Development and Simulation of an Algorithm for UAV Swarm Coordination and Collision Avoidance

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The use of Unmanned Aerial Vehicles (UAVs) is increasing as their usage enhance many activities in our modern world. These include their specific roles in warfare, surveillance, agricultural activities, entertainments with attendant economic importance. In areas grappling with insecurity challenges due to banditry, kidnappings, oil spillage and theft, farmers and herdsmen clashes, utilizing more than one UAV in an area for surveillance is not only good but more advantageous. If many UAVs are used in an area at the same time, they are termed swarm or group of UAVs. Their operations in this manner, are seen as more scalable and reliable mode of using UAVs in current and future applications. Thus, usage of multiple UAVs that operate together as a cohesive unit are redundant and scalable, performing tasks that would be challenging or inefficient for a single UAV to accomplish. However, operating a group of UAVs as one unit can become expensive and risky if they are not properly coordinated. The UAVs may collide, causing catastrophic damage and requiring costly repairs. The need for autonomous coordination therefore comes from the vast number of vehicles, which might be intrinsic members of the system as a whole. Also, all UAVs in the swarm are to contribute to the effective execution of task without wasting resources. These imply that an intelligent coordination algorithm that implements awareness for swarm UAVs to avoid risky states is required. This paper presents the development and implementation of an algorithm for intra-swarm collision avoidance by treating each UAV in a swarm unit as individual agent capable of a homogenous number of tasks modelled as contours using their field of view and received signal strength indication.

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April 2026

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[1] Kellermann, R., Biehle, T., Fischer, L. (2020) Drones for parcel and passenger transportation: A literature review. Transportation Research Interdisciplinary Perspectives, Volume 4, 100088, ISSN 2590-1982.

DOI: 10.1016/j.trip.2019.100088

Google Scholar

[2] Abiodun, T. F., & Dahiru, M. Y. (2021), Public support and prospects of drones or unmanned aerial vehicles (uavs) technologies for effective transport and logistics delivery in nigeria. International Journal of Advanced Academic Research (Sciences, Technology and Engineering) ISSN: 2488-9849 Vol. 7, Issue 1 (January, 2021).

DOI: 10.46654/ij.24889849

Google Scholar

[3] Rajendran, S. & Shulman, J., (2020). Study of emerging air taxi network operation using discrete-event systems simulation approach. Journal of Air Transport Management, Volume 87, 101857, ISSN 0969-6997.

DOI: 10.1016/j.jairtraman.2020.101857

Google Scholar

[4] Siciliano B. (Editor), Khatib O. (Editor), Springer Handbook of Robotics (Springer Handbooks) Second Edition 2016.

Google Scholar

[5] Valavanis, K. P., & Vachtsevanos, G. J. (2015). Handbook of unmanned aerial vehicles. Handbook of Unmanned Aerial Vehicles, 1–3022.

DOI: 10.1007/978-90-481-9707-1

Google Scholar

[6] Usage Patterns and Costs of Unmanned Aerial Systems | Congressional Budget Office. (n.d.). Retrieved March 9, 2024, from https://www.cbo.gov/publication/57090.

Google Scholar

[7] Poudel, S., Arafat, M. Y., & Moh, S. (2023). Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey. Sensors 2023, Vol. 23, Page 3051, 23(6), 3051.

DOI: 10.3390/S23063051

Google Scholar

[8] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, "Electron spectroscopy studies on magneto-optical media and plastic substrate interface," IEEE Transl. J. Magn. Japan, vol. 2, p.740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p.301, 1982].

DOI: 10.1109/tjmj.1987.4549593

Google Scholar

[9] Javed, S., Hassan, A., Ahmad, R., Ahmed, W., Ahmed, R., Saadat, A., & Guizani, M. (2024). State-of-the-Art and Future Research Challenges in UAV Swarms. IEEE Internet of Things Journal.

DOI: 10.1109/JIOT.2024.3364230

Google Scholar

[10] U.S. Department of Defense. (n.d.). Retrieved May 17, 2024, from https://www.defense.gov/.

Google Scholar

[11] Watts, A. C., Ambrosia, V. G., & Hinkley, E. A. (2012). Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use. Remote Sensing 2012, Vol. 4, Pages 1671-1692, 4(6), 1671–1692.

DOI: 10.3390/RS4061671

Google Scholar

[12] Ruetten, L., Regis, P. A., Feil-Seifer, D., & Sengupta, S. (2020). Area-Optimized UAV Swarm Network for Search and Rescue Operations. 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020, 613–618.

DOI: 10.1109/CCWC47524.2020.9031197

Google Scholar

[13] Coppola, P., & Silvestri, F., (2019) Autonomous vehicles and future mobility solutions, Editor(s): Pierluigi Coppola, Domokos Esztergár-Kiss, Autonomous Vehicles and Future Mobility, Elsevier, Pages 1-15, ISBN 9780128176962.

