AUTONOMOUS ROBOT PATH PLANNING USING PARTICLE SWARM OPTIMIZATION IN STATIC AND OBSTACLE ENVIRONMENT

Abstract

This paper presents a simple and effective approach for mobile robot that detects and avoids densely populated and randomly distributed same size circular shaped static obstacles. The PSO (Particle Swarm Optimization) algorithm is implemented in the proposed technique to determine the optimum route of a robot from source to destination point until any obstacle is detected on its path. Once any obstacle is detected over the optimized path, the obstacle avoidance is done by moving robot towards the nearest safe point around the obstacle’s boundary which is pre-defined and calculated. Furthermore, proposed technique calculates the next point around the obstacle’s boundary which is nearest to the predefined target. For finding the effectiveness, PSO and GA (Genetic Algorithm) is applied and simulated on three different cases. Each case has variation in the location of starting point and goal. Finally, simulation results of all three cases are compared in-terms of Number of Iterations, Path Length and Execution Time. In result, the PSO performs better than GA in all three cases and provides a collision free smooth path in simulated environment.