Heuristic Planning for Rough Terrain Locomotion in Presence of External Disturbances and Variable Perception Quality
This work addresses the challenge of robust locomotion for legged robots in unstructured environments with variable perception, which is incremental as it builds on existing planning methods with heuristic and reflex enhancements.
The authors tackled the problem of legged robot locomotion on rough terrain with unreliable visual feedback by proposing a heuristic-based planning approach that enables a quadruped robot to traverse obstacles like stones up to 10 cm in diameter without visual input and adaptively use it when available. They demonstrated effectiveness in experiments with the HyQ robot on various terrains and showed compensation for disturbances, such as pulling a cart up a ramp.
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.