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Footstep adaptation strategy for reactive omnidirectional walking in humanoid robots

Published online by Cambridge University Press:  12 April 2017

Jiwen Zhang ,
Zeyang Xia ,
Li Liu  and
Ken Chen
Jiwen Zhang
Affiliation:
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China. E-mails: jwzhang@mail.tsinghua.edu.cn, liuli@tsinghua.edu.cn, kenchen@tsinghua.edu.cn Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
Zeyang Xia
Affiliation:
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. E-mail: zeyang.xia@ieee.org
Li Liu
Affiliation:
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China. E-mails: jwzhang@mail.tsinghua.edu.cn, liuli@tsinghua.edu.cn, kenchen@tsinghua.edu.cn Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
Ken Chen*
Affiliation:
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China. E-mails: jwzhang@mail.tsinghua.edu.cn, liuli@tsinghua.edu.cn, kenchen@tsinghua.edu.cn Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
*
*Corresponding author. Email: kenchen@tsinghua.edu.cn
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Summary

Stability, high response quality and rapidity are required for reactive omnidirectional walking in humanoids. Early schemes focused on generating gaits for predefined footstep locations and suffered from the risk of falling over because they lacked the ability to suitably adapt foot placement. Later methods combining stride adaptation and center of mass (COM) trajectory modification experienced difficulties related to increasing computing loads and an unwanted bias from the desired commands. In this paper, a hierarchical planning framework is proposed in which the footstep adaption task is separated from that of COM trajectory generation. A novel omnidirectional vehicle model and the inequalities deduced therefrom are adopted to describe the inter-pace connection relationship. A constrained nonlinear optimization problem is formulated and solved based on these inequalities to generate the optimal strides. A black-box optimization problem is then constructed and solved to determine the model constants using a surrogate-model-based approach. A simulation-based verification of the method and its implementation on a physical robot with a strictly limited computing capacity are reported. The proposed method is found to offer improved response quality while maintaining rapidity and stability, to reduce the online computing load required for reactive walking and to eliminate unnecessary bias from walking intentions.

Keywords

Humanoid robotsOmnidirectionalReactive walkingInverted pendulum modelNonlinear optimizationSurrogate model optimization
Type
Articles
Information
Robotica , Volume 36 , Issue 1 , January 2018 , pp. 57 - 77
Copyright
Copyright © Cambridge University Press 2017 

