This document explores an autonomous navigation approach based on the generation of free-obstacle navigation diagrams by means of using the skeletonization technique and other image processing algorithms. These diagrams are turned into a path by means of a vector of points and performed by a hybrid robotic platform, composed of a humanoid robot and one with omni-directional locomotion. Four different configurations (origin, obstacles and destination) were studied in order to test the algorithm performance, computing the mean squared error and the average of relative error for each point in the path. For all of the tested configurations, the proposed algorithm chooses the shortest path through the navigation diagram. The final point and mean point-by-point relative errors were obtained for each of the 4 proposed configurations, where the average values over all of them were 1.11% for the final point relative error and 1.23% for the mean relative error, which validates the use of the algorithm for the autonomous generation of navigation paths for the hybrid robotic platform under study.