Rescue Drone Swarm Design for Innovative Disaster Management and Rescue Operations
DOI:
https://doi.org/10.51903/n2anez60Keywords:
Drone Swarm, Disaster Management, Multi-Agent Systems, Artificial Intelligence , Emergency ResponseAbstract
Conventional emergency response strategies are often constrained by limited operational coverage, slow resource mobilization, and fragmented data acquisition, hindering coordination effectiveness during critical phases. This research proposes the design of an autonomous drone swarm system to support innovative disaster management and rescue operations. A coordinated multi-agent based system is designed to perform real-time monitoring, structured search and rescue, and post-disaster infrastructure evaluation. Rule-based behavioral modeling is used in complex emergency scenarios. Initial simulation results show greater area coverage, accelerated response times, and increased mission adaptability under dynamic conditions. The system is also designed to deliver logistical assistance such as food and medicine to hard to reach areas, such as ravines or steep mountains. The integration of artificial intelligence algorithms improves target identification precision as well as adaptive swarm response. This model is considered to have transformational potential to be the foundation for the development of future rapid-response autonomous rescue systems.
References
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Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. L. (2022). Machine Learning in Disaster Management: Recent Developments in Methods and Applications. Machine Learning and Knowledge Extraction, 4(2), 446–473. https://doi.org/10.3390/make4020020
Maleka, N. H., & Matli, W. (2024). A review of telehealth during the COVID-19 emergency situation in the public health sector: challenges and opportunities. Journal of Science and Technology Policy Management, 15(4), 707–724. https://doi.org/10.1108/JSTPM-08-2021-0126
Mavrouli, M., Mavroulis, S., Lekkas, E., & Tsakris, A. (2021). Respiratory Infections Following Earthquake-Induced Tsunamis: Transmission Risk Factors and Lessons Learned for Disaster Risk Management. International Journal of Environmental Research and Public Health, 18(9). https://doi.org/10.3390/ijerph18094952
Nagata, T., Ikeda, M., Kimura, R., & Oda, T. (2022). Development of Tsunami Disaster Risk Reduction Education Program for Children with No Experience of Earthquake Disaster – Practice and Verification at Shichigahama Town, Miyagi Prefecture. Journal of Disaster Research, 17(6), 1000–1014. https://doi.org/10.20965/jdr.2022.p1000
Nguyen, L. V. (2024). Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review. AppliedMath, 4(4), 1192–1210. https://doi.org/10.3390/appliedmath4040064
Niu, Q., Li, H., Liu, Y., Qin, Z., Zhang, L. B., Chen, J., & Lyu, Z. (2024). Toward the Internet of Medical Things: Architecture, trends and challenges. Mathematical Biosciences and Engineering, 21(1), 650–678. https://doi.org/10.3934/mbe.2024028
Park, B., Kang, C., & Choi, J. (2022). Cooperative multi-robot task allocation with reinforcement learning. Applied Sciences (Switzerland), 12(1). https://doi.org/10.3390/app12010272
Pavitra, R. R. A., Muthukrishnan, A., Maheswari, U. P., Venkatasamy, R., & Lawrence, D. I. (2024). Research Review Inquisitive on Indoor Air Quality Monitoring System Facilitate with Internet of Things. E3S Web of Conferences, 477, 1–11. https://doi.org/10.1051/e3sconf/202447700044
Shahzad, M. M., Saeed, Z., Akhtar, A., Munawar, H., Yousaf, M. H., Baloach, N. K., & Hussain, F. (2023). A Review of Swarm Robotics in a NutShell. Drones, 7(4), 1–28. https://doi.org/10.3390/drones7040269
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Siawsh, N., Peszynski, K., Vo-Tran, H., & Young, L. (2023b). Toward the creation of disaster-resilient communities: The Machizukuri initiative – The 2011 Tōhoku Great East Japan Earthquake and Tsunami. International Journal of Disaster Risk Reduction, 96, 103961. https://doi.org/https://doi.org/10.1016/j.ijdrr.2023.103961
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