Journal article
BiConMP: a nonlinear model predictive control framework for whole body motion planning
- Abstract:
- Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear model predictive control (MPC) framework, the BiConMP which can generate whole body trajectories online by efficiently exploiting the structure of the robot dynamics. BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes, and transitioning online between different gaits. Furthermore, the ability of BiConMP to generate nontrivial acyclic whole-body dynamic motions on the robot is presented. The same approach is also used to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. Finally, an extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Preview, Accepted manuscript, pdf, 19.7MB, Terms of use)
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- Publisher copy:
- 10.1109/tro.2022.3228390
Authors
- Publisher:
- IEEE
- Journal:
- IEEE Transactions on Robotics More from this journal
- Volume:
- 39
- Issue:
- 2
- Pages:
- 905-922
- Publication date:
- 2023-01-06
- DOI:
- EISSN:
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1941-0468
- ISSN:
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1552-3098
- Language:
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English
- Keywords:
- Pubs id:
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1318438
- Local pid:
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pubs:1318438
- Deposit date:
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2023-01-23
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2023
- Rights statement:
- © IEEE 2023
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/TRO.2022.3228390
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