  The quality of the visual feedback can vary significantly on a legged robot
that is meant to traverse unknown and unstructured terrains. The map of the
environment, acquired with online state-of-the-art algorithms, often degrades
after a few steps, due to sensing inaccuracies, slippage and unexpected
disturbances. When designing locomotion algorithms, this degradation can result
in planned trajectories that are not consistent with the reality, if not dealt
properly. In this work, we propose a heuristic-based planning approach that
enables a quadruped robot to successfully traverse a significantly rough
terrain (e.g., stones up to 10 cm of diameter), in absence of visual feedback.
When available, the approach allows also to exploit the visual feedback (e.g.,
to enhance the stepping strategy) in multiple ways, according to the quality of
the 3D map. The proposed framework also includes reflexes, triggered in
specific situations, and the possibility to estimate online an unknown
time-varying disturbance and compensate for it. We demonstrate the
effectiveness of the approach with experiments performed on our quadruped robot
HyQ (85 kg), traversing different terrains, such as: ramps, rocks, bricks,
pallets and stairs. We also demonstrate the capability to estimate and
compensate for disturbances, showing the robot walking up a ramp while pulling
a cart attached to its back.

Robotics