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Robust Bipedal Locomotion Based on a Hierarchical Control Structure

Published online by Cambridge University Press:  01 March 2019

Jianwen Luo
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China. E-mail: [email protected] Department of Computer Science, Stanford University, Stanford, CA, USA
Yao Su
Affiliation:
Mechanical and Aerospace Engineering Department, University of California, Los Angeles, CA, USA. E-mails: [email protected], [email protected]
Lecheng Ruan
Affiliation:
Mechanical and Aerospace Engineering Department, University of California, Los Angeles, CA, USA. E-mails: [email protected], [email protected]
Ye Zhao
Affiliation:
Woodruff School of Mechanical Engineering, Georgia Institute of Technology. E-mail: [email protected]
Donghyun Kim
Affiliation:
Mechanical Engineering Department, Massachusetts Institute of Technology, MA, USA. E-mail: [email protected]
Luis Sentis
Affiliation:
Department of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, Austin, TX, USA. E-mail: [email protected]
Chenglong Fu*
Affiliation:
Department of Computer Science, Stanford University, Stanford, CA, USA
*
*Corresponding author. E-mail: [email protected]

Summary

To improve biped locomotion’s robustness to internal and external disturbances, this study proposes a hierarchical structure with three control levels. At the high level, a foothold sequence is generated so that the Center of Mass (CoM) trajectory tracks a planned path. The planning procedure is simplified by selecting the midpoint between two consecutive Center of Pressure (CoP) points as the feature point. At the middle level, a novel robust hybrid controller is devised to drive perturbed system states back to the nominal trajectory within finite cycles without chattering. The novelty lies in that the hybrid controller is not subject to linear CoM dynamic constraints. The hybrid controller consists of two sub-controllers: an oscillation controller and a smoothing controller. For the oscillation controller, the desired CoM height is specified as a sine-shaped function, avoiding a new attractive limit cycle. However, this controller results in the inevitable chattering because of discontinuities. A smoothing controller provides continuous properties and thus can inhibit the chattering problem, but has a smaller region of attraction compared with the oscillation controller. A hybrid controller merges the two controllers for a smooth transition. At the low level, the desired CoM motion is defined as tasks and embedded in a whole body operational space (WBOS) controller to compute the joint torques analytically. The novelty of the low-level controller lies in that within the WBOS framework, CoM motion is not subject to fixed CoM dynamics and thus can be generalized.

