Obstacles Avoidance in a Self Path Plannning of a Polar Robot

The control of a robot´s movement in the self path planning problem requires the balance between time response and task requirements. In the case of robotic arms, both conditions are essential to turn a task efficient enough to be included into a productive environment. The robot´s movement has been researched several times trying to give them the capability of establishing a proper response under specific conditions without human interference. To achieve this goal, several strategies have been used, including spatial maps, area sweeping, iterative models and, as in this case, a neural genetic algorithm, in which the genetic algorithm builds the path based in a set of spatial positions, and the neural network learns from each of these paths to associate them with future positions and tasks. The target, is to adjust the robot´s performance to a level in which it is able to self define if a new path requires a searching process, if a previous sequence can be used, or if the points that conform the movement structure need to be adjusted in accordance to the accuracy needed