Nonlinear Model Predictive Control of a 3D Hopping Robot: Leveraging Lie Group Integrators for Dynamically Stable Behaviors
Abstract
Achieving stable hopping has been a hallmark challenge in the field of dynamic legged locomotion. Controlled hopping is notably difficult due to extended periods of underactuation, combined with very short ground phases wherein ground interactions must be modulated to regulate global state. In this work, we explore the use of hybrid nonlinear model predictive control, paired with a low-level feedback controller in a multi-rate hierarchy, to achieve dynamically stable motions on a novel 3D hopping robot. In order to demonstrate richer behaviors on the manifold of rotations, both the planning and feedback layers must be done in a geometrically consistent fashion; therefore, we develop the necessary tools to employ Lie group integrators and an appropriate feedback controller. We experimentally demonstrate stable 3D hopping on a novel robot, as well as trajectory tracking and flipping in simulation.
Additional Information
This research was supported by NSF Graduate Research Fellowship No. DGE-1745301 and Raytheon, Beyond Limits, JPL RTD 1643049. The authors would like to especially thank Igor Sadalski, as well as Sergio Esteban and Adrian Boedtker Ghansah for their help with simulation and hardware implementation, and Will Compton for his experimental assistance.Attached Files
Submitted - 2209.11808.pdf
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Additional details
- Eprint ID
- 118466
- Resolver ID
- CaltechAUTHORS:20221219-234055506
- NSF Graduate Research Fellowship
- DGE-1745301
- Raytheon Company
- Beyond Limits
- JPL Research and Technology Development Fund
- 1643049
- Created
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2022-12-21Created from EPrint's datestamp field
- Updated
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2023-06-02Created from EPrint's last_modified field
- Caltech groups
- Center for Autonomous Systems and Technologies (CAST)