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Sagittal stability PD controllers for a biped robot using a neurofuzzy network and an SVR

Published online by Cambridge University Press:  12 October 2010

João P. Ferreira ,
Manuel Crisóstomo  and
A. Paulo Coimbra
João P. Ferreira*
Affiliation:
Department of Electrical Engineering, Superior Institute of Engineering of Coimbra, Rua Pedro Nunes-Quinta da Nora, Coimbra 3030-199, Portugal Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Polo 2-Pinhal de Marrocos, Coimbra 3030-290, Portugal. E-mail: mcris@isr.uc.pt, acoimbra@deec.uc.pt
Manuel Crisóstomo
Affiliation:
Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Polo 2-Pinhal de Marrocos, Coimbra 3030-290, Portugal. E-mail: mcris@isr.uc.pt, acoimbra@deec.uc.pt
A. Paulo Coimbra
Affiliation:
Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Polo 2-Pinhal de Marrocos, Coimbra 3030-290, Portugal. E-mail: mcris@isr.uc.pt, acoimbra@deec.uc.pt
*
*Corresponding author. E-mail: ferreira@isec.pt
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Summary

The real-time balance PD control of an eight-link biped robot using a zero-moment point (ZMP) dynamic model is implemented using two alternative intelligent computing control techniques that were compared: one based on support vector regression (SVR) and another based on a first order Takagi–Sugeno–Kang (TSK) -type neural-fuzzy (NF). Both methods use the ZMP error, and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data, and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods.

The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is based on ankle and hip human trajectories that were acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs at 0.2 ms, about 50 times faster than the NF controller and much faster than a controller based on full ZMP dynamic model equations.

Keywords

Neural-fuzzy (NF) networksSupport vector regression (SVR)Biped robotBalanceZero-moment point (ZMP)
Type
Articles
Information
Robotica , Volume 29 , Issue 5 , September 2011 , pp. 717 - 731
Copyright
Copyright © Cambridge University Press 2010

