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

Published online by Cambridge University Press:  09 September 2016

Ya-Fang Ho
Affiliation:
Department of Electrical Engineering, aiRobots Laboratory, National Cheng Kung University, Tainan 70101, Taiwan e-mail: makinosakuya@hotmail.com, thsli@mail.ncku.edu.tw, coll22000@hotmail.com, s1020jason@gmail.com
Tzuu-Hseng S. Li
Affiliation:
Department of Electrical Engineering, aiRobots Laboratory, National Cheng Kung University, Tainan 70101, Taiwan e-mail: makinosakuya@hotmail.com, thsli@mail.ncku.edu.tw, coll22000@hotmail.com, s1020jason@gmail.com
Ping-Huan Kuo
Affiliation:
Department of Electrical Engineering, aiRobots Laboratory, National Cheng Kung University, Tainan 70101, Taiwan e-mail: makinosakuya@hotmail.com, thsli@mail.ncku.edu.tw, coll22000@hotmail.com, s1020jason@gmail.com
Yan-Ting Ye
Affiliation:
Department of Electrical Engineering, aiRobots Laboratory, National Cheng Kung University, Tainan 70101, Taiwan e-mail: makinosakuya@hotmail.com, thsli@mail.ncku.edu.tw, coll22000@hotmail.com, s1020jason@gmail.com

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.

Type
Review Article
Copyright
© Cambridge University Press, 2017 

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References

Choi, Y., You, B. J. & Oh, S. R. 2004. On the stability of indirect ZMP controller for biped robot systems. In Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2, 1966–1971.Google Scholar
Endo, G., Morimoto, J., Matsubara, T., Nakanishi, J. & Cheng, G. 2008. Learning CPG-based biped locomotion with a policy gradient method: application to a humanoid robot. The International Journal of Robotics Research 27(2), 213228.CrossRefGoogle Scholar
Erbatur, K. & Kurt, O. 2009. Natural ZMP trajectories for biped robot reference generation. IEEE Transactions on Industrial Electronics 56(3), 835845.CrossRefGoogle Scholar
Farzaneh, Y., Akbarzadeh, A. & Akbari, A. A. 2014. Online bio-inspired trajectory generation of seven-link biped robot based on T–S fuzzy system. Applied Soft Computing 14, 167180.CrossRefGoogle Scholar
Ferreira, J. P., Crisóstomo, M. & Coimbra, A. P. 2011. Sagittal stability PD controllers for a biped robot using a neurofuzzy network and an SVR. Robotica 29(5), 717731.CrossRefGoogle Scholar
Hu, L., Zhou, C. & Sun, Z. 2008. Estimating biped gait using spline-based probability distribution function with Q-learning. IEEE Transactions on Industrial Electronics 55(3), 14441452.Google Scholar
Kajita, S., Kanehiro, F., Kaneko, K., Yokoi, K. & Hirukawa, H. 2001. The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation. In Proceedings of 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1, 239–246.Google Scholar
Kajita, S., Morisawa, M., Harada, K., Kaneko, K., Kanehiro, F., Fujiwara, K. & Hirukawa, H. 2006. Biped walking pattern generator allowing auxiliary ZMP control. In Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2993–2999.Google Scholar
Kajita, S., Nagasaki, T., Kaneko, K. & Hirukawa, H. 2007. ZMP-based biped running control. IEEE Robotics & Automation Magazine 2(14), 6372.CrossRefGoogle Scholar
Kim, D., Seo, S. J. & Park, G. T. 2005. Zero-moment point trajectory modelling of a biped walking robot using an adaptive neuro-fuzzy system. IEE Proceedings – Control Theory and Applications 152(4), 411426.CrossRefGoogle Scholar
Li, T. H. S., Kuo, P. H., Ho, Y. F., Kao, M. C. & Tai, L. H. 2015. A biped gait learning algorithm for humanoid robots based on environmental impact assessed artificial bee colony. IEEE Access 3, 1326.CrossRefGoogle Scholar
Li, T. H. S., Su, Y. T., Lai, S. W. & Hu, J. J. 2011. Walking motion generation, synthesis, and control for biped robot by using PGRL, LPI, and fuzzy logic. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41(3), 736748.CrossRefGoogle ScholarPubMed
Liu, C., Wang, D. & Chen, Q. 2013. Central pattern generator inspired control for adaptive walking of biped robots. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(5), 12061215.Google Scholar
Michel, O. 2004. WebotsTM: professional mobile robot simulation. International Journal of Advanced Robotic Systems 1(1), 3942.CrossRefGoogle Scholar
Nassour, J., Hugel, V., Ouezdou, F. B. & Cheng, G. 2013. Qualitative adaptive reward learning with success failure maps: applied to humanoid robot walking. IEEE Transactions on Neural Networks and Learning Systems 24(1), 8193.CrossRefGoogle ScholarPubMed
Park, K.-H., Jo, J. & Kim, J.-H. 2004. Stabilization of biped robot based on two mode Q-learning. In Proceedings of the 2nd International Conference on Autonomous Robots and Agents, 446–451.Google Scholar
Shin, H. K. & Kim, B. K. 2014. Energy-efficient gait planning and control for biped robots utilizing the allowable ZMP region. IEEE Transactions on Robotics 30(4), 986993.CrossRefGoogle Scholar
Su, Y. T., Chong, K. Y. & Li, T. H. S. 2011. Design and implementation of fuzzy policy gradient gait learning method for walking pattern generation of humanoid robots. International Journal of Fuzzy Systems 13(4), 369382.Google Scholar
Taskiran, E., Yilmaz, M., Koca, O., Seven, U. & Erbatur, K. 2010. Trajectory generation with natural ZMP references for the biped walking robot SURALP. In Proceedings of 2010 IEEE International Conference on Robotics and Automation (ICRA), 4237–4242.Google Scholar
Tedrake, R., Zhang, T. W. & Seung, H. S. 2004. Stochastic policy gradient reinforcement learning on a simple 3D biped. In Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3, 2849–2854.Google Scholar
Vukobratović, M. & Stepanenko, J. 1972. On the stability of anthropomorphic systems. Mathematical Biosciences 15(1), 137.CrossRefGoogle Scholar

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