Theoretical Article
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İnsansız ve Otonom Sistemlerde Sürü Zekâsının Uygulamaları ve Gelecek Perspektifleri

Year 2024, , 106 - 130, 30.11.2024
https://doi.org/10.54733/smar.1555925

Abstract

Bu makale, sürü zekâsı ve doğada kolektif davranış ilkelerinin insansız sistemler ve otonom örgütsel yapılar için potansiyel çıkarımlarını incelemektedir. Sürü zekâsı, bireysel birimlerin karmaşık ve organize bir bütün oluşturmak için basit kurallara göre etkileşime girdiği doğal sistemlerden esinlenmiştir. Bu ilkeler, kuş sürülerinin senkronize uçuşundan balık sürülerinin uyumlu yüzme davranışına kadar çok çeşitli durumlarda gözlemlenebilir. Çalışma, sürü zekâsı ilkelerinin, merkezi olmayan kontrol mekanizmaları ve otonom karar alma süreçleri ile daha esnek, dayanıklı ve verimli sistemler yaratma potansiyeline sahip olduğunu vurgulamaktadır. Dahası, bu yaklaşımların askeri operasyonlardan tarımsal ve çevresel izleme, afet müdahalesinden şehir planlamasına kadar birçok alanda uygulama bulabileceği önerilmektedir. Çalışma, doğada sürü davranışının ayrıntılı bir analizini sunmakta ve bu davranışların insansız sistemlerde nasıl taklit edilebileceğini ve optimize edilebileceğini tartışmaktadır. Bu bağlamda, sürü zekâsı ve kolektif davranış prensiplerinin insansız sistemler üzerindeki potansiyel etkileri, uyarlanabilirliklerini artırma, enerji verimliliğini optimize etme ve görev başarısını maksimize etme açısından değerlendirilmektedir. Ayrıca, bu prensiplerin insansız sistemleri beklenmedik durumlara ve değişen çevre koşullarına karşı daha dirençli hale getirmeye katkıda bulunabileceği ileri sürülmektedir. Sürü zekâsı prensiplerini, insansız hava, kara ve deniz araçlarında daha etkili koordinasyon sağlamak için kullanılabilir. Dijitalleşen sektörlerde, merkezi olmayan karar alma mekanizmaları oluşturarak işletmelerin esnekliği artırılabilir ve kaynak kullanımı optimize edilebilir.

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Applications and Future Perspectives of Swarm Intelligence in Unmanned and Autonomous Systems

Year 2024, , 106 - 130, 30.11.2024
https://doi.org/10.54733/smar.1555925

Abstract

This paper examines the potential implications of the principles of swarm intelligence and collective behavior in nature for unmanned systems and autonomous organizational structures. Swarm intelligence is inspired by natural systems in which individual units interact according to simple rules to form a complex and organized whole. These principles can be observed in a wide range of situations, from the synchronized flight of flocks of birds to the harmonized swimming behavior of schools of fish. The study emphasizes that swarm intelligence principles have the potential to create more flexible, resilient and efficient systems with decentralized control mechanisms and autonomous decision-making processes. Furthermore, it is suggested that these approaches can find applications in many fields, from military operations to agricultural and environmental monitoring, from disaster response to urban planning. The study provides a detailed analysis of swarm behavior in nature and discusses how these behaviors can be emulated and optimized in unmanned systems. In this context, the potential impacts of swarm intelligence and collective behavior principles on unmanned systems are evaluated in terms of increasing their adaptability, optimizing energy efficiency and maximizing mission success. It is also argued that these principles can contribute to making unmanned systems more resilient to contingencies and changing environmental conditions. Swarm intelligence principles can be used to provide more effective coordination in unmanned air, land and sea vehicles. In digitalizing sectors, the flexibility of businesses can be increased and resource usage can be optimized by creating decentralized decision-making mechanisms.

