Many researches which have been done within this field, some of them used a “visibility graph” to set up a configuration space that can be mapped into a graph of vertices between which travel is possible in a straight line. The disadvantage of this method is time consuming. At the opposite, some researches have been based on dividing the world map into a grid and assign a cost to each square. Path cost is the sum of the cost of the grid squares through which the path passes. A grid model has been adopted by many authors, where the robot environment is divided into many squares and indicated to the presence of an object or not in each square [6, 9]. A cellular model, in other hand, has been developed by many researchers where the world of navigation is decomposed into cellular areas, some of which include obstacles. More, the skeleton models for map representation in buildings have been used to understand the environment’s structure, avoid obstacles and to find a suitable path of navigation. These researches have been developed in order to find an efficient automated path strategy for mobile robots to work within the described environment where the robot moves.