Modeling the Optimal Routing of Autonomous Microbirds Using Fuzzy Inference Systems and Evolutionary Algorithms

Document Type : -

Authors

1 Assistant Professor, Academy of Artificial Intelligence and New Technologies, Tehran, Iran

2 Master's degree, Iran University of Science and Technology, Tehran, Iran

Abstract

An automatic guidance system in drones can be divided into two parts: path design and follow the path. In these systems, due to the time limitations and the uncertainty in the conditions prevailing on the battlefield, it is very important to use the expertise of the military commanders and emulate their behavior in these systems, due to the time constraints and the uncertainty in the conditions prevailing on the battlefield, it is very important to use the expertise of military commanders and simulate their behavior in the design and route tracking processes. Therefore, according to the characteristics of fuzzy inference systems, by using them, the commanders' expertise can be applied in autonomous drones. In this article, in order to model the UAVs offensive routing, a Mamdani fuzzy inference system with five inputs and one output is used to determine the edges weight. At each stage of decision-making process of choosing the optimal route, the need to perform complex mathematical calculations can make drone routing algorithms useless in real-world conditions. Therefore, in order to reduce the dependence of the routing and target tracking system on mathematical calculations and to use the advantages of anytime algorithms to produce the optimal answer, genetic algorithm and non-dominant sorting genetic algorithm-II have been used as the method of solving the model. The simulation results show that the combination of genetic algorithm and fuzzy inference system has a very favorable efficiency in performing the routing process in real world conditions and can meet the operational needs of commanders in this field.

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References

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Journal of Advanced Defense Science & Technology
Volume 14, Issue 3 - Serial Number 53
November 2023
Pages 153-163

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History

  • Receive Date: 18 September 2023
  • Revise Date: 05 November 2023
  • Accept Date: 13 November 2023

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