Heuristic Planning for Rough Terrain Locomotion in Presence of External Disturbances and Variable Perception Quality

The quality of visual feedback can vary significantly on a legged robot 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. If a locomotion algorithm is not designed to deal with this degradation, its planned trajectories might end-up to be inconsistent in reality. 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 leverage 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 external disturbances by showing the robot walking up a ramp while pulling a cart attached to its back.