Automatic Optimal Biped Walking as a Mixed-Integer Quadratic Program

Chapter

Abstract

This chapter proposes an original Model Predictive Control approach to the walking control for humanoid robots, which allows to generate stable walking motions without the prior definition of footsteps positions and instants. Both the instant and amplitude of the changes in the supporting surface are part of the walking motion generation problem, and are described by a set of highly-constrained integer and real variables. Combined with the center of mass trajectory of the robot, this description leads to the formulation of a Mixed-Integer Quadratic Program in a Model Predictive Control framework aiming at reaching high-level objectives, such as velocity tracking and tip-over risk minimization. The contribution of this approach is illustrated by the simulation of two scenarii, demonstrating the validity of the steps and trajectories computed in push-recovery and walking velocity tracking cases.

Keywords

Biped walking Balance control Hybrid systems  Footsteps planning Push recovery Mixed-integer quadratic programming 

References

  1. 1.
    Barthelemy, S., Salini, J., Micaelli, A.: Arboris-python. https://github.com/salini/arboris-python
  2. 2.
    Herdt, A., Diedam, H., Wieber, P.B., Dimitrov, D., Mombaur, K., Diehl, M.: Online walking motion generation with automatic footstep placement. Adv. Rob. 24, 719–737 (2010)CrossRefGoogle Scholar
  3. 3.
    Kajita, S., Kanehiro, F., Kaneko, K., Kajiwara, K., Harada, K., Yokoi, K., Hirukawa, H.: Biped walking pattern generation by using preview control of zero-moment point. In: Proceedings of the IEEE ICRA (2003)Google Scholar
  4. 4.
    Maki, B.E., Mcilroy, W.E., Fernie, G.R.: Change-in-support reactions for balance recovery. IEEE Eng. Med. Biol. Mag. 22(2), 20–26 (2003)CrossRefGoogle Scholar
  5. 5.
    Pratt, J., Carff, J., Drakunov, S., Goswami, A.: Capture point: A step toward humanoid push recovery. In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots, pp. 200–207. IEEE (2006)Google Scholar
  6. 6.
    Salini, J., Padois, V., Bidaud, P.: Synthesis of complex humanoid whole-body behavior: a focus on sequencing and tasks transitions. In: Proceedings of the IEEE ICRA, pp. 1283–1290. IEEE (2011)Google Scholar
  7. 7.
    Sandini, G., Metta, G., Vernon, D.: The icub cognitive humanoid robot: an open-system research platform for enactive cognition. In: 50 Years of Artificial Intelligence, Lecture Notes in Computer Science, chap. 32, pp. 358–369. Springer (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Aurelien Ibanez
    • 1
    • 2
  • Philippe Bidaud
    • 1
    • 2
    • 3
  • Vincent Padois
    • 1
    • 2
  1. 1.Institut des Systèmes Intelligents et de RobotiqueSorbonne Universités, UPMC Univ Paris 06, UMR 7222ParisFrance
  2. 2.Institut des Systèmes Intelligents et de RobotiqueCNRSParisFrance
  3. 3.ONERAPalaiseauFrance

Personalised recommendations