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Title:
TAMOLS: terrain-aware motion optimization for planting systems
Authors:
Fabian Jenelten, Ruben Grandia, Farbod Farshidian and Marco Hutter
IEEE: https://doi.org/10.1109/TRO.2022.3186804
arXiv: https://doi.org/10.48550/arXiv.2206.14049
filter mapping code: https://github.com/leggedrobotics/elevation_mapping_cupy
Abstract:
The terrain geometry is generally non-smooth, non-linear, non-convex and, when observed by a robot-oriented visual unit, appears partially occluded and noisy. This work presents the complete control pipeline capable of addressing the above-mentioned issues in real-time. We formulate a trajectory optimization problem that jointly optimizes the base position and footrests depending on a height map. To avoid ending up in unwanted local optima, we apply a gradual optimization technique. We embedded a compact, contact force-free stability criterion that is compatible with the non-planar ground formulation. Direct collocation is used as the transcription method, resulting in a nonlinear 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 climbing stairs, walking on stepping stones and over gaps, using different dynamic gaits.
Credits:
This research was supported in part by the Swiss National Science Foundation (SNSF) as part of project No. 188596, the European Union Horizon 2020 research and innovation program under grant agreement No. 780883 and No. 101016970, and the Swiss National Science Foundation through the National Competence Center for Research Robotics (NCCR Robotics).
Voice-over by Maria Alejandra Jaimes
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