A Comparison Study between Using the Pso Algorithm and the Ga in Mobile Robot’s Motion

This paper introduces using Particle Swarm Optimization algorithm (PSO) to improve the robot motion to arrive to its goal in cluttered environments. The Particle Swarm Optimizer is used to optimize the forces applied to the robot by selecting optimum factors of these applied forces. This improvement raises the robot ability to avoid the obstacles. A measure of smoothness is used to guide the PSO algorithm during the optimization. The optimized controller is simulated with different cases on Windows Vista using Matlab Software. These cases include environments with single obstacle up to three obstacles and multi-knee corridor. An Advanced Artificial Potential Field (AAPF) controller is used to control the robot’s motion. A comparison study of the robot motion optimization between using the PSO and using the Genetic algorithms is given. The results of the proposed system and the results of a previous work are introduced.