Path Planning for Mobile Robot Based on Rough Set Genetic Algorithm

A rough set genetic algorithm (RSGA) is mainly studied to optimize the robot path planning speed and enhance the precision. At first, under the grid model and by the feasibility of the grid, the initial decision-making table of the robot is obtained, which can be simplified by the rough set theory to extract the minimal decision-making rules. We use these rules to train the initial population of the genetic algorithm (GA), and then solve the best path using GA. At last, the simulations are performed and contrasted under multi-group test conditions to the initial population of GA simplified by rough sets and generated randomly respectively, the results of which suggest that the effect of this suggested RSGA is significant at optimizing the robot path planning speed, especially in the complicated environments.