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

  • Ya-Fang Ho (a1), Tzuu-Hseng S. Li (a1), Ping-Huan Kuo (a1) and Yan-Ting Ye (a1)

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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References

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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.
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.
Erbatur, K. & Kurt, O. 2009. Natural ZMP trajectories for biped robot reference generation. IEEE Transactions on Industrial Electronics 56(3), 835845.
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.
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.
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.
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.
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.
Kajita, S., Nagasaki, T., Kaneko, K. & Hirukawa, H. 2007. ZMP-based biped running control. IEEE Robotics & Automation Magazine 2(14), 6372.
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.
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.
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.
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.
Michel, O. 2004. WebotsTM: professional mobile robot simulation. International Journal of Advanced Robotic Systems 1(1), 3942.
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.
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.
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.
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.
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.
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.
Vukobratović, M. & Stepanenko, J. 1972. On the stability of anthropomorphic systems. Mathematical Biosciences 15(1), 137.
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