  We present a novel control strategy for dynamic legged locomotion in complex
scenarios, that considers information about the morphology of the terrain in
contexts when only on-board mapping and computation are available. The strategy
is built on top of two main elements: first a contact sequence task that
provides safe foothold locations based on a convolutional neural network to
perform fast and continuous evaluation of the terrain in search of safe
foothold locations; then a model predictive controller that considers the
foothold locations given by the contact sequence task to optimize target ground
reaction forces. We assess the performance of our strategy through simulations
of the hydraulically actuated quadruped robot HyQReal traversing rough terrain
under realistic on-board sensing and computing conditions.
