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Intelligent Routing Therapist Nano Robots in a Dynamic Environment of the Human Body with the Help of Fuzzy Logic

Hamidreza Naderloo, Ali Reza Naderloo



Introduction: Using nanotechnology, scientists have been able Nano robot knowledge that the human body is designed to be therapeutic and protective role and play therapist. This Nano robot could help in different ways such as a capsule or injection into the vascular system of the body, the human body and controlled or programmed manner (smart) to play the role of a therapist. . These robots can perform a routing optimization in the shortest time and shortest distance traveled, upon detection of possible damage, damage to the site and using nanotechnology to repair it.

Material and Methods: In this class of problems to find the path or paths (preferably optimum), which move the robot without colliding with obstacles static/dynamic of a specific origin to its destination. In this paper presents a simulation system under manual control, wireless routing protocol, and is used for navigation Nano robot because of the problems raised in writing and to fix bugs of the system, an intelligent routing system, To move Nano robot therapist, based on the fuzzy control system in which the steering motor in the vicinity of obstacles and destination point for platform Nano robot be issued.

Results: For the more accurate and more complete routing in dynamic environments without the structure of the human body (the restrictions are not predetermined and fixed), need to work on issues relating to robot motion planning and to determine the optimal path will be moved. This research finally revealed that a system following the fuzzy logic for a mobile Nano robot navigation.

Conclusion: The present findings from the implemented designs here took into account the merits and demerits and evaluated the optimal navigation issue in robots following the fuzzy logic and the genetic algorithm. We found out that to solve such issues more efficiently.


Nasiri M. New technologies. Med & Lab Engineering Magazine. 2011; 132(11): 8-11.

Alavi SM, Ghanbarzadeh A, Alavi SE, Ghanavati M. Using fuzzy method to robot routing based on sensors’ values. Proceeding of 1st National Conference of Machanical Systems Simulation. Ahvaz, Iran. 2011.

Borenstein J, Everett HR, Feng L. Navigating mobile robots: Systems and techniques. A.K.Peters Ltd.; Wellesley. 1996.

Barraquand J, Langlois B, Latombe JC. Numerical potential techniques for robot path planning. IEEE Transactions on Systems, Man, and Cybernetics. 1992; 22(2): 224-41.

Maaref H, Barret C. Sensor-based navigation of a mobile robot in an indoor environment. Robotics and Automation Systems. 2002; 38(1): 1-18.

Storn R, Price K. Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization. 1997; 11(4): 341-59.

MotieGhader H, Lotfi Sh, Seyed Asfehlan MM. Intelligent optimization methods. Azad Islamic University Publication; Shabestar. 2010.

Cavalcanti A, Freitas RA Jr. Nanorobotics control design: A collective behavior approach for medicine. IEEE Trans on NanoBioScience. 2005; 4(2): 133-40. PMID: 16117021

Yang X, Moallem M, Patel RV. An improved fuzzy logic based navigation system for mobile robots. Proceedings of Intelligent Robots and Systems Conference. Las Vegas, USA. 2003.

Pholsiri C, Kapoor C, Tesar D. Real-time robot capability analysis. Proceeding of Computers and Information in Engineering Conference. Long Beach, Canada. 2005.

Petrenko YN, Alavi SE. Fuzzy logic and genetic algorithm technique for non-linear system of overhead crane. IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering (SIBIRCON), 2010.

Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. Addition-Wesley Professional; 1989.

DOI: http://dx.doi.org/10.24200/ijmi.v5i0.148


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