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İnsansız ve Otonom Sistemlerde Sürü Zekâsının Uygulamaları ve Gelecek Perspektifleri

Yıl 2024, Cilt: 5 Sayı: Innovative Conceptual Approaches to Social Sciences, 106 - 130, 30.11.2024
https://doi.org/10.54733/smar.1555925

Öz

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.

Kaynakça

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  • Abualigah, L., Falcone, D., & Forestiero, A. (2023). Swarm intelligence to face IoT challenges. Computational Intelligence and Neuroscience, 2023, 4254194.
  • Akkaya, B., & Yazıcı, A. M. (2020). Comparing agile leadership with biomimicry-based gray wolf: Proposing a new model. Business & Management Studies: An International Journal, 8(2), 1455-1478.
  • Altshuler, Y. (2023). Recent developments in the theory and applicability of swarm search. Entropy, 25(5), 710.
  • 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.
  • Bajec, I. L., Zimic, N., & Mraz, M. (2007). The computational beauty of flocking: Boids revisited. Mathematical and Computer Modelling of Dynamical Systems, 13(4), 331-347.
  • Beni, G., & Wang, J. (1993). Robots and biological systems: Towards a new bionics?. In A. Dario, G. Sandini, & P. Aebischer (Eds.), Swarm intelligence in cellular robotic systems (pp. 703-712). Springer, Berlin, Heidelberg.
  • Berlinger, F., Gauci, M., & Nagpal, R. (2021). Implicit coordination for 3D underwater collective behaviors in a fish-inspired robot swarm. Science Robotics, 6(50), eabd8668.
  • Bhumichai, D., Smiliotopoulos, C., Benton, R., Kambourakis, G., & Damopoulos, D. (2024). The convergence of artificial intelligence and blockchain: The state of play and the road ahead. Information, 15(5), 268.
  • Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. Oxford University Press.
  • Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7, 1-41.
  • Bu, Y., Yan, Y., & Yang, Y. (2024). Advancement challenges in UAV swarm formation control: A comprehensive review. Drones, 8(7), 320.
  • Caballero-Martin, D., Lopez-Guede, J. M., Estevez, J., & Graña, M. (2024). Artificial intelligence applied to drone control: A state of the art. Drones, 8(7), 296.
  • Cai, W., Liu, Z., Zhang, M., & Wang, C. (2023). Cooperative artificial intelligence for underwater robotic swarm. Robotics and Autonomous Systems, 164, 104410.
  • Chandra Mohan, B., & Baskaran, R. (2011). Survey on recent research and implementation of ant colony optimization in various engineering applications. International Journal of Computational Intelligence Systems, 4(4), 566-582.
  • Chen, A., Xie, F., Wang, J., & Chen, J. (2023). Intelligent optimization method of human–computer interaction interface for UAV cluster attack mission. Electronics, 12(21), 4426.
  • Cognominal, M., Patronymic, K., & Wańkowicz, A. (2021). Evolving field of autonomous mobile robotics: Technological advances and applications. Fusion of Multidisciplinary Research, An International Journal, 2(2), 189-200.
  • Csaszar, F. A., & Steinberger, T. (2022). Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory. Academy of Management Annals, 16(1), 1-37.
  • Da Silva, A. R., Lages, W. S., & Chaimowicz, L. (2008). Improving boids algorithm in GPU using estimated self occlusion. Proceedings of SBGames’ 08: Computing Track, Computers in Entertainment (CIE), 41-46.
  • Dorigo, M. (2007). Ant colony optimization. Scholarpedia, 2(3), 1461.
  • Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28-39.
  • Fan, R., Wang, J., Han, W., & Xu, B. (2023). UAV swarm control based on hybrid bionic swarm intelligence. Guidance, Navigation and Control, 3(02), 2350008.
  • Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm Intelligence, 1, 3-31.
  • Hasbach, J. D., & Bennewitz, M. (2022). The design of self-organizing human–swarm intelligence. Adaptive Behavior, 30(4), 361-386.
  • Islam, T., Islam, M. E., & Ruhin, M. R. (2018). An analysis of foraging and echolocation behavior of swarm intelligence algorithms in optimization: ACO, BCO and BA. International Journal of Intelligence Science, 8(01), 82211.
  • 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.
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  • 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.
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Applications and Future Perspectives of Swarm Intelligence in Unmanned and Autonomous Systems

Yıl 2024, Cilt: 5 Sayı: Innovative Conceptual Approaches to Social Sciences, 106 - 130, 30.11.2024
https://doi.org/10.54733/smar.1555925

Öz

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.

