Enhancing Maritime Search and Rescue (SAR) Operations Using UAVBased Flight Control Systems: Opportunities, and Challenges
Abstract
The number of maritime accidents has been generally in decline in recent years, but maritime incidents still pose an important threat to human life, with optimization of search strategies and efficient use of resources hence being a high priority. In this regard, this study employed a mixed-methods approach by carrying out a Python-based simulation in the modeling of UAV-supported SAR missions and semistructured interviews with professionals in the areas of maritime SAR and experts in drone technology.
This study aims to explore how UAVs may enhance the effectiveness of maritime SAR by employing both quantitative simulations and qualitative insights from expert viewpoints. The research investigates the potential of UAVs in optimizing search patterns, detecting persons in water (PIW), and delivering life-saving equipment under various conditions.
Besides the quantitative analysis through a Python-based simulation modeled UAV-supported SAR missions, semi-structured interviews provided practical insights into operational challenges. Many practitioners recognized the potential of UAVs for faster observations and interventions, but they expressed concerns about their vulnerability to bad weather conditions, regulatory restrictions, and also limited payload capacities.
The interviewees mentioned the greater strength and weather resistance given to drones, improved sensorial advances like thermal imaging, Lidar, and further action in terms of algorithms within autonomous navigation. Again, participants stressed that availability is related to specialized training for this type of SAR team with high-performance UAV systems. These results have proved that UAVs indeed provide a quantifiable advantage under maritime SAR situations, precisely in terms of speed and area covered, real-time situational awareness. The significant hurdles will be overstepped before UAVs can be fully integrated into normal SAR operations. Better long-enduring drones, up-to-date sensors for further detection, refined flight stabilizing controls, and easier regulatory frameworkswhen that matures with the technology, UAVs will find their rightful place as invaluable tools bound to go a long distance in increasing the chances of survival during maritime disasters.
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Ai, Bo, Maoxin Jia, Hanwen Xu, Jiangling Xu, Zhen Wen, Benshuai Li, and Dan Zhang. 2021.
“Coverage Path Planning for Maritime Search and Rescue Using Reinforcement Learning.” Ocean Engineering 241 (December):110098. https://doi.org/10.1016/j.oceaneng.2021.110098.
Aslam, Sheraz, Michalis P. Michaelides, and Herodotos Herodotou. 2020. “Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges.” IEEE Internet of Things Journal 7 (10): 9714–27. https://doi.org/10.1109/JIOT.2020.2993411.
Baig, Mirza Zeeshan, Khanssa Lagdami, and Maximo Q. Mejia Jr. 2024. “Enhancing Maritime Safety: A Comprehensive Review of Challenges and Opportunities in the Domestic Ferry Sector.” Maritime Technology and Research 6 (3): 268911. https://doi.org/10.33175/mtr.2024.268911.
Baig, Mirza Zeeshan, Khanssa Lagdami, and Maximo Q. Mejia. 2024. “Safeguarding Maritime Transport: Disclosing the Paradox of Safety in Domestic Ferry Operations.” Australian Journal of Maritime & Ocean Affairs, May, 1–30. https://doi.org/10.1080/18366503.2024.2354558.
Cucinelli, Jessica, Floris Goerlandt, and Ronald Pelot. 2023. “Exploring Risk Governance Deficits of Maritime Search and Rescue in Canada.” Marine Policy 149 (March):105511. https://doi.org/10.1016/j.marpol.2023.105511.
Dong, Yaxin, Hongxiang Ren, Yuzhu Zhu, Rui Tao, Yating Duan, and Nianjun Shao. 2024. “A Multi-Objective Optimization Method for Maritime Search and Rescue Resource Allocation: An Application to the South China Sea.” Journal of Marine Science and Engineering 12 (1): 184. https://doi.org/10.3390/jmse12010184.
Emimi, Mohamed, Mohamed Khaleel, and Abobakr Alkrash. 2023. “The Current Opportunities and Challenges in Drone Technology.” Int. J. Electr. Eng. and Sustain. 1 (3): 74–89.
https://ijees.org/index.php/ijees/article/view/47.
Guimarães, Nathalie, Luís Pádua, Pedro Marques, Nuno Silva, Emanuel Peres, and Joaquim J. Sousa. 2020. “Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities.” Remote Sensing 12 (6): 1046. https://doi.org/10.3390/rs12061046.
Li, Jiqiang, Guoqing Zhang, Changyan Jiang, and Weidong Zhang. 2023. “A Survey of Maritime Unmanned Search System: Theory, Applications and Future Directions.” Ocean Engineering 285 (October):115359. https://doi.org/10.1016/j.oceaneng.2023.115359.
Liu, Yan, Xiaowen Fu, Kun Wang, Shiyuan Zheng, and Yibin Xiao. 2024. “Bibliometric Analysis and Literature Review on Maritime Transport Resilience and Its Associated Impacts on Trade.” Maritime Policy & Management, June, 1–38. https://doi.org/10.1080/03088839.2024.2367971.
Lyu, Mingyang, Yibo Zhao, Chao Huang, and Hailong Huang. 2023. “Unmanned Aerial Vehicles for Search and Rescue: A Survey.” Remote Sensing 15 (13): 3266. https://doi.org/10.3390/rs15133266.
Sánchez-García, J., J.M. García-Campos, M. Arzamendia, D.G. Reina, S.L. Toral, and D. Gregor. 2018. “A Survey on Unmanned Aerial and Aquatic Vehicle Multi-Hop Networks: Wireless Communications, Evaluation Tools and Applications.” Computer Communications 119 (April):43–65. https://doi.org/10.1016/j.comcom.2018.02.002.
Sirry, Jacob, and Amar Nath Patra. 2024. “Harnessing Modern Technological Advancements in Search and Rescue Missions.” In SoutheastCon 2024, 240–49. IEEE. https://doi.org/10.1109/SoutheastCon52093.2024.10500268.
Somaiyeh, Mahmoud Zadeh, Amir mehdi Yazdani, Yashar Kalantari, Bekir Ciftler, Fathi
Aidarus, and Mhd Omar Al Kadri. 2024. “Holistic Review of UAV-Centric Situational Awareness: Applications, Limitations, and Algorithmic Challenges.” Robotics 13 (8): 117. https://doi.org/10.3390/robotics13080117.
Sun, Yang, Jun Ling, Xinqiang Chen, Fancun Kong, Qinyou Hu, and Salvatore Antonio Biancardo. 2022. “Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method.” Journal of Marine Science and Engineering 10 (7): 906. https://doi.org/10.3390/jmse10070906.
Wu, Jie, Liang Cheng, Sensen Chu, and Yanjie Song. 2024. “An Autonomous Coverage Path
Planning Algorithm for Maritime Search and Rescue of Persons-in-Water Based on Deep Reinforcement Learning.” Ocean Engineering 291 (January):116403. https://doi.org/10.1016/j.oceaneng.2023.116403.
Xiong, Weitao, P.H.A.J.M. van Gelder, and Kewei Yang. 2020. “A Decision Support Method for Design and Operationalization of Search and Rescue in Maritime Emergency.” Ocean Engineering 207 (July):107399. https://doi.org/10.1016/j.oceaneng.2020.107399.
Zhou, Xiao. 2022. “A Comprehensive Framework for Assessing Navigation Risk and Deploying Maritime Emergency Resources in the South China Sea.” Ocean Engineering 248 (March):110797. https://doi.org/10.1016/j.oceaneng.2022.110797.
DOI: https://dx.doi.org/10.21622/MARLOG.2025.14.1.60
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The International Maritime Transport and Logistics Journal (MARLOG)
E-ISSN: 2974-3141
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