Particle swarm fuzzy controller for behavior-based mobile robot

The attractive researches in the field of mobile robotics is Behavior-based mobile robots. Behavior-based approach should have an ideal controller to generate perfect behavior action and able to handle conflicting ones that are seemingly irreconcilable. Schemas to overcome these problems based on Fuzzy Logic Controller (FLC) are provided, known as Fuzzy Behavior-based robot. This paper presents a novel approach that the fuzzy membership functions and fuzzy rule bases are tuned automatically by Particle Swarm Optimization (PSO), named as Particle Swarm Fuzzy Controller (PSFC). PSO is an optimization method that uses a principle of social behavior of a group of particles. The behaviors 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. 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.