Fuzzy Obstacle-avoiding Controller of Autonomous Mobile Robot Optimized by Genetic Algorithm under Multi-obstacles Environment

The question concerned with how to guide an autonomous mobile robot (AMR) moving in obstructive environment to avoid obstacles and reach the goal was studied. Firstly, the dynamic equations of AMR were constructed. Secondly, according to the number of obstacles, the avoiding behavior was studied, and a path-planning algorithm based on fuzzy control was also proposed. The angle between the obstacle and the goal, and the distance between the obstacle and AMR were inputs of the controller. Based on above, a fuzzy controller was used to modify the moving direction of AMR. At last, a genetic algorithm was used for optimization searching of parameters in design of the controller, the searching parameters included the 5 times 5 consequent variables of the control rule table, the bottom parameters of triangular membership functions and scaling factors. The fitness function was set as the total traveling length. Optimal controller was found for various obstructive environments through Matlab 6.5 simulation. The simulation results showed that the optimal controller obtained for complex environment was also fit for simpler ones