  In this paper, we propose a method for selecting the optimal footholds for
legged systems. The goal of the proposed method is to find the best foothold
for the swing leg on a local elevation map. We apply the Convolutional Neural
Network to learn the relationship between the local elevation map and the
quality of potential footholds. The proposed network evaluates the geometrical
characteristics of each cell on the elevation map, checks kinematic constraints
and collisions. During execution time, the controller obtains the qualitative
measurement of each potential foothold from the neural model. This method
allows to evaluate hundreds of potential footholds and check multiple
constraints in a single step which takes 10~ms on a standard computer without
GPGPU. The experiments were carried out on a quadruped robot walking over rough
terrain in both simulation and real robotic platforms.
