• Logo
  • HamaraJournals

The Comparative Application of Fuzzy Logic and Genetic Algorithm for Intelligent Navigation of Mobile Robot in Dynamic Unknown Environment in the Case of Fixed and Moving Obstacles

Ali Reza Naderloo, Fazlollah Adibnia, Alimohammad Latif



Introduction:  In recent years, topics related to robotics have become one of the areas of research and development. In the meantime, smart robots are very popular, but the control and navigation of these devices are very difficult, and the lack of handling and staggering obstacles and avoidance of them, due to safe and secure routing, outweigh the basic needs of these systems.

Material and Methods:  In this research, for the purpose of solving the intelligent navigation problem, a moving robot in a dynamic unknown environment (conditions at any moment in the range of moving and obstacles in motion) and the choice of optimal path, the methods of genetic algorithm and fuzzy logic are used comparatively.

Results:  By using genetic algorithm and fuzzy logic methods, the robot can move in the dynamic and unknown environment to the optimal path to the target.

Conclusion: Information about the environment is also necessary to avoid obstacles, optimal path design and environment exploration, and to establish a clever relationship between perception and practice that requires the use of appropriate algorithms such as the genetic algorithm and fuzzy logic (fuzzy controller) are also needed to manage the control and navigation.


Ratering S, Gini M. Robot navigation in a known environment with unknown obstacles. Auton Robots. 1995; 1(2): 149-65.

Boada MJL, Egido V, Barber R, Salichs MA. Continuous reinforcement learning algorithm for skills learning in an autonomous mobile robot. Proceeding of IEEE International Conference on Industrial Electronics Society. Sevilla, Spain. 2002.

Borenstein J, Everett B, Feng L. Navigating mobile robots: Systems and techniques. AK Peters Ltd., Wellesley, MA, 1996.

Lamiraux F, Bonnafous B, Lefebvre O. Reactive path deformation for non-holomonic mobile robots. Robotics. 2004; 20(6): 967-77.

Qu Z, Wang J, Plaisted CE. A new analytical solution to mobile trajectory generation in the presence of moving obstacles. Robotics. 2005; 20(6): 978-92.

Vichuzhanin V. Realization of a fuzzy controller with fuzzy dynamic correction. Central European Journal of Engineering. 2012; 2 (3): 392–8.

Hagras HA. A hierarchial type two fuzzy logic control architecture for autonomous mobile robots. Fuzzy Systems. 2004; 12(4): 524-39.

Beom HR, Cho HS. A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning. Systems, Man, and Cybernetics. 1995; 25(2): 464–77.

Gerla G. Fuzzy logic programming and fuzzy control. Studia Logica. 2005; 79(2): 231-54.

Ye C, Yung NHC, Wang D. A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance. Systems, Man, and Cybernetics. 2003; 33(1): 17-27.

Mitchell M. An introduction to genetic algorithms. MIT Press, Cambridge, MA, 1996.

Goldberg DE. Genetic algorithms in search, optimization and machine learning. Addition-Wesley, 1989.

Petrenko YN, Alavi SE. Fuzzy logic and genetic algorithm technique for non-linear system of overhead crane. Proceeding of International Conference on Computational Technologies in Electrical and Electronics Engineering. 2010.

Vafaie H, Imam I. Feature selection methods: Genetic algorithms vs. greedy-like search. Proceeding of the International Conference on Fuzzy and Intelligent Control Systems. 1994.


  • There are currently no refbacks.