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References

1.Honda Worldwide page. [Online]. Available http://world.honda.com/ASIMO/new/ (18/3/2008).Google Scholar
2.The Humanoid robot research center at KAIST. [Online]. Available http://hubolab.kaist.ac.kr/KHR-3.php (18/3/2008).Google Scholar
3.Biped Humanoid Robot Group – WASEDA. [Online]. Available http://www.takanishi.mech.waseda.ac.jp/research/wabian/index.htm (18/3/2008).Google Scholar
4.Sony page. [Online]. Available http://www.sony.net/SonyInfo/QRIO/top_nf.html (18/3/2008).Google Scholar
5.Vukobratović, M., Borovac, B., Surla, D. and Stokic, D., Biped locomotion: Dynamics, Stability, Control and Application (Springer-Verlag, Berlin, 1990).CrossRefGoogle Scholar
6.Nakamura, M., Mori, M. and Nishii, J., “Trajectory Planning for a Leg Swing During Human Walking,” 2004 IEEE International Conference on Systems, Man and Cybernetics, Vol. 1 (Oct. 10–13, 2004), pp. 784–790.Google Scholar
7.Yoo, J.-H., Nixon, M. S. and Harris, C. J., “Extracting Human Gait Signatures by Body Segment Properties,” Fifth IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI'02) (Apr. 7–9, 2002) pp. 35–39.Google Scholar
8.Ferreira, J. P., Crisóstomo, M. M., Coimbra, A. P., Carnide, D. and Marto, A., “A Human Gait Analyzer,” 2007 IEEE International Symposium on Intelligent Signal Processing—(WISP'07), Madrid, Spain (Oct. 3–5, 2007) pp. 1–5.Google Scholar
9.Winter, D. A., The Biomechanics and Motor Control of Human Movement, 2nd ed. (John Wiley & Sons, 1990).Google Scholar
10.Hirai, K., Hirose, M., Haikawa, Y. and Takenaka, T., “The Development of Honda Humanoid Robot,” Proceedings of the International Conference on Robotics and Automation (1998) pp. 1321–1326.Google Scholar
11.Huang, Q., Kajita, S., Kaneko, K., Yokoi, K., Komoriya, K. and Tanie, K., “A High Stability, Smooth Walking Pattern for a Biped Robot,” Proceedings of the International Conference on Robotics and Automation (1999) pp. 65–71.Google Scholar
12.Park, I. W., Kim, J. Y., Lee, J. and Oh, J. H., “Online Free Walking Trajectory Generation for Biped Humanoid Robot KHR-3(HUBO),” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida (May 15–19, 2006) pp. 1231–1236.Google Scholar
13.Kim, J. Y., Lee, J. and Oh, J. H., “Experimental realization of dynamic walking for a human-riding biped robot, HUBO FX-1,” Adv. Robot. 21 (3–4), 461–484 (2007).CrossRefGoogle Scholar
14.Prahlad, V., Dip, G. and Hwee, C. M., “Disturbance Rejection by Online ZMP Compensation,” In: Robotica (Cambridge University Press, 2007) pp. 1–9.Google Scholar
15.Low, K. H., Liu, X., Goh, C. H. and Yu, H., “Locomotive control of a wearable lower exoskeleton for walking enhancement,” J. Vib. Control 12 (12), 1311–1336 (2006).CrossRefGoogle Scholar
16.Park, J. H., “Fuzzy-logic zero-moment point trajectory generation for reduced trunk motions of biped robots,” Fuzzy Sets Syst. 134, 189–203 (2003).CrossRefGoogle Scholar
17.Negoita, M. Gh., Kim, D., Kim, N.-H., Seo, S.-J. and Park, G.-T., “Fuzzy Modeling of Zero Moment Point Trajectory for a Biped Walking Robot,” International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2004) (Lecture Notes in Artificial Intelligence, Vol. 3214), Springer-Verlag, Berlin, Heidelberg (2004) pp. 716–722.Google Scholar
18.Choi, K. C., Lee, M. C. and Lee, J. M., “Fuzzy Posture Control for a Biped Walking Robot Based on Force Sensor for ZMP,” The 11th International Symposium on Artificial Life and robotics 2006 (ISAROB '06), Oita, Japan (Jan. 23–25, 2006) pp. 1185–1189.Google Scholar
19.Ferreira, J. P., Amaral, T. G., Pires, V. F., Crisóstomo, M. M. and Coimbra, A. P., “A Neural-Fuzzy Walking Control of An Autonomous Biped Robot,” Proceedings of the 10th International Symposium on Robotics with Applications, IEEE CNF, Seville, Vol. 15 (June 21–23, 2004) pp. 253–258.Google Scholar
20.Sim, D., Seo, J. and Park, G. T., “Zero moment point trajectory modeling of a biped walking robot using an adaptive neuro-fuzzy system,” IEE Proc. Control Theory Appl. 152 (4)411–426 (July 2005).Google Scholar
21.Behnke, S., “Online trajectory Generation for Omnidirectional Biped Walking,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida (May 2006) pp. 1597–1603.Google Scholar
22.Katić, D. and Vukobratović, M., “Survey of Intelligent Control Algorithms For Humanoid Robots,” Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, Vol. 16 (July 2005), ISBN: 978-0-08-045108-4, Elsevier Science (27 June 2006).Google Scholar
23.Park, K. H., Jo, J. and Kim, J. H., “Stabilization of a Biped Robot Based on Two Mode Q-learning,” 2nd International Conference on Autonomous Robots and Agents, Palmerston North, New Zealand (Dec. 13–15, 2004) pp. 446–451.Google Scholar
