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2017 Volume 32
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RESEARCH ARTICLE   Open Access    

Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references

  • Department of Electrical Engineering

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  • Abstract: This paper presents a parameterized gait generator based on linear inverted pendulum model (LIPM) theory, which allows users to generate a natural gait pattern with desired step sizes. Five types of zero moment point (ZMP) components are proposed for formulating a natural ZMP reference, where ZMP moves continuously during single support phases instead of staying at a fixed point in the sagittal and lateral plane. The corresponding center of mass (CoM) trajectories for these components are derived by LIPM theory. To generate a parameterized gait pattern with user-defined parameters, a gait planning algorithm is proposed, which determines related coefficients and boundary conditions of the CoM trajectory for each step. The proposed parameterized gait generator also provides a concept for users to generate gait patterns with self-defined ZMP references by using different components. Finally, the feasibility of the proposed method is validated by the experimental results with a teen-sized humanoid robot, David, which won first place in the sprint event at the 20th Federation of International Robot-soccer Association (FIRA) RoboWorld Cup.
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  • Cite this article

    Ya-Fang Ho, Tzuu-Hseng S. Li, Ping-Huan Kuo, Yan-Ting Ye. 2017. Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000138
    Ya-Fang Ho, Tzuu-Hseng S. Li, Ping-Huan Kuo, Yan-Ting Ye. 2017. Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000138
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RESEARCH ARTICLE   Open Access    

Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references

  • Department of Electrical Engineering

  • Published online: 09 September 2016
The Knowledge Engineering Review  32,  2017, 32(1)  |  Cite this article

Abstract: Abstract: This paper presents a parameterized gait generator based on linear inverted pendulum model (LIPM) theory, which allows users to generate a natural gait pattern with desired step sizes. Five types of zero moment point (ZMP) components are proposed for formulating a natural ZMP reference, where ZMP moves continuously during single support phases instead of staying at a fixed point in the sagittal and lateral plane. The corresponding center of mass (CoM) trajectories for these components are derived by LIPM theory. To generate a parameterized gait pattern with user-defined parameters, a gait planning algorithm is proposed, which determines related coefficients and boundary conditions of the CoM trajectory for each step. The proposed parameterized gait generator also provides a concept for users to generate gait patterns with self-defined ZMP references by using different components. Finally, the feasibility of the proposed method is validated by the experimental results with a teen-sized humanoid robot, David, which won first place in the sprint event at the 20th Federation of International Robot-soccer Association (FIRA) RoboWorld Cup.

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    Acknowledgments

    • This work was supported in part by the Ministry of Science and Technology, Taiwan, ROC, under Grants MOST 103-2221-E-006-252 and MOST 104-2221-E-006-228-MY2, and in part by the Ministry of Education, Taiwan, within the Aim for the Top University Project through National Cheng Kung University, Tainan, Taiwan.

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    Ya-Fang Ho, Tzuu-Hseng S. Li, Ping-Huan Kuo, Yan-Ting Ye. 2017. Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000138
    Ya-Fang Ho, Tzuu-Hseng S. Li, Ping-Huan Kuo, Yan-Ting Ye. 2017. Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000138
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