Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-18T16:04:11.406Z Has data issue: false hasContentIssue false
  • English
  • Français

Hybrid control for SLIP-based robots running on unknown rough terrain

Published online by Cambridge University Press:  15 January 2014

Bin Han ,
Xin Luo ,
Qingyu Liu ,
Bo Zhou  and
Xuedong Chen
Bin Han
Affiliation:
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People's Republic of China
Xin Luo
Affiliation:
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People's Republic of China
Qingyu Liu
Affiliation:
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People's Republic of China
Bo Zhou
Affiliation:
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People's Republic of China
Xuedong Chen*
Affiliation:
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People's Republic of China
*
*Corresponding author: E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Rapid and efficient dynamic stability control has been one of the important motivations in legged robot research, especially for legged robots running at high speed and/or on rough terrain. This paper presents a feasible control strategy, named Hybrid Feedback Control (HFC), for running systems based on the spring-loaded inverted pendulum principle (SLIP). The HFC strategy, which comprises two modules (i.e., touchdown angle control and energy compensation), predicts and regulates touchdown angle of the current cycle and need-to-complement energy input of the next cycle through hybrid feedback of flying apex state. This strategy can significantly reduce the computational complexity and enable the system to quickly converge to its control target, meeting the requirements of real-time control. Simulation experiments on various terrains were conducted to verify the adaptability of our HFC strategy. Results of these simulation experiments show that the approach herein can realize the periodical stability control of SLIP systems on different terrain conditions quickly and effectively.

Keywords

Legged robotsSpring-loaded inverted pendulum (SLIP)Hybrid feedback controlEnergy compensationRough terrain
Type
Articles
Information
Robotica , Volume 32 , Issue 7 , October 2014 , pp. 1065 - 1080
Copyright
Copyright © Cambridge University Press 2014 