24.Vapnik, V., The Nature of Statistical Learning Theory (Springer, New York, 1998).Google Scholar
25.Ferreira, J. P., Crisóstomo, M. M., Coimbra, A. P. and Ribeiro, B., “Simulation Control of a Biped Robot with Support Vector Regression,” 2007 IEEE International Symposium on Intelligent Signal Processing—(WISP'07), Madrid, Espanha (Out. 3–5, 2007) pp. 1–6.Google Scholar
26.Jang, J. S. R., “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern. 23 (3), 665–685 (May/June 1993).CrossRefGoogle Scholar
27.Mohamed, R. M. and Farag, A. A., “Classification of Multispectral Data Using Support Vector Machines Approach for Density Estimation,” IEEE Seventh International Conference on Intelligent Engineering Systems, INES03, Assiut, Egypt (March 2003).Google Scholar
28.Vapnik, V., Golowich, S. and Smola, A., Support Vector Method for Multivariate Density Estimation, Advances in Neural Information Processing Systems, Vol. 12 (MIT Press, April 1999) pp. 659–665.Google Scholar
29.Chang, C.-C. and Lin, C.-J., “LIBSVM: A Library for Support Vector Machines,” January 2, 2007.Google Scholar
30.Ziegler, J. G. and Nichols, N. B., “Optimum settings for automatic controllers”, Trans. ASME 64, 759–768 (Nov. 1942).Google Scholar
31.Katić, D. M., Rodić, A. D. and Vukobratović, M. K., “Hybrid dynamic control algorithm for humanoid robots based on reinforcement learning,” J. Intell. Robot. Syst., 51 (1), 3–30 (Jan. 2008).CrossRefGoogle Scholar
32.Nakamura, Y., Mori, T., Sato, M.-A. and Ishii, S., “Reinforcement learning for a biped robot based on a CPG-actor-critic method”, Neural Netw., 20 (6), 723–735 (Aug. 2007).CrossRefGoogle ScholarPubMed
33.Morimoto, J., Nakanishi, J., Endo, G., Cheng, G., Atkeson, C. G. and Zeglin, G., “Poincaré-Map-Based Reinforcement Learning For Biped Walking,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA '05) (Apr. 18–22, 2005) pp. 2381–2386.Google Scholar
34.Hitomi, K., Shibata, T., Nakamura, Y. and Ishii, S., “Reinforcement learning for quasi-passive dynamic walking of an unstable biped robot,” Robot. Autonom. Syst., 54 (12), 982–988 (Dec. 31 2006).CrossRefGoogle Scholar
35.Schuitema, E., Hobbelen, D. G. E., Jonker, P. P., Wisse, M. and Karssen, J. G. D., “Using a Controller Based on Reinforcement Learning for a Passive Dynamic Walking Robot,” 5th IEEE-RAS International Conference on Humanoid Robots (Dec. 5, 2005) pp. 232–237.Google Scholar
36.Hyon, S. e Cheng, G., “Disturbance Rejection for Biped Humanoids,” IEEE International Conference on Robotics and Automation, Roma, Italy (Apr. 10–14, 2007) pp. 2668–2675.Google Scholar
37.Kajita, S., Nagasaki, T., Kaneko, K. and Hirukawa, H., “ZMP-based biped running control,” IEEE Robot. Autom. Magaz. (Disturbance Rejection for Biped Humanoids) 14 (2), 63–72 (June 2007).CrossRefGoogle Scholar
38.Stasse, O., Escande, A., Mansard, N., Miossec, S., Evrard, P. and Kheddar, A. “Real-Time (Self)-Collision Avoidance Task on a HRP-2 Humanoid Robot,” IEEE International Conference on Robotics and Automation, Pasadena, CA, USA (May 19–23, 2008) pp. 3200–3205.Google Scholar
39.Heinen, M. R. and Osório, F. S., “Gait Control Generation for Physically Based Simulated Robots Using Genetic Algorithms”, Lecture Notes in Computer Science (Advances in Artificial Intelligence), Springer-Verlag, Berlin, Heidelberg (Oct. 2006) pp. 562–571.Google Scholar
40.Dip, G., Prahlad, V. and Kien, P. D., “Genetic algorithm-based optimal bipedal walking gait synthesis considering tradeoff between stability margin and speed,” Robotica (Cambridge University Press, 2008) pp. 1–11.Google Scholar
41.Yoo, S. J., Park, J. B. and Choi, Y. H., “Robust control of planar biped robots in single support phase using intelligent adaptive backstepping technique,” Int. J. Control Autom. Syst. 5 (3), 269–282 (2007).Google Scholar
42.Lee, S. H., Park, J. B. and Choi, Y. H., “Sliding Mode Control Based on Self-Recurrent Wavelet Neural Network for Five-link Biped Robot,” SICE-ICASE International Joint Conference, Busan (Oct. 18–21, 2006) pp. 726–731.Google Scholar
43.Or, J. and Takanishi, A., “A Biologically Inspired CPG-ZMP Control System for the Real-time Balance of a Single-Legged Belly Dancing Robot,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (Sep. 28–Oct. 2, 2004) pp. 931–936.Google Scholar
44.Ferreira, J. P., Crisóstomo, M. and Coimbra, A. P., “Rejection of an External Force in the Sagittal Plane Applied on a Biped Robot using a Neuro-Fuzzy Controller,” IEEE International Conference on Advanced Robotics –(ICAR 2009), Munich, Germany (June 22–26, 2009) pp. 1–6.Google Scholar
45.Ferreira, J. P., Crisóstomo, M. and Coimbra, A. P., “Human gait acquisition and characterization,” IEEE Trans. Instrum. Meas. 58 (9), 2979–2988 (Sep. 2009).CrossRefGoogle Scholar