The demand among the population for household robots continues to rise. These include in particular mobile cleaning and lawn mowing robots. These are usually very expensive and still very inefficient. Especially for lawn mowing robots, it is essential to have visited the entire working space in order to perform their task correctly. However, the current state of the art is still random walk algorithms, which are very unreliable and inefficient. The present bachelor thesis therefore presents a method for intelligent path planning for mobile ”low cost”robots using a lawn mower robot. The robot is only equipped with binary sensors to detect its position in its working space, which is fraught with high uncertainties. By an intelligent representation of the already visited working space as well as the path planning inspired by neural networks, the lawn mower robot manages to achieve a decisive improvement in efficiency compared to the random walk.