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.LaBarbera, M., “Why the wheels won't go,” Am. Nat. 121, 395–408 (1983).Google Scholar
2.Farley, C. T., Blickhan, R., Saito, J. and Taylor, C. R., “Hopping frequency in humans: A test of how springs set stride frequency in bouncing gaits,” J. Appl. Physiol. 71, 2127–2132 (1991).Google Scholar
3.Farley, C. T. and Morgenroth, D. C., “Leg stiffness primarily depends on ankle stiffness during human hopping,” J. Biomech. 32, 267–273 (1999).Google Scholar
4.Granata, K. P., Padua, D. A. and Wilson, S. E., “Gender differences in active musculoskeletal stiffness. Part II. Quantification of leg stiffness during functional hopping tasks,” J. Electromyogr. Kinesiol. 12, 127–135 (2002).CrossRefGoogle ScholarPubMed
5.Hobara, H., Kanosue, K. and Suzuki, S., “Changes in muscle activity with increase in leg stiffness during hopping,” Neurosci. Lett. 418, 55–59 (2007).CrossRefGoogle ScholarPubMed
6.Full, R. J., “Mechanics and Energetics of Terrestrial Locomotion: From Bipeds to Polypeds,” In: Energy Transformation in Cells and Animals (Wieser, W. and Gnaiger, E., eds.) (Georg Thieme Verlag Press, Stuttgart, Germany, 1989) pp. 175–182.Google Scholar
7.Alexander, R., “Three uses for springs in legged locomotion,” Int. J. Robot. Res. 9, 53–61 (1990).CrossRefGoogle Scholar
8.Alexander, R. and Jayes, A. S., “Vertical movements in walking and running,” J. Zool. 185, 27–40 (1978).Google Scholar
9.Full, R. J. and Koditschek, D. E., “Templates and anchors: Neuromechanical hypotheses of legged locomotion on land,” J. Exp. Biol. 202, 3325–3332 (1999).Google Scholar
10.Karssen, J. G. D. and Wisse, M., “Running with improved disturbance rejection by using non-linear leg springs,” Int. J. Robot. Res. 30, 1585–1595 (2011).Google Scholar
11.Blickhan, R. and Full, R. J., “Similarity in multilegged locomotion: Bouncing like a monopode,” J. Comp. Physiol. A-Sens. Neural Behav. Physiol. 173, 509–517 (1993).Google Scholar
12.Holmes, P., Full, R. J., Koditschek, D. and Guckenheimer, J., “The dynamics of legged locomotion: Models, analyses, and challenges,” SIAM Rev. 48, 207–304 (2006).CrossRefGoogle Scholar
13.Saranli, U., Schwind, W. J. and Koditschek, D. E., “Toward the Control of a Multi-Jointed, Monoped Runner,” Proceedings of IEEE International Conference on Robotics and Automation, 1998 (ICRA '98), Leuven, Belgium (May 1998) pp. 2676–2682.Google Scholar
14.Blickhan, R., “The spring-mass model for running and hopping,” J. Biomech. 22, 1217–1227 (1989).Google Scholar
15.Schwind, W. J. and Koditschek, D. E., “Approximating the stance map of a 2-DOF monoped runner,” J. Nonlinear Sci. 10, 533–568 (2000).Google Scholar
16.Raibert, M. H., Legged Robots that Balance (MIT Press, Cambridge, MA, 1986).CrossRefGoogle Scholar
17.Gregorio, P., Ahmadi, M. and Buehler, M., “Design, control, and energetics of an electrically actuated legged robot,” IEEE Trans. Syst. Man Cybern. B-Cybern. 27, 626–634 (1997).Google Scholar
18.Hurst, J. W., Chestnutt, J. E. and Rizzi, A. A., “Design and Philosophy of the Bimasc, a Highly Dynamic Biped,” Proceedings of IEEE International Conference on Robotics and Automation, 2007 (ICRA 2007), Roma, Italy (Apr. 2007) pp. 1863–1868.Google Scholar
19.Zeglin, G., “The Bow Leg Hopping Robot,” Proceedings of IEEE International Conference on Robotics and Automation, 1998 (ICRA'98), Leuven, Belgium (May 1998), pp. 781–786.Google Scholar
20.Ahmadi, M. and Buehler, M., “The ARL Monopod II Running Robot: Control and Energetics,” Proceedings of IEEE International Conference on Robotics and Automation, 1999 (ICRA '99), Detroit, Michigan (May. 1999) pp. 1689–1694.Google Scholar
21.Cherouvim, N. and Papadopoulos, E., “Energy saving passive-dynamic gait for a one-legged hopping robot,” Robotica 24, 491–498 (2006).Google Scholar
22.Ghigliazza, R. M., Altendorfer, R., Holmes, P. and Koditschek, D. E., “A simply stabilized running model,” SIAM J. Appl. Dyn. Syst. 2, 187–218 (2003).Google Scholar
23.Holmes, P., “Poincaré, celestial mechanics, dynamical-systems theory and chaos,” Phys. Rep. 193, 137–163 (1990).CrossRefGoogle Scholar
24.Geyer, H., Seyfarth, A. and Blickhan, R., “Spring-mass running: Simple approximate solution and application to gait stability,” J. Theor. Biol. 232, 315–328 (2005).Google Scholar
25.Saranli, U., Arslan, Ö., Ankaralí, M. M. and Morgül, Ö., “Approximate analytic solutions to non-symmetric stance trajectories of the passive spring-loaded inverted pendulum with damping,” Nonlinear Dyn. 62, 729–742 (2010).Google Scholar
26.Haitao, Y., Mantian, L. and Hegao, C., “Approximating the Stance Map of the SLIP Runner Based on Perturbation Approach,” Proceedings of IEEE International Conference on Robotics and Automation, 2012 (ICRA 2012), St. Paul, MN (May 2012) pp. 4197–4203.CrossRefGoogle Scholar
27.Carver, S. G., “Control of a Spring-Mass Hopper,” Ph.D. thesis, Cornell University, Ithaca, 2003.Google Scholar
28.Seyfarth, A., Geyer, H. and Herr, H., “Swing-leg retraction: A simple control model for stable running,” J. Exp. Biol. 206, 2547–2555 (2003).Google Scholar
29.Arslan, Ö. and Saranli, U., “Reactive planning and control of planar spring–mass running on rough terrain,” IEEE Trans. Robot. 28, 567–579 (2012).Google Scholar
30.Schmitt, J. and Clark, J., “Modeling posture-dependent leg actuation in sagittal plane locomotion,” Bioinspir. Biomim. 4, 046005 (2009).Google Scholar
31.Ahmadi, M. and Buehler, M., “Controlled passive dynamic running experiments with the ARL-monopod II,” IEEE Trans. Robot. 22, 974–986 (2006).Google Scholar
32.Hodgins, J. K. and Raibert, M. N., “Adjusting step length for rough terrain locomotion,” IEEE Trans. Robot. Autom. 7, 289–298 (1991).Google Scholar
33.Hyon, S. H. and Emura, T., “Energy-preserving control of a passive one-legged running robot,” Adv. Robot. 18, 357–381 (2004).CrossRefGoogle Scholar
34.Herr, H. M., Huang, G. T., and McMahon, T. A., “A model of scale effects in mammalian quadrupedal running,” J. Exp. Biol. 205, 959–967 (2002).Google Scholar
35.Farley, C. T., Glasheen, J. and McMahon, T. A., “Running and springs: Speed and animal size,” J. Exp. Biol. 185, 71–86 (1993).Google Scholar