Mobile Robot Path Planning in Static Environment

The success of Particle Swarm Optimization (PSO) and Genetic algorithm (GA) as single objective optimizer has motivated researchers to extend the use of this bio- inspired techniques to other areas. One of them is multi-objective optimization. As a part of this review we present a classification of the approaches and identify the main approaches here. We describe useful performance measures and simulation results of conventional Genetic algorithm and PSO. We extend this to multi-objective genetic algorithm and PSO. This means that GA and PSO optimizes path based on two criteria: length and difficult. Another method that has new to this field of research is the Artificial Potential field method. In this method the entire space is supposed to contain a potential field and we calculate the net force that is acted upon the robot to reach its goal