Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-28T04:21:51.959Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust push recovery by whole-body dynamics control with extremal accelerations

Published online by Cambridge University Press:  28 August 2013

Xuechao Chen ,
Qiang Huang ,
Zhangguo Yu  and
Yuepin Lu
Xuechao Chen
Affiliation:
The Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, P. R. China Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing, P. R. China Key Laboratory of Intelligent Control and Decision of Complex System, Beijing, P. R. China The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
Qiang Huang
Affiliation:
The Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, P. R. China Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing, P. R. China Key Laboratory of Intelligent Control and Decision of Complex System, Beijing, P. R. China
Zhangguo Yu
Affiliation:
The Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, P. R. China Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing, P. R. China Key Laboratory of Intelligent Control and Decision of Complex System, Beijing, P. R. China
Yuepin Lu*
Affiliation:
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, P. R. China
*
*Corresponding author. E-mail: luyuepin@126.com.
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a whole-body dynamics controller for robust push recovery on a force-controlled bipedal robot. Featherstone's spatial vector method is used to deduce dynamics formulas. We reveal a relationship between the accelerations of the floating base and the desired external forces needed for those accelerations. Introducing constraints on the desired external forces causes corresponding constraints on the accelerations. Quadratic programming is applied to find the extremal accelerations, which recover the robot from pushes as best as possible. A robustness criterion is proposed based on the linear inverted pendulum model to evaluate the performance of push recovery methods quantitatively. We evaluate four typical push recovery methods and the results show that our method is more robust than these. The effectiveness of the proposed method is demonstrated by push recovery in simulations.

Keywords

Push recoveryWhole-body dynamicsBipedal robotForce controlQuadratic programming
Type
Articles
Information
Robotica , Volume 32 , Issue 3 , May 2014 , pp. 467 - 476
Copyright
Copyright © Cambridge University Press 2013 

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.Pratt, J. and Krupp, B., “Design of a bipedal walking robot,” Proc. SPIE 6962, 69621F (1–13) (2008).Google Scholar
2.Stephens, B. and Atkeson, C., “Dynamic Balance Force Control for Compliant Humanoid Robots,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (2010) pp. 1248–1255.Google Scholar
3.Hyon, S.-H. and Cheng, G., “Passivity-Based Full-Body Force Control for Humanoids and Application to Dynamic Balancing and Locomotion,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (2006) pp. 4915–4922.Google Scholar
4.Boston Dynamics, “PETMAN,” available at: http://www.bostondyna-mics.com/robot_petman.html (Oct. 2009)Google Scholar
5.Stephens, B. J. and Atkeson, C. G., “Push Recovery by Stepping for Humanoid Robots with Force Controlled Joints,” In: Proceedings of IEEE International Conference on Humanoid Robots, Nashville, TN (2010), pp. 52–59.Google Scholar
6.Diedam, H., Dimitrov, D., Wieber, P.-B., Mombaur, K. and Diehl, M., “Online Walking Gait Generation with Adaptive Foot Positioning Through Linear Model Predictive Control,” In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Nice, France (2008) pp. 1121–1126.Google Scholar
7.Wieber, P.-B., “Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong Perturbations,” In: Proceedings of the IEEE International Conference on Humanoid Robots, Genoa, Italy (2006) pp. 137–142.Google Scholar
8.Kuo, A., “An optimal control model for analyzing human postural balance,” IEEE Trans. Biomed. Eng. 42, 87–101 (1995).Google Scholar
9.Atkeson, C. and Stephens, B., “Multiple Balance Strategies from One Optimization Criterion,” In: Proceedings of the IEEE International Conference on Humanoid Robots, Pittsburgh, PA (2007) pp. 57–64.Google Scholar
10.Dengpeng, X. and Xu, L., “Multiple balance strategies for humanoid standing control,” Acta Autom. Sin. 37 (2), 228–233 (2001).Google Scholar
11.Liu, C. G. and Atkeson, C. G., “Standing Balance Control Using a Trajectory Library,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO (2009) pp. 3031–3036.Google Scholar
12.Stephens, B., “Humanoid Push Recovery,” In: Proceedings of the IEEE International Conference on Humanoid Robots (2007) pp. 589–595.Google Scholar
13.Qing, T., Rong, X. and Jian, C., “Tip over avoidance control for biped robot,” Robotica 27, 883–889 (2009).Google Scholar
14.Han, H., Kim, T. and Park, T., “Tolerance Analysis of a Spur Gear Train,” In: Proceedings of the 3rd DADS Korean User's Conference, Seoul, Korea (1987) pp. 61–81.Google Scholar
15.Roy, F., “A beginner's guide to 6-D vectors (Part 1),” IEEE Robot. Autom. Mag. 17 (3), 83–94 (2010).Google Scholar
16.Roy, F., “A beginner's guide to 6-D vectors (Part 2),” IEEE Robot. Autom. Mag. 17 (4), 88–99 (2010).Google Scholar