We present a model-based optimization framework that optimizes base pose and footholds simultaneously. It can generate motions in rough environments for a variety of different gaits in real time.
Title:
TAMOLS: Terrain-Aware Motion Optimization for Legged Systems
Authors:
Fabian Jenelten, Ruben Grandia, Farbod Farshidian, and Marco Hutter
IEEE: doi.org/10.1109/TRO.2022.3186804
arXiv: doi.org/10.48550/arXiv.2206.1...
code for mapping filters: github.com/leggedrobotics/ele...
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.
Acknowledgments:
This research was partially supported by the Swiss National Science Foundation (SNSF) as part of project No.188596, the European Union’s Horizon 2020 research and innovation programme under grant agreement No.780883 and No. 101016970, and the Swiss National Science Foundation through the National Centre of Competence in Research Robotics (NCCR Robotics).
Voice-over by Maria Alejandra Jaimes
28 июн 2022
