Ding, J., Della Santina, C., Lam, T.L. et al. (4 more authors) (2024) Robust Humanoid Locomotion via Sequential Stepping and Angular Momentum Optimization. IEEE Transactions on Industrial Electronics. ISSN 0278-0046
Abstract
Stepping strategy, including step time and step location modulation, and hip strategy, i.e., upper-body movement, play crucial roles in achieving robust humanoid locomotion. However, exploiting these balance strategies in a unified and flexible manner has not been well addressed. In this article, we propose a sequential convex optimization approach. Based on the linear inverted pendulum model, we modulate step parameters, including step location and step time, using quadratically constrained quadratic programming in real time. Then, based on the nonlinear inverted pendulum plus flywheel model, we regulate angular momentum using the linear model predictive control. To accommodate for scenarios with height variation, we consider nonlinear 3-D locomotion dynamics explicitly. The proposed approach is validated via comparison studies and extensive experiments on the humanoid with planar and linear feet. The results demonstrate enhanced robustness against dynamic disturbances and adaptability to real-world scenarios. On average, the enhanced stepping strategy rejects 135% larger external forces than our previous article. Also, robust locomotion across height-varying stepping stones is realized, which is rarely reported for a humanoid robot with planar feet.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of an article published in IEEE Transactions on Industrial Electronics, made available under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Angular momentum, humanoid robot, optimization, robust locomotion, step time modulation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Oct 2024 13:36 |
Last Modified: | 29 Oct 2024 13:51 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/TIE.2024.3433451 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218152 |