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arXiv · 2206.14049

TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

Fabian Jenelten·Ruben Grandia·Farbod Farshidian·Marco Hutter
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Abstract

Terrain geometry is, in general, non-smooth, non-linear, non-convex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This work presents the complete control pipeline capable of handling the aforementioned problems in real-time. We formulate a trajectory optimization problem that jointly optimizes over the base pose and footholds, subject to a heightmap. To avoid converging into undesirable local optima, we deploy a graduated optimization technique. We embed a compact, contact-force free stability criterion that is compatible with the non-flat ground formulation. Direct collocation is used as transcription method, resulting in a non-linear optimization problem that can be solved online in less than ten milliseconds. To increase robustness in the presence of external disturbances, we close the tracking loop with a momentum observer. Our experiments demonstrate stair climbing, walking on stepping stones, and over gaps, utilizing various dynamic gaits.

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Fabian Jenelten, Ruben Grandia, Farbod Farshidian, Marco Hutter. 2022-06-28. TAMOLS: Terrain-Aware Motion Optimization for Legged Systems. https://doi.org/10.1109/tro.2022.3186804

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