Type
Articles
Copyright
© Cambridge University Press 2019 

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References

Dai, H., Valenzuela, A. and Tedrake, R., “Whole-Body Motion Planning with Centroidal Dynamics and Full Kinematics,” IEEE-RAS International Conference on Humanoid Robots, Madrid, Spain (2014) pp. 295302.Google Scholar
Feng, S., Whitman, E., Xinjilefu, X. and Atkeson, C. G., “Optimization Based Full Body Control for the Atlas Robot,” Proceedings of the 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Madrid, Spain (2014) pp. 120127.Google Scholar
Garimort, J., Hornung, A. and Bennewitz, M., “Humanoid Navigation with Dynamic Footstep Plans,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (2011) pp. 39823987.Google Scholar
Michel, P., Chestnutt, J., Kuffner, J. and Kanade, T., “Vision-Guided Humanoid Footstep Planning for Dynamic Environments,” Proceedings of the 5th IEEE-RAS International Conference on Humanoid Robots, Seoul, Korea (2005) pp. 1318.Google Scholar
Baudouin, L., Perrin, N., Moulard, T., Lamiraux, F., Stasse, O. and Yoshida, E., “Real-Time Replanning Using 3d Environment for Humanoid Robot,” Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Bled, Slovenia (2011) pp. 584589.Google Scholar
Chestnutt, J., Nishiwaki, K., Kuffner, J. and Kagami, S., “An Adaptive Action Model for Legged Navigation Planning,” Proceedings of the 7th IEEE-RAS International Conference on Humanoid Robots, Pittsburgh, USA (2007) pp. 196202.Google Scholar
Kuffner, J. J., Kagami, S., Nishiwaki, K., Inaba, M. and Inoue, H., “Dynamically-stable motion planning for humanoid robots,” Auton. Robots 12(1), 105118 (2002).CrossRefGoogle Scholar
Kuffner, J., Nishiwaki, K., Kagami, S., Inaba, M. and Inoue, H. “Motion Planning for Humanoid Robots under Obstacle and Dynamic Balance Constraints,” Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea, vol. 1 (2001) pp. 692698.Google Scholar
Chestnutt, J., Lau, M., Cheung, G., Kuffner, J., Hodgins, J. and Kanade, T., “Footstep Planning for the Honda Asimo Humanoid,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA, Barcelona, Spain (2005) pp. 629634.Google Scholar
Zhao, Y., Fernandez, B. R. and Sentis, L., “Robust optimal planning and control of non-periodic bipedal locomotion with a centroidal momentum model,” Int. J. Rob. Res. 36(11), 12111242 (2017).CrossRefGoogle Scholar
Westervelt, E. R., Chevallereau, C., Choi, J. H., Morris, B. and J. W. Grizzle. Feedback Control of Dynamic Bipedal Robot Locomotion (CRC Press, Boca Raton, USA, 2007).Google Scholar
Posa, M., Tobenkin, M. and Tedrake, R., “Lyapunov Analysis of Rigid Body Systems with Impacts and Friction via Sums-of-Squares,” Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control, Philadelphia, USA (2013) pp. 6372.Google Scholar
Manchester, I. R., Tobenkin, M. M., Levashov, M. and Tedrake, R., “Regions of Attraction for Hybrid Limit Cycles of Walking Robots,” IFAC Proceedings Volumes, 44(1) (2011), pp. 58015806.Google Scholar
Papachristodoulou, A. and Prajna, S., “Robust stability analysis of nonlinear hybrid systems,” IEEE Trans. Automat. Contr. 54(5), 10351041 (2009).CrossRefGoogle Scholar
Sentis, L., Synthesis and Control of Whole-Body Behaviors in Humanoid Systems Ph.D. Dissertation (Stanford University, Stanford, 2007).Google Scholar
Koolen, T., Bertrand, S., Thomas, G., De Boer, T., Wu, T., Smith, J., Englsberger, J. and Pratt, J., “Design of a momentum-based control framework and application to the humanoid robot atlas,” Int. J. HR 13(1), 1650007 (2016).Google Scholar
Pratt, J., Chew, C. M., Torres, A., Dilworth, P. and Pratt, G., “Virtual model control: An intuitive approach for bipedal locomotion,” Int. J. Rob. Res. 20(2), 129143 (2001).CrossRefGoogle Scholar
Khatib, O. and Burdick, J., “Motion and Force Control of Robot Manipulators,” Proceedings of the 1986 IEEE International Conference on Robotics and Automation, San Francisco, USA, vol. 3 (1986) pp. 13811386.Google Scholar