Kaynakça

  • Abdulsaheb, J. A., & Kadhim, D. J. (2023). Classical and heuristic approaches for mobile robot path planning: A survey. Robotics, 12(4), 93.
  • Abualigah, L., Falcone, D., & Forestiero, A. (2023). Swarm intelligence to face IoT challenges. Computational Intelligence and Neuroscience, 2023, 4254194.
  • Akkaya, B., & Yazıcı, A. M. (2020). Comparing agile leadership with biomimicry-based gray wolf: Proposing a new model. Business & Management Studies: An International Journal, 8(2), 1455-1478.
  • Altshuler, Y. (2023). Recent developments in the theory and applicability of swarm search. Entropy, 25(5), 710.
  • 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.
  • Bajec, I. L., Zimic, N., & Mraz, M. (2007). The computational beauty of flocking: Boids revisited. Mathematical and Computer Modelling of Dynamical Systems, 13(4), 331-347.
  • Beni, G., & Wang, J. (1993). Robots and biological systems: Towards a new bionics?. In A. Dario, G. Sandini, & P. Aebischer (Eds.), Swarm intelligence in cellular robotic systems (pp. 703-712). Springer, Berlin, Heidelberg.
  • Berlinger, F., Gauci, M., & Nagpal, R. (2021). Implicit coordination for 3D underwater collective behaviors in a fish-inspired robot swarm. Science Robotics, 6(50), eabd8668.
  • Bhumichai, D., Smiliotopoulos, C., Benton, R., Kambourakis, G., & Damopoulos, D. (2024). The convergence of artificial intelligence and blockchain: The state of play and the road ahead. Information, 15(5), 268.
  • Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. Oxford University Press.
  • Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7, 1-41.
  • Bu, Y., Yan, Y., & Yang, Y. (2024). Advancement challenges in UAV swarm formation control: A comprehensive review. Drones, 8(7), 320.
  • Caballero-Martin, D., Lopez-Guede, J. M., Estevez, J., & Graña, M. (2024). Artificial intelligence applied to drone control: A state of the art. Drones, 8(7), 296.
  • Cai, W., Liu, Z., Zhang, M., & Wang, C. (2023). Cooperative artificial intelligence for underwater robotic swarm. Robotics and Autonomous Systems, 164, 104410.
  • Chandra Mohan, B., & Baskaran, R. (2011). Survey on recent research and implementation of ant colony optimization in various engineering applications. International Journal of Computational Intelligence Systems, 4(4), 566-582.
  • Chen, A., Xie, F., Wang, J., & Chen, J. (2023). Intelligent optimization method of human–computer interaction interface for UAV cluster attack mission. Electronics, 12(21), 4426.
  • Cognominal, M., Patronymic, K., & Wańkowicz, A. (2021). Evolving field of autonomous mobile robotics: Technological advances and applications. Fusion of Multidisciplinary Research, An International Journal, 2(2), 189-200.
  • Csaszar, F. A., & Steinberger, T. (2022). Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory. Academy of Management Annals, 16(1), 1-37.
  • Da Silva, A. R., Lages, W. S., & Chaimowicz, L. (2008). Improving boids algorithm in GPU using estimated self occlusion. Proceedings of SBGames’ 08: Computing Track, Computers in Entertainment (CIE), 41-46.
  • Dorigo, M. (2007). Ant colony optimization. Scholarpedia, 2(3), 1461.
  • Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28-39.
  • Fan, R., Wang, J., Han, W., & Xu, B. (2023). UAV swarm control based on hybrid bionic swarm intelligence. Guidance, Navigation and Control, 3(02), 2350008.
  • Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm Intelligence, 1, 3-31.
  • Hasbach, J. D., & Bennewitz, M. (2022). The design of self-organizing human–swarm intelligence. Adaptive Behavior, 30(4), 361-386.
  • Islam, T., Islam, M. E., & Ruhin, M. R. (2018). An analysis of foraging and echolocation behavior of swarm intelligence algorithms in optimization: ACO, BCO and BA. International Journal of Intelligence Science, 8(01), 82211.
