An exploratory path planning method based on genetic algorithm for autonomous mobile robots

The path planning task for mobile robots consists of define a trajectory to the robot leaves its start position and reach its goal without to collide with obstacles. In general, the robot needs to know previous information about the environment (e.g. maps, predefined routes) to plan its trajectory. In an exploration task, the robot does not know the environment and discovers it when moving to reach the goal coordinates. In this paper, an exploratory path planning aiming to reach a goal position is studied and a new method based on genetic algorithm, topological environment representation and realistic robot actions is proposed. In this method, the robots execute a sequence of reliable local actions (simple reactive behaviors) to move through the unknown environment, adopting a topological environment representation. They plan the path at the same time the environment is explored, in which the genetic algorithm evolves the sequence of actions to be executed. The results show that the squad of robots (GA population) reach the goal faster than an individual search. The proposed approach deal with environment traps better than the classical search A∗ algorithm and a variation of the A∗, named C∗, here also introduced.