Optimization of Fuzzy Logic Controller Using PSO for Mobile Robot Navigation in an Unknown Environment

Abstract:

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.

• Periodical: Applied Mechanics and Materials (Volumes 541-542)
• Pages: 1053-1061
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.541-542.1053
• Online since: March 2014
• Authors: Mohammed Algabri*, Hedjar Ramdane, Hassan Mathkour, Khalid Al-Mutib, Mansour Alsulaiman
• Keywords: Fuzzy Logic Controller, Obstacle Avoidance, Particle Swarm Optimization (PSO), Robot Navigation

References

[1] S. Kosanam and D. Simon, Fuzzy membership function optimization for system identification using an extended Kalman filter, in Fuzzy Information Processing Society, (2006) 459–462.

DOI: 10.1109/nafips.2006.365453

[2] D. E. Goldberg and J. H. Holland, Genetic algorithms and machine learning, Machine learning, vol. 3, no. 2, (1988) 95–99.

[3] K. Benbouabdallah and Z. Qi-dan, Genetic Fuzzy Logic Control Technique for a Mobile Robot Tracking a Moving Target, IJCSI International Journal of Computer Science , (2013) 607-613.

DOI: 10.1109/iccsnt.2012.6526016

[4] Q. Liu, Y. Lu, and C. Xie, Optimal Genetic fuzzy obstacle avoidance controller of autonomous mobile robot based on ultrasonic sensors, ROBIO'06. IEEE International Conference (2006) 125–129.

DOI: 10.1109/robio.2006.340327

[5] K. S. Senthilkumar and K. K. Bharadwaj, Hybrid genetic-fuzzy approach to autonomous mobile robot, in Technologies for Practical Robot Applications Conference, (2009) 29–34.

DOI: 10.1109/tepra.2009.5339649

[6] S. F. Desouky and H. M. Schwartz, Genetic based fuzzy logic controller for a wall-following mobile robot, in American Control Conference, (2009) 3555–3560.

DOI: 10.1109/acc.2009.5159805

[7] A. AbuBaker, A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique, (2012).

[8] K. K. Tahboub and M. S. Al-Din, A Neuro-Fuzzy reasoning system for mobile robot navigation, JJMIE, (2009).

[9] A. Zhu and S. X. Yang, Neurofuzzy-based approach to mobile robot navigation in unknown environments, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions (2007) 610–621.

DOI: 10.1109/tsmcc.2007.897499

[10] R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in Sixth International Symposium of Micro Machine and Human Science, (1995) 39–43.

DOI: 10.1109/mhs.1995.494215

[11] A. Adriansyah and S. H. Amin, Learning of fuzzy-behaviours using Particle Swarm Optimisation in behaviour-based mobile robot, International journal of intelligent systems technologies and applications, vol. 5, (2008) 49–67.

DOI: 10.1504/ijista.2008.018166

[12] C. Wong, H. Wang, and S. Li, PSO-based motion fuzzy controller design for mobile robots, International Journal of Fuzzy Systems, vol. 10, (2008) 24.

[13] R. Martínez-Marroquín, O. Castillo, and J. Soria, Particle Swarm Optimization Applied to the Design of Type-1 and Type-2 Fuzzy Controllers for an Autonomous Mobile Robot, in Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition, Springer, (2009).

DOI: 10.1007/978-3-642-04516-5_15

[14] M. Gupta, L. Behera, and K. S. Venkatesh, PSO based modeling of Takagi-Sugeno fuzzy motion controller for dynamic object tracking with mobile platform, in International Multiconference Computer Science and Information Technology (IMCSIT), (2010).

DOI: 10.1109/imcsit.2010.5680034

[15] G. Imbens, W. Newey, and G. Ridder, Mean-square-error calculations for average treatment effects, (2005).

DOI: 10.2139/ssrn.820205

[16] Information on http: /www. theodorstorm. se/index/2866. html.

[17] Information on http: /www. k-team. com.