Hostname: page-component-745bb68f8f-v2bm5 Total loading time: 0 Render date: 2025-01-12T11:26:27.693Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic algorithm-based optimal bipedal walking gait synthesis considering tradeoff between stability margin and speed

Published online by Cambridge University Press:  01 May 2009

Goswami Dip ,
Vadakkepat Prahlad  and
Phung Duc Kien
Goswami Dip
Affiliation:
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore117576.
Vadakkepat Prahlad*
Affiliation:
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore117576.
Phung Duc Kien
Affiliation:
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore117576.
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The inverse kinematics of a 12 degrees-of-freedom (DOFs) biped robot is formulated in terms of certain parameters. The biped walking gaits are developed using the parameters. The walking gaits are optimized using genetic algorithm (GA). The optimization is carried out considering relative importance of stability margin and walking speed. The stability margin depends on the position of zero-moment-point (ZMP) while walking speed varies with step-size. The ZMP is computed by an approximation-based method which does not require system dynamics. The optimal walking gaits are experimentally realized on a biped robot.

Keywords

Biped robotInverse kinematicsZero-moment-pointStability marginWalking speedGenetic algorithm
Type
Article
Information
Robotica , Volume 27 , Issue 3 , May 2009 , pp. 355 - 365
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Vukobratovié, M., Borovac, B., Surla, D. and Stokié, D., Biped Locomotion: Dynamics, Stability, Control and Application (Springer-Verlag, Berlin, 1990).CrossRefGoogle Scholar
2.Yamasaki, F., Hosoda, K. and Asada, M., “An Energy Consumption Based Control for Humanoid Walking,” IEEE/RS J International Conference on Intelligent Robots and System (IROS) (Switzerland, Sept. 30–Oct. 5, 2002) Vol. 3, pp. 2473–2477.Google Scholar
3.Ono, K., Takahashi, R., Imadu, A. and Shimada, T., “Self-Excitation Control for Biped Walking Mechanism,” IROS (Japan, Oct. 31–Nov. 5, 2000) Vol. 2, pp. 1143–1148.Google Scholar
4.Kyung-Kon, Noh, Jin-Geol, Kim and Uk-Youl, Huh, “Stability Experiment of a Biped Walking Robot with Inverted Pendulum,” 30th IEEE Annual Conference of Industrial Electronics Society (USA, Nov. 2–6, 2004) Vol. 3, pp. 2475–2479.Google Scholar
5.Hun-ok, Lim, Yamamoto, Y. and Takanishi, A., “Control to Realize Human-Like Walking of a Biped Humanoid Robot,” IEEE International Conference on Systems, Man, and Cybernetics (USA, Oct. 8–11, 2000) Vol. 5, pp. 3271–3276.CrossRefGoogle Scholar
6.Xiuping, Mu and Qiong, Wu, “Synthesis of a complete sagittal gait cycle for a five-link biped robot,” Robotica 21 (05), 581–587 (Oct. 2003).Google Scholar
7.Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K. and Hirukawa, H., “Biped Walking Pattern Generation by Using Preview Control of Zero-Moment Point,” IEEE International Conference on Robotics and Automation (ICRA) (Taipei, Sept. 14–19, 2003) Vol. 2, pp. 1620–1626.Google Scholar
8.Prahlad, Vadakkepat, Goswami, Dip and Hwee, Chia Meng, “Disturbance rejection by online ZMP compensation,” Robotica 26, 9–17, (2007).Google Scholar
9.Sugihara, T., Nakamura, Y. and Inoue, H., “Real-time Humanoid Motion Generation Through ZMP Manipulation Based on Inverted Pendulum Control,” ICRA (USA, May 11–15, 2002) Vol. 2, pp. 1404–1409.Google Scholar
10.Kitamura, S., Kurematsu, Y. and Iwata, M., “Motion Generation of a Biped Locomotive Robot Using an Inverted Pendulum Model and Neural Networks,” IEEE Conference on Decision and Control (USA, Dec. 5–7, 1990) Vol. 6, pp. 3308–3312.CrossRefGoogle Scholar
11.Salatian, A. W. and Zheng, Y. F., “Gait Synthesis for a Biped Robot Climbing Sloping Surfaces Using Neural Networks. II. Dynamic learning,” ICRA (France, May 12–14, 1992) Vol. 3, pp. 2607–2611.Google Scholar
