Learning of fuzzy-behaviours using Particle Swarm Optimisation in behaviour-based mobile robot

Authors:
Andi Adriansyah;Shamsudin H. M. Amin
Affiliations:
Centre of Artificial Intelligence and Robotics (CAIRO), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, 81310 Johor Bahru, Malaysia.;Centre of Artificial Intelligence and Robotics (CAIRO), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, 81310 Johor Bahru, Malaysia
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2008

Citing 5
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Abstract

Behaviour-based mobile robots should have an ideal controller togenerate perfect behaviour action. A schema to overcome theseproblems is provided, known as Fuzzy Behaviour-based robot.However, tuning fuzzy parameters is not a simple effort. This paperpresents a technique to tune automatically fuzzy Rule Bases andfuzzy Membership Functions (MF) by Particle Swarm Optimisation(PSO), named as Particle Swarm Fuzzy Controller (PSFC). Thebehaviours are controlled by PSFC to generate individual commandaction. Later, a Context-Dependent Blending (CDB) based onmeta-fuzzy rules coordinates the commands to produce final controlaction. A Sigmoid Decreasing Inertia Weight has been proposed for agood balancing of global and local searches for obtaining goodconvergence speed and best accuracy of PSO algorithm. The algorithmis validated using parameters of MagellanPro mobile robot andtested by simulation using MATLAB/SIMULINK. Simulation results showthat the proposed model offers hopeful advantages and has improvedperformance.