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

By Michele Focchi, Romeo Orsolino, Marco Camurri, Victor Barasuol, Carlos Mastalli, Darwin G. Caldwell, Claudio Semini. 2018

69 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Learning quadrupedal locomotion over challenging terrain (2020, background): “This distinguishes the presented approach from conventional controller design methods, which explicitly build in such reflexes and orchestrate their execution by a higher-level state machine (1, 3).”
Learning robust perceptive locomotion for quadrupedal robots in the wild (2022, background): “Existing controllers avoid catastrophic failures by simply refraining from using visual information in outdoor environments (2, 4, 38) or by adding heu ristically defined reflex rules (43, 44).”