Path generation for mobile robot navigation using genetic algorithm

The shortest/optimal path generation is essential for the efficient operation of a mobile robot. This paper presents an algorithm for global path planning to a goal for a mobile robot in an known environment. The algorithm uses the modified quadtree data structure to make database of the environment and utilizes a genetic algorithm to generate an optimal path for the robot to move along. Actually, the genetic algorithm consists of two stages, the first (named a minor league) checks if a chromosome can reach a goal position or not, and makes the individuals evolve; only a reaching chromosome is then transferred to the second stage (called a major league) and are then evolved. Finally, the best chromosome of individuals in the second stage survives, so that the optimal/shortest path is generated. It is shown that the authors' proposed method can find an optimal path very quickly according to simulation results.