DOI: 10.1016/B978-0-12-817696-2.00001-9

Google Scholar

[14] Alghamdi, Y., Munir, A., & Manh La, H., (2021) Architecture, Classification, and Applications of Contemporary Unmanned Aerial Vehicles IEEE Consumer Electronics Magazine PP (99):1-1.

DOI: 10.1109/MCE.2021.3063945

Google Scholar

[15] UN. (2018). World Urbanization Prospects: The 2018 Revision. Key Facts. United Nations, Department of Economic and Social Affairs, Population Division.

DOI: 10.18356/cd4eece8-en

Google Scholar

[16] Zhuozhen, T., & Ma, H., (2021), An overview of path planning algorithms, 6th International Conference on Energy Science and Applied Technology, IOP Conf. Series: Earth and Environmental Science 804 (2021) 022024 IOP Publishing.

DOI: 10.1088/1755-1315/804/2/022024

Google Scholar

[17] Gad, Ahmed. (2022). Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering. 29. 2531–2561.

DOI: 10.1007/s11831-021-09694-4

Google Scholar

[18] Yannis Marinakis, Magdalene Marinaki, A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem, Computers & Operations Research, Volume 37, Issue 3, 2010, Pages 432-442, ISSN 0305-0548.

DOI: 10.1016/j.cor.2009.03.004

Google Scholar

[19] Nigam, N., Bieniawski, S., Kroo, I., Vian, J., & Member, S. (2012). Control of multiple UAVs for persistent surveillance: Algorithm and flight test results. Ieeexplore.Ieee.Org, 20(5).

DOI: 10.1109/TCST.2011.2167331

Google Scholar

[20] Fan, X., Li, H., Chen, Y., & Dong, D. (2024). A Path-Planning Method for UAV Swarm under Multiple Environmental Threats. Drones, 8(5), 171.

DOI: 10.3390/drones8050171

Google Scholar

[21] Phadke, A., Medrano, F. A., Sekharan, C. N., & Chu, T. (2023). Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process. Sensors 2023, Vol. 23, Page 7359, 23(17), 7359.

DOI: 10.3390/s23177359

Google Scholar

[22] Wei, Y., Madey, G., Simulation, M. B.-P. of the A.-D., & 2013, U. (2013). Agent-based simulation for uav swarm mission planning and execution. Nd.EduY Wei, GR Madey, MB BlakeProceedings of the Agent-Directed Simulation Symposium, 2013•nd.Edu. https://www3.nd.edu/~dddas/AFOSR/resources/papers/WEI_ads2013.pdf.

DOI: 10.1016/j.procs.2013.05.364

Google Scholar

[23] Zhou, Y., Rao, B., Access, W. W.-I., & 2020, U. (2020). UAV swarm intelligence: Recent advances and future trends. Ieeexplore.Ieee.OrgY Zhou, B Rao, W WangIeee Access, 2020•ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/9214446/.

DOI: 10.1109/access.2020.3028865

Google Scholar

[24] Wei, Y., Blake, M., Science, G. M.-P. C., & 2013, undefined. (n.d.). An operation-time simulation framework for UAV swarm configuration and mission planning. Elsevier. Retrieved April 9, 2024, from https://www.sciencedirect.com/science/article/pii/S1877050913005073.

DOI: 10.1016/j.procs.2013.05.364

Google Scholar

[25] Coppola, M., Mcguire, K. N., Scheper, K. Y. W., & Croon, G. C. H. E. De. (2018). On-board communication-based relative localization for collision avoidance in Micro Air Vehicle teams. Autonomous Robots, 42(8), 1787–1805.

DOI: 10.1007/s10514-018-9760-3

Google Scholar

[26] Chen, Z., Yan, J., Shi, K., Yu, Q., & Yuan, W. (2023). A Survey on Open-Source Simulation Platforms for Multi-Copter UAV Swarms. 1–24.

DOI: 10.3390/robotics12020053

Google Scholar

[27] Pagliari, E., Davoli, L., & Ferrari, G., (Wi-Fi-based Real-Time UAV Localization: a Comparative Analysis between RSSI-based and FTM-based approaches. IEEE Transactions on Aerospace and Electronic Systems.

DOI: 10.1109/taes.2024.3433829

Google Scholar

[28] Craighead, J., Murphy, R., Burke, J., & Goldiez, B. (2007). A survey of commercial & open-source unmanned vehicle simulators. Proceedings - IEEE International Conference on Robotics and Automation, 852–857.

DOI: 10.1109/ROBOT.2007.363092

Google Scholar

[29] Siciliano, B., & Khatib, O. (2016). Springer handbook of robotics. In Springer Handbook of Robotics.

DOI: 10.1007/978-3-319-32552-1

Google Scholar

[30] Alqudsi, Y., Makaraci, M. UAV swarms: research, challenges, and future directions. J. Eng. Appl. Sci. 72, 12 (2025).

DOI: 10.1186/s44147-025-00582-3

Google Scholar