Learning traversability map of different robotic platforms for unstructured terrains path planning

This paper aims to propose an innovative method to obtain the traversability maps of unstructured environments and the best path between two points on the basis of the specific characteristics of the robots that has to perform a given mission. Taken in consideration a robot team that have to traverse an assigned terrain, the peculiar capabilities of each robot are underlined in a dynamic simulation environment and then embedded into a neural network finally used as a robot model for the generation of the traversability maps. On the basis of the obtained results, the best robot within the team (wheeled, legged, hybrid) can be selected. The proposed strategy, together with the obtained simulation results, are presented, carefully analyzed and then compared.