The aim of the Master Thesis is to develop, study and validate a path planning algorithm that can support the navigation of rovers for planetary exploration. In particular, the objective is to develop a global path planning algorithm that could be integrated in the rover auto-nav system in order to improve the autonomy and velocity of the planetary rover. Global path planning using grid-based model of the environment is a well-known problem in AI, planning and robotics with a variety of methods and algorithms proposed so far. This work presents a deep-learning approach to the path planning problem. In particular, grid maps, containing information about the traversability of the terrain, are suitable input to modern neural networks, such as convolutional neural networks.