Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm


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A radial basis function neural networks (RBFNs) mobile robot control system is automatically developed with the image processing and learned by the bacterial foraging particle swarm optimization (BFPSO) algorithm in this paper. The image-based architecture of robot model is self-generated to travel the routing path in the dynamical and complicated environments. The visible omni-directional image sensors capture the surrounding environment to represent the behavior model of the mobile robot system. Three parameterize RBFNs model with the centers and spreads of each radial basis function, and the connection weights to solve the mobile robot path traveling and routing problems. Several free parameters of radial basis functions can be automatically tuned by the direct of the specified fitness function. In additional, the proper number of radial basis functions of the constructed RBFNs can be chosen by the defined fitness function which takes this factor into account. The desired multiple objectives of the RBFNs control system are proposed to simultaneously approach the shorter path and avoid the unexpected obstacles. Evaluations of PSO and BFPSO show that the developed RBFNs robot systems skip the obstacles and efficiently achieve the desired targets as soon as possible.



Edited by:

Wen-Hsiang Hsieh






S. M. Joug et al., "Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm", Applied Mechanics and Materials, Vols. 284-287, pp. 2128-2136, 2013

Online since:

January 2013




[1] A. Sipahioglua, A Yazici, O. Parlaktuna and U. Gurel, Real-time tour construction for a mobile robot in a dynamic environment, Robotics and Autonomous Systems. 56 (2008) 289–295.

DOI: 10.1016/j.robot.2007.09.011

[2] C. -Y. Chen, H. -M. Feng, Hybrid Intelligent Vision-Based Car-Like Vehicle Backing Systems Design, Expert Systems with Applications, 36 (2009) 7500–7509.

DOI: 10.1016/j.eswa.2008.09.057

[3] H. -M. Feng, C. -Y. Chen, J. -H. Horng, Intelligent Omni-Directional Vision-Based Mobile Robot Fuzzy Systems Design and Implementation, Expert Systems with Applications, 37 (2010) 4009–4119.

DOI: 10.1016/j.eswa.2009.11.030

[4] H. -M. Feng, Self-Generation RBFNs Using Evolutional PSO Learning, NEUROCOMPUTING, 70 (2006), 241–251.

DOI: 10.1016/j.neucom.2006.03.007

[5] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, New Jersey, (1999).

[6] M. L. Corradini, G. Ippoliti, S. Longhi, Neural Networks Based Control of Mobile Robots: Development and Experimental Validation, Journal of Robotic Systems, 20 (2003) 587–600.

DOI: 10.1002/rob.10110

[7] A. D'Amico, G. Ippoliti and S. Longhi, A Multiple Models Approach for Adaptation and Learning in Mobile Robots Control, Journal of Intelligent & Robotic Systems, 47 (2006), 3–31.

DOI: 10.1007/s10846-006-9053-5

[8] K. M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Systems Magazine, (2002) 52–67.

DOI: 10.1109/mcs.2002.1004010

[9] Sastry V.R.S. Gollapudi, Shyam S. Pattnaik, O.P. Bajpai, S. Devi, K.M. Bakwad, Velocity Modulated Bacterial Foraging Optimization Technique (VMBFO), Applied Soft Computing, 11 (2011) 154–165.

DOI: 10.1016/j.asoc.2009.11.006

[10] H. -M. Feng, Autonomous Rule-Generated Fuzzy Systems Designs through Bacterial Foraging Particle Swarm Optimization Algorithm, Lecture Notes in Electrical Engineering, 98 (2011) 19–28.

DOI: 10.1007/978-3-642-21765-4_3

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