  • 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.
  • Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation (Vol. 5, pp. 4104-4108). IEEE.
  • 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.
  • Mohsan, S. A. H., Othman, N. Q. H., Li, Y., Alsharif, M. H., & Khan, M. A. (2023). Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics, 16, 109-137.
  • Muhsen, D. K., Sadiq, A. T., & Raheem, F. A. (2023). A survey on swarm robotics for area coverage problem. Algorithms, 17(1), 3.
  • Nayak, J., Swapnarekha, H., Naik, B., Dhiman, G., & Vimal, S. (2023). 25 years of particle swarm optimization: Flourishing voyage of two decades. Archives of Computational Methods in Engineering, 30, 1663-1725.
  • Netjinda, N., Achalakul, T., & Sirinaovakul, B. (2015). Particle swarm optimization inspired by starling flock behavior. Applied Soft Computing, 35, 411-422.
  • Olivares, R., Noel, R., Guzmán, S. M., Miranda, D., & Munoz, R. (2024). Intelligent learning-based methods for determining the ideal team size in agile practices. Biomimetics, 9(5), 292.
  • Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22(3), 52-67.
  • Powell, J., McCafferty-Leroux, A., Hilal, W., & Gadsden, S. A. (2024). Smart grids: A comprehensive survey of challenges, industry applications, and future trends. Energy Reports, 11, 5760-5785.
  • Puente-Castro, A., Rivero, D., Pazos, A., & Fernandez-Blanco, E. (2022). A review of artificial intelligence applied to path planning in UAV swarms. Neural Computing and Applications, 34, 153-170.
  • Rajasekhar, A., Lynn, N., Das, S., & Suganthan, P. N. (2017). Computing with the collective intelligence of honey bees–a survey. Swarm and Evolutionary Computation, 32, 25-48.
  • Rashid, A. B., & Kausik, A. K. (2024). AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Advances, 7, 100277.
  • Reiche, B. S. (2023). Between interdependence and autonomy: Toward a typology of work design modes in the new world of work. Human Resource Management Journal, 33(4), 1001-1017.
  • Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model [Conference Presentation]. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques, New York, NY, United States.
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  • Schranz, M., Di Caro, G. A., Schmickl, T., Elmenreich, W., Arvin, F., Şekercioğlu, A., & Sende, M. (2021). Swarm intelligence and cyber-physical systems: concepts, challenges and future trends. Swarm and Evolutionary Computation, 60, 100762.
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  • Seeley, W. W. (2010). Anterior insula degeneration in frontotemporal dementia. Brain Structure and Function, 214, 465-475.
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  • Wu, G., Xu, T., Sun, Y., & Zhang, J. (2022). Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence. International Journal of Advanced Robotic Systems, 19(2), 1-20.
  • Yazıcı, A. M., & Kınay, M. (2021). How biomimicry inspires robotics for space research. Havacılık ve Uzay Çalışmaları Dergisi, 1(2), 64-77.
  • Zhang, F., Yu, J., Lin, D., & Zhang, J. (2022). UnIC: Towards unmanned intelligent cluster and its integration into society. Engineering, 12, 24-38.
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Toplam 74 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Örgütsel Davranış
Bölüm Kavramsal Makale
Yazarlar

Ayşe Meriç Yazıcı 0000-0001-6769-2599

Gökçe Akdemir Ömür 0000-0002-5327-8474

Duysal Askun Celik 0000-0002-0745-4756

Yayımlanma Tarihi 30 Kasım 2024
Gönderilme Tarihi 25 Eylül 2024
Kabul Tarihi 14 Kasım 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 5 Sayı: Innovative Conceptual Approaches to Social Sciences

Kaynak Göster

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