Ding, J, Zhou, C orcid.org/0000-0002-6677-0855 and Xiao, X (2019) Energy-Efficient Bipedal Gait Pattern Generation via CoM Acceleration Optimization. In: 2018 IEEE-RAS International Conference on Humanoid Robots. 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), 06-09 Nov 2018, Beijing, China. IEEE , pp. 238-244. ISBN 9781538672839
Abstract
Energy consumption for bipedal walking plays a central role for a humanoid robot with limited battery capacity. Studies have revealed that exploiting the allowable Zero Moment Point region (AZR) and Center of Mass (CoM) height variation (CoMHV) are strategies capable of improving energy performance. In general, energetic cost is evaluated by integrating the electric power of multi joints. However, this Joint-Power-based Index requires computing joint torques and velocities in advance, which usually requires time-consuming iterative procedures, especially for multi-joints robots. In this work, we propose a CoM-Acceleration-based Optimal Index (CAOI) to synthesize an energetically efficient CoM trajectory. The proposed method is based on the Linear Inverted Pendulum Model, whose energetic cost can be easily measured by the input energy required for driving the point mass to track a reference trajectory. We characterize the CoM motion for a single walking cycle and define its energetic cost as Unit Energy Consumption. Based on the CAOI, an analytic solution for CoM trajectory generation is provided. Hardware experiments demonstrated the computational efficiency of the proposed approach and the energetic benefits of exploiting AZR and CoMHV strategies.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jul 2019 13:18 |
Last Modified: | 05 Sep 2019 21:51 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/HUMANOIDS.2018.8625042 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148952 |