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References

1. Chestnutt, J. et al. “An Intelligent Joystick for Biped Control,” Proceedings of the IEEE International Conference on Robotics and Automation, Piscataway, NJ (May 15–19, 2006) pp. 860–865.Google Scholar
2. Dune, C. et al., “Cancelling the Sway Motion of Dynamic Walking in Visual Servoing,” Proceedings of IEEE International Conference on Intelligent Robots and Systems, Piscataway, NJ (Oct. 18–22, 2010) pp. 3175–3180.CrossRefGoogle Scholar
3. Kajita, S. et al., “The 3D Linear Inverted Pendulum Mode: A Simple Modeling for a Biped Walking Pattern Generation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ, Vol. 1 (Oct. 29–Nov. 03, 2001) pp. 239–246.Google Scholar
4. Kajita, S. et al. “Biped Walking Pattern Generation by Using Preview Control of Zero-Moment Point,” Proceedings of IEEE International Conference on Robotics and Automation, Piscataway, NJ, Vol. 2 (Sep. 14–19, 2003) pp. 1620–1626.Google Scholar
5. Czarnetzki, S., Kerner, S. and Urbann, O., “Observer-based dynamic walking control for biped robots,” Robot. Auton. Syst. 57, 839–845 (2009).Google Scholar
6. Zeyang, X., Jing, X. and Ken, C., “Global navigation for humanoid robots using sampling-based footstep planners,” IEEE/ASME Trans. Mechatronics 16, 716–723 (2011).Google Scholar
7. Zeyang, X., Jing, X. and Ken, C., “Parameter self-adaptation in biped navigation employing nonuniform randomized footstep planner,” Robotica 28, 929–936 (2010).Google Scholar
8. Morisawa, M. et al., “Motion Planning of Emergency Stop for Humanoid Robot by State Space Approach,” Proceedings of IEEE International Conference on Intelligent Robots and Systems, Piscataway, NJ (Oct. 9–15, 2006) pp. 2986–2992.CrossRefGoogle Scholar
9. Herdt, A. et al., “Online walking motion generation with automatic footstep placement,” Adv. Robot. 24, 719–737 (2010).CrossRefGoogle Scholar
10. Piperakis, S., Orfanoudakis, E. and Lagoudakis, M. G., “Predictive Control for Dynamic Locomotion of Real Humanoid Robots,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ (Sep. 14–18, 2014) pp. 4036–4043.Google Scholar
11. Dimitrov, D., Paolillo, A. and Wieber, P. B., “Walking Motion Generation with Online Foot Position Adaptation Based on l-1 and l-∞ Norm Penalty Formulations,” Proceedings of IEEE International Conference on Robotics and Automation, Piscataway, NJ (May 9–13, 2011) pp. 3523–3529.Google Scholar
12. Zhang, J., Liu, L., Li, C. and Chen, K., “Parametric omnidirectional gait planning of humanoid robots(in Chinese),” Robotica 36, 210–217 (2014).Google Scholar
13. Qiang, H. et al., “Planning walking patterns for a biped robot,” IEEE Trans. Robot. Autom. 17, 280–289 (2001).CrossRefGoogle Scholar
14. Dip, G., Prahlad, V. and Kien, P. D., “Genetic algorithm-based optimal bipedal walking gait synthesis considering tradeoff between stability margin and speed,” Robotica 27, 355–365 (2009).CrossRefGoogle Scholar
15. Hu, L., Zhou, C. and Sun, Z., “Estimating biped gait using spline-based probability distribution function with Q-learning,” IEEE Trans. Ind. Electron. 55, 1444–1452 (2008).Google Scholar
16. Kensuke, H. et al. “An analytical method on real-time gait planning for a humanoid robot,” Int. J. Humanoid Robot. 3, 1–19 (2006).Google Scholar
17. Strom, J., Slavov, G. and Chown, E., “Omnidirectional Walking Using ZMP and Preview Control for the NAO Humanoid Robot,” In: RoboCup 2009: Robot Soccer World Cup XIII (Springer, Berlin Heideiberg, 2010) pp. 378–389.CrossRefGoogle Scholar
18. Gouaillier, D., Collette, C. and Kilner, C., “Omnidirectional Closed-Loop Walk for NAO,” Proceedings of 2010 IEEE-RAS International Conference on Humanoid Robots, Piscataway, NJ (Dec. 6–8, 2010) pp. 448–454.CrossRefGoogle Scholar
19. Shafii, N. et al., “Omnidirectional Walking and Active Balance for Soccer Humanoid Robot,” In: Progress in Artificial Intelligence (Springer, Berlin, Heidelberg, 2013) pp. 283–294.Google Scholar
