Applications and Future Perspectives of Swarm Intelligence in Unmanned and Autonomous Systems
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.
Anahtar Kelimeler
swarm intelligence, unmanned systems, autonomous organizational structures, principles of collective behavior
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.
