry Transport (MQTT) and artificial intelligence for real-time disaster victim detection. Using a Design Science Research approach, the system architecture integrates an explorer robot based on ESP32-CAM and GPS for data acquisition, a central server running the You Only Look Once (YOLO) algorithm for image analysis, and involves a human operator for critical decision validation. Experimental results show that the system can detect victims with an average accuracy of 87.3% across various simulated scenarios. Communication via the MQTT protocol proved to be highly reliable and efficient, with an average latency of 127 ms and a packet loss rate of only 2.3%, enabling swift coordination between components. This research successfully validates an effective and replicable end-to-end architectural model, thereby presenting a practical blueprint for the development of low-cost Search and Rescue (SAR) robotic systems

Downloads

Download data is not yet available.

References

Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010

Ahmed, A., & Kim, D. (2022). Performance analysis of deep learning models in edge devices for real-time object detection. Sensors, 22(6), 2150. https://doi.org/10.3390/s22062150

Al-Dulaimi, A., Fattah, G. H., & Hussain, S. A. (2024). A comprehensive review of object detection techniques in computer vision. Journal of Imaging, 10(2), 37. https://doi.org/10.3390/jimaging10020037

Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010

Bozcan, A., & Kajan, E. E. (2021). UAV-based post-disaster assessment using optimized object detection. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 1–6). IEEE. https://doi.org/10.1109/UBMK52708.2021.9558863

Chen, X., Li, H., & Zhao, W. (2023). YOLO-based real-time human detection under complex environments. Sensors, 23(4), 2132. https://doi.org/10.3390/s23042132

Guo, J., Sun, C., & Zhang, L. (2020). Design and implementation of wireless video monitoring system based on ESP32-CAM. IOP Conference Series: Materials Science and Engineering, 768(6), 062024. https://doi.org/10.1088/1757-899X/768/6/062024

He, Y., Cui, J., Vizzari, G., & Xiang, X. (2021). A survey on communication and networking for swarms of unmanned aerial vehicles. Journal of Systems Architecture, 118, 102222. https://doi.org/10.1016/j.sysarc.2021.102222

Johannesson, P., & Perjons, E. (2021). An introduction to design science. Springer. https://doi.org/10.1007/978-3-030-78132-3

Kim, J., Park, H., & Lee, J. (2023). Multi-sensor fusion for autonomous robot navigation in disaster environments. Robotics and Autonomous Systems, 161, 104308. https://doi.org/10.1016/j.robot.2023.104308

Kumar, A., & Singh, S. (2022). Cost-effective rescue robots for disaster response: A review. Journal of Field Robotics, 39(5), 789–803. https://doi.org/10.1002/rob.22034

Lee, H., Wang, J., & Chen, S. (2023). Swarm robotics approach for disaster victim search and rescue. IEEE Transactions on Robotics, 39(2), 1259–1274. https://doi.org/10.1109/TRO.2022.3201847

Li, J., Song, W., & Zhang, Y. (2021). Object detection-based post-disaster search using drone surveillance. Remote Sensing, 13(5), 912. https://doi.org/10.3390/rs13050912

Murphy, R. R. (2014). A decade of rescue robots. Communications of the ACM, 57(8), 78–87. https://doi.org/10.1145/2629540

Nugroho, A., Sari, D. P., & Wijaya, R. (2019). Disaster management system in Indonesia: Challenges and opportunities. International Journal of Disaster Risk Reduction, 35, 101089. https://doi.org/10.1016/j.ijdrr.2019.101089

Padilla, R., Passos, W. L., Dias, T. L., Netto, S. L., & da Silva, E. A. B. (2021). A comparative analysis of object detection metrics with a companion open-source toolkit. Electronics, 10(3), 279. https://doi.org/10.3390/electronics10030279

Qin, Y., Wang, Z., & Zhang, J. (2020). Human-machine interface design for disaster rescue robots. International Journal of Advanced Robotic Systems, 17(1). https://doi.org/10.1177/1729881420905711

Quattrini, A. M., Chimienti, M. I., Caccetta, F., & Giannoccaro, N. I. (2020). A communication system based on an ad-hoc network for rescue robots. In Intelligent Human Systems Integration (pp. 535–540). Springer. https://doi.org/10.1007/978-3-030-39512-4_87

Rahman, M. M., Islam, M. R., & Ghosh, R. (2022). IoT-based disaster management systems: A review. Internet of Things, 18, 100475. https://doi.org/10.1016/j.iot.2022.100475

Saad, M. M., Al-emran, M., & Teo, J. (2022). A communication platform for a swarm of heterogeneous search and rescue robots. International Journal of Advanced Robotic Systems, 19(5). https://doi.org/10.1177/17298814221124430

Sharma, M., & Tripathi, R. (2021). MQTT: A machine to machine Internet of Things protocol. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1–6). IEEE. https://doi.org/10.1109/CONIT51480.2021.9498533

Smith, J. A., & Johnson, M. R. (2021). IoT-enabled disaster response: A comprehensive survey. ACM Computing Surveys, 54(3), Article 59. https://doi.org/10.1145/3447754

Zhang, Q., Liu, Y., & Wang, H. (2021). YOLO-based victim detection system for disaster response applications. Computer Vision and Image Understanding, 207, 103201. https://doi.org/10.1016/j.cviu.2021.103201

Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2020). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865

Downloads

Published

2025-08-29

How to Cite

Marta Dinata, R. ., Ikrar Yamin, M. ., Sofwan, A. ., Ariman, A., & Niko, N. P. (2025). PENGEMBANGAN SISTEM ROBOT PENJELAJAH BERBASIS MQTT MITIGASI BENCANA DENGAN DUKUNGAN IMAGE PROCESSING. INTI Nusa Mandiri, 20(1), 103–111. https://doi.org/10.33480/inti.v20i1.7047