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A uniform biped gait generator with offline optimization and online adjustable parameters

Published online by Cambridge University Press:  01 September 2007

Lin Yang ,
Chee-Meng Chew ,
Teresa Zielinska  and
Aun-Neow Poo
Lin Yang*
Affiliation:
Control and Mechatronics Lab, Department of Mechanical Engineering, The National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
Chee-Meng Chew
Affiliation:
Control and Mechatronics Lab, Department of Mechanical Engineering, The National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
Teresa Zielinska
Affiliation:
Warsaw University of Technology, Institute for Aircraft Engineering and Applied Mechanics, 00-665 Warsaw, Poland
Aun-Neow Poo
Affiliation:
Control and Mechatronics Lab, Department of Mechanical Engineering, The National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
*
*Corresponding author. Email: yanglin@nus.edu.sg
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Summary

This paper presents the Genetic Algorithm Optimized Fourier Series Formulation (GAOFSF) method for stable gait generation in bipedal locomotion. It uses a Truncated Fourier Series (TFS) formulation with its coefficients determined and optimized by Genetic Algorithm. The GAOFSF method can generate human-like stable gaits for walking on flat terrains as well as on slopes in a uniform way. Through the adjustment of only a single or two parameters, the step length and stride-frequency can easily be adjusted online, and slopes of different gradients are accommodated. Dynamic simulations show the robustness of the GAOFSF, with stable gaits achieved even if the step length and stride frequency are adjusted by significant amounts. With its ease of adjustments to accommodate different gait requirements, the approach lends itself readily for control of walking on a rough terrain and in the presence of external perturbations.

Keywords

Truncated Fourier SeriesGenetic algorithmBipedGait generation and adjustment

Information

Type
Article
Information
Robotica , Volume 25 , Issue 5 , September 2007 , pp. 549 - 565
Copyright
Copyright © Cambridge University Press 2007