References

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  • Araujo, H., Mousavi, M. R., & Varshosaz, M. (2023). Testing, validation, and verification of robotic and autonomous systems: a systematic review. ACM Transactions on Software Engineering and Methodology, 32(2), 1-61.
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  • Hasbach, J. D., & Bennewitz, M. (2022). The design of self-organizing human–swarm intelligence. Adaptive Behavior, 30(4), 361-386.
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  • Janssen, M., & Van der Voort, H. (2020). Agile and adaptive governance in crisis response: Lessons from the COVID-19 pandemic. International Journal of Information Management, 55, 102180.
  • Javaid, S., Saeed, N., Qadir, Z., Fahim, H., He, B., Song, H., & Bilal, M. (2023). Communication and control in collaborative UAVs: Recent advances and future trends. IEEE Transactions on Intelligent Transportation Systems, 24(6), 5719-5739.
  • Kannan, S. K., & Diwekar, U. (2024). An enhanced particle swarm optimization (PSO) algorithm employing quasi-random numbers. Algorithms, 17(5), 195.
  • Kappagantula, S., Vojjala, S., Iyer, A. A., Velidi, G., Emani, S., & Vandrangi, S. K. (2023). Heuristic optimization of bat algorithm for heterogeneous swarms using perception. Operational Research in Engineering Sciences: Theory and Applications, 6(2), 52-77.
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  • Khaldi, B., & Cherif, F. (2015). An overview of swarm robotics: Swarm intelligence applied to multi-robotics. International Journal of Computer Applications, 126(2), 31-37.
  • Kliestik, T., Nica, E., Durana, P., & Popescu, G. H. (2023). Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of industrial internet of things. Oeconomia Copernicana, 14(4), 1097-1138.
  • Kolling, A., Walker, P., Chakraborty, N., Sycara, K., & Lewis, M. (2015). Human interaction with robot swarms: A survey. IEEE Transactions on Human-Machine Systems, 46(1), 9-26.
  • Kondam, A., & Yella, A. (2023). Advancements in artificial intelligence: Shaping the future of technology and society. Advances in Computer Sciences, 6(1), 1-7.
  • Lazic, D., & Schmickl, T. (2023). Will biomimetic robots be able to change a hivemind to guide honeybees’ ecosystem services?. Bioinspiration & Biomimetics, 18(3), 035004.
  • Lim, W. M., Kumar, S., & Ali, F. (2022). Advancing knowledge through literature reviews: ‘what’, ‘why’, and ‘how to contribute’. The Service Industries Journal, 42(7-8), 481-513.
  • Malone, T. W. (2004). The future of work: How the new order of business will shape your organization, your management style and your life. Harvard Business Review Press.
  • Marek, D., Paszkuta, M., Szyguła, J., Biernacki, P., Domański, A., Szczygieł, M., Król, M., & Wojciechowski, K. (2024). Swarm of drones in a simulation environment—efficiency and adaptation. Applied Sciences, 14(9), 3703.
  • Martorell-Torres, A., Guerrero-Sastre, J., & Oliver-Codina, G. (2024). Coordination of marine multi robot systems with communication constraints. Applied Ocean Research, 142, 103848.
  • Mishra, E. A., Das, M. N., & Panda, T. C. (2013). Swarm intelligence optimization: editorial survey. International Journal of Emerging Technology and Advanced Engineering, 3(1), 217-230.
  • Moffett, M. W., Garnier, S., Eisenhardt, K. M., Furr, N. R., Warglien, M., Sartoris, C., Ocasio, W., Knudsen, T., Bach, L. A. & Offenberg, J. (2021). Ant colonies: Building complex organizations with minuscule brains and no leaders. Journal of Organization Design, 10, 55-74.
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There are 74 citations in total.

Details

Primary Language English
Subjects Organisational Behaviour
Journal Section Conceptual Articles
Authors

Ayşe Meriç Yazıcı 0000-0001-6769-2599

Gökçe Akdemir Ömür 0000-0002-5327-8474

Duysal Askun Celik 0000-0002-0745-4756

Publication Date November 30, 2024
Submission Date September 25, 2024
Acceptance Date November 14, 2024
Published in Issue Year 2024

Cite

APA Yazıcı, A. M., Akdemir Ömür, G., & Askun Celik, D. (2024). Applications and Future Perspectives of Swarm Intelligence in Unmanned and Autonomous Systems. Sosyal Mucit Academic Review, 5(Innovative Conceptual Approaches to Social Sciences), 106-130. https://doi.org/10.54733/smar.1555925