12.Cao, M. and Kawamura, A., “A Design Method of Neural Oscillatory Networks for Generation of Humanoid Biped Walking Patterns,” ICRA (Belgium, May 16–20, 1998) Vol. 3, pp. 2357–2362.Google Scholar
13.Dasgupta, A. and Nakamura, Y., “Making Feasible Walking Motion of Humanoid Robots from Human Motion Capture Data,” ICRA (USA, May 10–15, 1999) Vol. 2, pp. 1044–1049.Google Scholar
14.Choi, Sang-Ho, Choi, Young-Ha and Kim, Jin-Geol, “Optimal Walking Trajectory Generation for a Biped Robot Using Genetic Algorithm,” IROS (South Korea, Oct. 17–21, 1999) Vol. 3, pp. 1456–1461.Google Scholar
15.Cheng, M. Y. and Lin, C. S., “Genetic Algorithm for Control Design of Biped Locomotion Systems,” IEEE International Conference on Systems, Man and Cybernetics (Canada, Oct. 22–25, 1995) Vol. 3, pp. 1456–1461.Google Scholar
16.Kong, Jung-Shik, Lee, Bo-Hee and Kim, Jin-Geol, “A Study on the Gait Generation of a Humanoid Robot Using Genetic Algorithm,” SICE 2004 Annual Conference (Japan, Aug. 4–6, 2004) Vol. 1, pp. 187–191.Google Scholar
17.Arakawa, F. and Fukuda, T., “Natural Motion Trajectory Generation of Biped Locomotion Robot Using Genetic Algorithm Through Energy Optimization,” IEEE International Conference on Systems, Man, and Cybernetics (China, Oct. 14–17, 1996) Vol. 2, pp. 1495–1500.CrossRefGoogle Scholar
18.John, J. Craig, Introduction to Robotics: Mechanics and Control, 2nd ed. (Addison-Wesley Longman Publishing Co., Boston, MA, 1989).Google Scholar
19.Tang, Z., Zhou, C. and Sun, Z., “Humanoid Walking Gait Optimization Using GA-Based Neural Network,” International Conference on Natural Computation (2) (ICNC(2)) (China, 2005) pp. 252–261.Google Scholar
20.Sakagami, Y., Watanabec, R., Aoyama, C., Matsunaga, S. and Higaki, N., “The Intelligent ASIMO: System Overview and Integration,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Switzerland (2002) pp. 2478–2483.Google Scholar
21.Righetti, L. and Ijspeert, A. J., “Programmable Central Pattern Generators: An Application to Biped Locomotion Control,” IEEE International Conference on Robotics and Automation (ICRA06), Orlando (2006) pp. 1585–1590.Google Scholar
22.Manoonpong, P., Geng, T., Kulvicius, T., Porr, B. and Wrgtter, F., “Adaptive, fast walking in a biped robot under neuronal control and learning,” PLoS Comput Biol 3 (7), e134 (2007).CrossRefGoogle Scholar
23.Capaday, C., “The special nature of human walking and its neural control,” Trends Neurosci 25, 370–376 (2002).Google Scholar
24.Duysens, J. and de Crommert, V., “Neural control of locomotion. Part 1: The central pattern generator from cats to humans,” Gait Posture 7, 131–141 (1998).CrossRefGoogle ScholarPubMed
25.Mc Geer, T., “Passive dynamic walking,” Int. J. Rob. Res. 9 (2), 62–82 (1990).CrossRefGoogle Scholar
26.Collins, S. H., Ruina, A. L., Tedrake, R. and Wisse, M., “Efficient bipedal robots based on passive-dynamic walkers,” Science 307, 1082–1085 (2005).CrossRefGoogle ScholarPubMed
27.Ijspeert, A. J., Crespi, A., Ryczko, D. and Cabelguen, J. M., “From swimming to walking with a salamander robot driven by a spinal cord model,” Science 315 (5817), 1416–1420 (2007).Google Scholar
28.Kimura, H., Fukuoka, Y. and Cohen, A. H., “Adaptive dynamic walking of a quadruped robot on natural ground based on biological concepts,” Int. J. Rob. Res. 26 (5), 475–490 (2007).CrossRefGoogle Scholar
29.Manoonpong, P., Pasemann, F. and Roth, H., “Modular reactive neurocontrol for biologically-inspired walking machines,” Int. J. Rob. Res. 26 (3), 301–331 (2007).CrossRefGoogle Scholar
30.Beer, R. D., Quinn, R. D., Chiel, H. J. and Ritzmann, R. E., “Biologically inspired approaches to robotics: What can we learn from insects?,” Commun. ACM 40 (3), 30–38 (1997).Google Scholar
31.Geng, T., Porr, B. and Wrgtter, F., “Fast biped walking with a sensor-driven neuronal controller and real-time online learning,” Int. J. Rob. Res. 25 (3), 243–259 (2006).Google Scholar