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References

1.Atienza, R. O. and M. Ang, Jr. M. Ang, Jr. “A flexible control architecture for mobile robots: an application for a walking robot,” J. Intell. Robot. Syst. 30, 29–48 2001.CrossRefGoogle Scholar
2.Chew, C. M. and Pratt, G. A., “Dynamic bipedal walking assisted by learning,” Robotica 20, 477–491 2002.CrossRefGoogle Scholar
3.Kajita, S., Matsumoto, O. and Saigo, M., “Real-Time 3D Walking Pattern Generation for a Biped Robot With Telescopic Legs,” Proceedings of the IEEE International Conference on Robotics and Automation, 3, 2299–2306, Seoul, Korea May 21–26, 2001.Google Scholar
4.Hirukawa, H., Kajita, S., Kanehiro, F., Kaneko, K. and Isozumi, T., “The human-size humanoid robot that can walk, lie down and get up,” Int. J. Robot. Res. 24 (9), 755–769 2005CrossRefGoogle Scholar
5.Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S. and Kawato, M., “Learning from demonstration and adaptation of biped locomotion,” Robot. Autonom. Syst. 47, 79–91 2004.CrossRefGoogle Scholar
6.Williamson, M. M., “Neural control of rhythmic arm movements,” Neural Netw. 11, 1379–1394 1998.CrossRefGoogle ScholarPubMed
7.Dutra, M. S., Filho, A. C. de P. and Romano, V. F., “Modeling of a bipedal locomotor using coupled nonlinear oscillators of Van der Pol,” Biol. Cybern. 88 (4), 286–292 2004.CrossRefGoogle Scholar
8.Fukuoka, Y., Kimura, H. and Cohen, A. H., “Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts,” Int. J. Robot. Res. 22 (3–4), 187–202 Mar.–Apr. 2003.CrossRefGoogle Scholar
9.Miyakoshi, S., Yamakita, M. and Furuta, K., “Juggling Control Using Neural Oscillators,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 1994 pp. 1186–1193.Google Scholar
10.Kotosaka, S. and Schaal, S., “Synchronized Robot Drumming by Neural Oscillator,” Journal of the Robotics Society of Japan 19 (1), 116–123 2001.CrossRefGoogle Scholar
11.Khoukhi, A., “Neural based RSPN multi-agent strategy for biped motion control,” Robotica 19, 611–617 2001.CrossRefGoogle Scholar
12.Taga, G., Yamaguchi, Y. and Shimizu, H., “Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment,” Biol. Cybern. 65, 147–159 1991.CrossRefGoogle ScholarPubMed
13.Matsuoka, K., “Sustaned oscillations generated by mutually inhibiting neurons with adaptation,” Biol. Cybern. 52, 367–376 1985.CrossRefGoogle Scholar
14.Vukobratovic, M., Borovac, B., Surla, D. and Stokic, D., Biped Locomotion—Dynamics, Stability, Control and Application (Springer-Verlag, London, UK, 1990).CrossRefGoogle Scholar
15.Sardain, P. and Bessonnet, G., “Forces acting on a biped robot. Center of pressure—zero moment point,” IEEE Trans. Syst., Man, Cybern.—part A: systems and humans 34 (5), 630–637 2004.CrossRefGoogle Scholar
16.Goswami, A., “Postural stability of biped robots and the foot-rotation indicator (FRI) point,” Int. J. Robot. Res. 18 (6), 523–533 1999.CrossRefGoogle Scholar
17.Lum, H. K., Zribi, M. and Soh, Y. C., “Planning and control of a biped robot,” Int. J. Eng. Sci. 37, 1319–1349 1999.CrossRefGoogle Scholar
18.Chevallereau, C. and Aoustin, Y., “Optimal reference trajectories for walking and running of a biped robot,” Robotica 19, 557–569 2001.CrossRefGoogle Scholar
19.Takanishi, A., Lim, H., Tsuda, M. and Kato, I., “Realization of Dynamic Biped Walking Stabilized by Trunk Motion on a Saggitally Uneven Surface,” Proceedings of the IEEE International Workshop on Intelligent Robots and Systems, Ibaraki, Japan 1990 pp. 323–330.Google Scholar
20.Ogura, Y. et al. , “A Novel Method of Biped Walking Pattern Generation With Predetermined Knee Joint Motion,” Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems 3, 2831–2836 2004.Google Scholar
21.Lim, H.-O. and Takanishi, A., “Compensatory motion control for a biped walking robot,” Robotica 23, 1–11 2005.CrossRefGoogle Scholar
22.Michalewicz, Z., “Genetic algorithms + data structures = evolution programs,” AI Series (Springer-Verlag, New York, 1994.Google Scholar
23.“Biomechanics of motion,” CISM Courses and Lectures 263, 79–129 1980.Google Scholar
24.Perry, MD, J., Gait Analysis: Normal and Pathological Function (McGraw Hill, New York, 1992 pp. 556.Google Scholar
25.Shih, C. L., “Gait synthesis for a biped robot,” Robotica 15, 599–607 1997.CrossRefGoogle Scholar
26.Park, J. H., “Fuzzy-logic zero-moment-point trajectory generation for reduced trunk motion of biped robots,” Fuzzy Sets Syst. 134, 189–203 2003.CrossRefGoogle Scholar
27.Arakawa, T. and Fukuda, T., “Natural Motion Generation of Biped Locomotion Robot Using Hierarchical Trajectory Generation,” Proceedings of the 1997 IEEE International Conference on Robotics and Automations 1997 pp. 211–216.Google Scholar
28.Mu, X. and Wu, Q., “Synthesis of a complete sagittal gait cycle for a five-link biped robot,” Robotica 21, 581–587 2003.CrossRefGoogle Scholar
29.Users Guide for Yobotics! (Yobotics, Inc., Boston, MA, 2000–2003.Google Scholar
30.Lalash, M. L., Control of Human Movement (Human Kinetics Publishers, USA, 1994.Google Scholar
31.Ogura, Y., Aikawa, H., Shimomura, K., Kondo, H., Morishima, A., Lim, H. and Takanish, A., “Development of a New Humanoid Robot to Realize Various Walking Pattern Using Waist Motions,” Springer CISM Courses and Lectures: Robotic Design, Dynamics, and Control 487, 279–286. Warsaw, Poland 2006.CrossRefGoogle Scholar
32.Nagasaki, T., Kajita, S., Yokoi, K., Kaneko, K. and Tanie, K., “Running Pattern Generation and Its Evaluation Using a Realistic Humanoid Model,” Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipet, Taiwan 2003 pp. 14–19.Google Scholar
33.Poo, A. N., Ang, Jr, M. H., Teo, C. L. and Li, Q., “Performance of a neuro-model-based robot controller: adaptability and noise rejection,” Intell. Syst. Eng. 1 (1), 50–62 1992.CrossRefGoogle Scholar