Luo, J., Zhao, Y., Kim, D., Khatib, O. and Sentis, L., “Locomotion Control of Three Dimensional Passive-Foot Biped Robot Based on Whole Body Operational Space Framework,” Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Marco, China (2017) pp. 15771582.Google Scholar
Stephens, B. J. and Atkeson, C. G., “Dynamic Balance Force Control for Compliant Humanoid Robots,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan (2010) pp. 12481255.Google Scholar
Posa, M., Kuindersma, S. and Tedrake, R., “Optimization and Stabilization of Trajectories for Constrained Dynamical Systems,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden (2016) pp. 13661373.Google Scholar
Righetti, L. and Schaal, S., “Quadratic Programming for Inverse Dynamics with Optimal Distribution of Contact Forces,” Proceedings of the 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Osakam, Japan (2012) pp. 538543.Google Scholar
Johnson, M., Shrewsbury, B., Bertrand, S., Wu, T., Duran, D., Floyd, M., Abeles, P., Stephen, D., Mertins, N., Lesman, A. and Carff, J., “Team IHMC’s lessons learned from the DARPA robotics challenge trials,” J. Field Robot. 32(2), 192208 (2015).CrossRefGoogle Scholar
Ott, C., Mukherjee, R. and Nakamura, Y., “A hybrid system framework for unified impedance and admittance control,” J. Intell. Robot. Syst. 78(3–4), 359375 (2015).CrossRefGoogle Scholar
Feng, S., Whitman, E., Xinjilefu, X. and Atkeson, C. G., “Optimization-based full body control for the DARPA robotics challeange,” J. Field Robot. 32(2), 293312 (2015).CrossRefGoogle Scholar
Kajita, S., Kanehiro, F., Kaneko, K., Yokoi, K. and Hirukawa, H., “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, Maui, USA, vol. 1 (2001) pp. 239246.Google Scholar
Gao, W., Jia, Z. and Fu, C.. “Increase the feasible step region of biped robots through active vertical flexion and extension motions,” Robotica 35(7), 15411561 (2017).CrossRefGoogle Scholar
Pratt, J., Koolen, T., De Boer, T., Rebula, J., Cotton, S., Carff, J., Johnson, M. and Neuhaus, P., “Capturability-based analysis and control of legged locomotion, Part 2: Application to M2V2, a lower-body humanoid,” Int. J. Rob. Res. 31(10), 11171133 (2012).CrossRefGoogle Scholar
Luo, J. W., Fu, Y. L. and Wang, S. G.. “3D stable biped walking control and implementation on real robot,” Adv. Robot. 31(12), 634649 (2017).CrossRefGoogle Scholar
Raibert, M. H., Legged Robots that Balance (MIT Press, Cambridge, MA, 1986).CrossRefGoogle Scholar
Geyer, H., Seyfarth, A. and Blickhan, R., “Compliant Leg Behaviour Explains Basic Dynamics of Walking and Running,” Proceedings of the Royal Society B: Biological Sciences (2006) pp. 28612867.Google Scholar
Rezazadeh, S., Hubicki, C., Jones, M., Peekema, A., Van Why, J., Abate, A. and Hurst, J., “Spring-mass Walking with ATRIAS in 3D: Robust Gait Control Spanning Zero to 4.3 kph on a Heavily Underactuated Bipedal Robot,” ASME 2015 Dynamic Systems and Control Conference, Columbus, USA (2015) pp. V001T04A003–V001T04A003.Google Scholar
Vejdani, H. R., Blum, Y., Daley, M. A. and Hurst, J. W., “Bio-inspired swing leg control for spring-mass robots running on ground with unexpected height disturbance,” Bioinspir. Biomim. 8(4), 046006 (2013).CrossRefGoogle ScholarPubMed
Kim, D., Zhao, Y., Thomas, G., Fernandez, B. R. and Sentis, L., “Stabilizing series-elastic point-foot bipeds using whole-body operational space control,” IEEE Trans. Robot. 32(6), 13621379 (2016).CrossRefGoogle Scholar
Kim, D., Thomas, G. and Sentis, L., “Continuous Cyclic Stepping on 3D Point-foot Biped Robots via Constant Time to velocity Reversal,” Proceedings of the 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore (2014) pp. 16371643.Google Scholar
Wieber, P. B., “Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong Perturbations,” IEEE-RAS International Conference on Humanoid Robots, Genoa, Italy (2006).CrossRefGoogle Scholar
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 Trans. Ind. Electron. 60(11), 51375147 (2013).Google Scholar
Luo, J., Wang, S., Zhao, Y. and Fu, Y., “Variable stiffness control of series elastic actuated biped locomotion,” Intel. Serv. Robot. 11(3), 225235 (2018).CrossRefGoogle Scholar