20. Graf, C. and Thomas, R., “A Closed-loop 3D-LIPM Gait for the RoboCup Standard Platform League Humanoid,” Proceedings of the 4th Workshop on Humanoid Soccer Robots in Conjunction with the 2009 IEEE-RAS International Conference on Humanoid Robots, Piscataway, NJ (2009) pp. 30–37.Google Scholar
21. Song, S., Ryoo, Y. J. and Hong, D. W. “Development of An Omnidirectional Walking Engine for Full-Sized Lightweight Humanoid Robots,” Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Aug. 28–31, 2011) pp. 847–854.Google Scholar
22. Alcaraz-Jimenez, J., Herrero-Perez, D. and Martinez-Barbera, H., “Motion planning for omnidirectional dynamic gait in humanoid soccer robots. J. Phys. Agents 5, 25–34 (2011).Google Scholar
23. Wieber, P. B., Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong Perturbation,” Proceedings of IEEE International Conference on Humanoid Robots, Piscataway, NJ (Dec. 4–6, 2006) pp. 137–142.Google Scholar
24. Vukobratovic, M. and Stepanenko, Y., On the stability of anthropomorphic systems. Math. Biosci. 15, 1–37 (1972).CrossRefGoogle Scholar
25. Herdt, A., Perrin, N. and Wieber, P. B., “Walking without Thinking About It,” Proceedings of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ (Oct. 18–22, 2010) pp. 190–195.Google Scholar
26. Garcia, M. et al., “Vision-guided motion primitives for humanoid reactive walking: Decoupled versus coupled approaches. Int. J. Robot. Res. 34, 402–419 (2014).Google Scholar
27. Dimitrov, D., Sherikov, A. and Wieber, P. B, “A Sparse Model Predictive Control Formulation for Walking Motion Generation,” Proceedings of 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ (Sep. 25–30, 2011) pp. 2292–2299.Google Scholar
28. Pratt, J. et al., “Capture Point: A Step Toward Humanoid Push Recovery,” Proceedings of IEEE-RAS International Conference on Humanoid Robots (2006) pp. 200–207.Google Scholar
29. Svanberg, K., “A class of globally convergent optimization methods based on conservative convex separable approximations,” SIAM J. Optim. 12, 555–573 (2002).Google Scholar
30. Conn, A. R., Gould, M. and Toint, P., “A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds,” SIAM J. Numer. Anal. 28, 545–572 (1991).CrossRefGoogle Scholar
31. Orin, D., Goswami, A. and Lee, H., “Centroidal dynamics of a humanoid robot,” Auton. Robots 35 (2–3), 161–176 (2013).Google Scholar
32. Hemker, T., Stelzer, M. and Stryk, O. V., “Efficient walking speed optimization of a humanoid robot,” Int. J. Robot. Res. 28, 303–314 (2009).Google Scholar
33. Sacks, J. et al. “Design and analysis of computer experiments. Stat. Sci. 4, 409–435 (1989).Google Scholar
34. Jones, D. R., Schonlau, M. and Welch, W. J., “Efficient global optimization of expensive black-box functions,” J. Global Opt. 13, 455–492 (1998).CrossRefGoogle Scholar
35. Bates, S. J., Sienz, J. and Toropov, V. V., “Formulation of the optimal Latin hypercube design of experiments using a permutation genetic algorithm,” AIAA J. 2011, 1–7 (2004).Google Scholar
36. Kanehiro, F., Hirukawa, H. and Kajita, S., “OpenHRP: Open architecture humanoid robotics platform,” Int. J. Robot. Res. 23, 155–165 (2004).Google Scholar
37. Johnson, S. G., “The NLopt Nonlinear-Optimization Package,” (2014). Available from: http://ab-initio.mit.edu/nlopt [cited Sep. 22, 2015].Google Scholar
38. Viana FAC, SURROGATES Toolbox User's Guide, Version 3.0 ed. Gainesville, FL, USA (2011). Available at http://sites.google.com/site/felipeacviana/surrogatestoolbox Google Scholar
39. Lophaven, S. N., Nielsen, H. B. and Sndergaard, J., “DACE: A MATLAB Kriging Toolbox,” (2007). Available at http://www.immdtu.dk/hbn/dace/ [accessed Nov. 7, 2007].Google Scholar
40. Xiao, X. et al. “Team TH-MOS,” (2014). Available at http://fei.edu.br/rcs/2014/TeamDescriptionPapers/Humanoid/index.html.Google Scholar
41. Bi, J. et al. “Team Description Paper for Team I-KID RoboCup 2014,” (2014). Available at http://fei.edu.br/rcs/2014/TeamDescriptionPapers/Humanoid/index.html.Google Scholar
42. Picheny, V. et al., “Quantile-based optimization of noisy computer experiments with tunable precision,” Technometrics 55, 2–13 (2013).Google Scholar