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

Behaviour-based mobile robots should have an ideal controller to generate perfect behaviour action. A schema to overcome these problems is provided, known as Fuzzy Behaviour-based robot. However, tuning fuzzy parameters is not a simple effort. This paper presents a technique to tune automatically fuzzy Rule Bases and fuzzy Membership Functions (MF) by Particle Swarm Optimisation (PSO), named as Particle Swarm Fuzzy Controller (PSFC). The behaviours are controlled by PSFC to generate individual command action. Later, a Context-Dependent Blending (CDB) based on meta-fuzzy rules coordinates the commands to produce final control action. A Sigmoid Decreasing Inertia Weight has been proposed for a good balancing of global and local searches for obtaining good convergence speed and best accuracy of PSO algorithm. The algorithm is validated using parameters of MagellanPro mobile robot and tested by simulation using MATLAB/SIMULINK. Simulation results show that the proposed model offers hopeful advantages and has improved performance.