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Contact Planning for the ANYmal Quadruped Robot Using an Acyclic Reachability-Based Planner

  • Mathieu GeisertEmail author
  • Thomas Yates
  • Asil Orgen
  • Pierre Fernbach
  • Ioannis Havoutis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11649)

Abstract

Despite the great progress in quadrupedal robotics during the last decade, selecting good contacts (footholds) in highly uneven and cluttered environments still remains an open challenge. This paper builds upon a state-of-the-art approach, already successfully used for humanoid robots, and applies it to our robotic platform; the quadruped robot ANYmal. The proposed algorithm decouples the problem into two subproblems: first a guide trajectory for the robot is generated, then contacts are created along this trajectory. Both subproblems rely on approximations and heuristics that need to be tuned. The main contribution of this work is to explain how this algorithm has been retuned to work with ANYmal and to show the relevance of the approach with a variety of tests in realistic dynamic simulations.

Keywords

Motion planning Contact planning Legged robotics Quadruped robots 

References

  1. 1.
    Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M., Schaal, S.: Learning, planning, and control for quadruped locomotion over challenging terrain. Int. J. Robot. Res. 30, 236–258 (2010)CrossRefGoogle Scholar
  2. 2.
    Carpentier, J., Mansard, N.: Multi-contact locomotion of legged robots. IEEE Trans. Robot. 34, 1441–1460 (2018)CrossRefGoogle Scholar
  3. 3.
    Ponton, B., Herzog, A., Schaal, S., Righetti, L.: A convex model of momentum dynamics for multi-contact motion generation. In: IEEE-RAS International Conference on Humanoid Robots (2016)Google Scholar
  4. 4.
    Herdt, A., Diedam, H., Wieber, P.B., Dimitrov, D., Mombaur, K., Diehl, M.: Online walking motion generation with automatic foot step placement. Adv. Robot. 24, 719–737 (2010)CrossRefGoogle Scholar
  5. 5.
    Naveau, M., Kudruss, M., Stasse, O., Kirches, C., Mombaur, K., Soures, P.: A reactive walking pattern generator based on nonlinear model predictive control. IEEE Robot. Autom. Lett. 2, 10–17 (2016)CrossRefGoogle Scholar
  6. 6.
    Mastalli, C., et al.: Trajectory and foothold optimization using low-dimensional models for rough terrain locomotion. In: IEEE-RAS International Conference on Robotics and Automation (2017)Google Scholar
  7. 7.
    Winkler, A., Bellicoso, D., Hutter, M., Buchli, J.: Gait and trajectory optimization for legged systems through phase-based end-effector parameterization. IEEE Robot. Autom. Lett. 3, 1560–1567 (2018)CrossRefGoogle Scholar
  8. 8.
    Mastalli, C., Havoutis, I., Focchi, M., Caldwell, D.G., Semini, C.: Hierarchical planning of dynamic movements without scheduled contact sequences. In: IEEE-RAS International Conference on Robotics and Automation (2016)Google Scholar
  9. 9.
    Posa, M., Kuindersma, S., Tedrake, R.: Optimization and stabilization of trajectories for constrained dynamical systems. In: IEEE/RAS International Conference on Robotics and Automation (2016)Google Scholar
  10. 10.
    Mordatch, I., Todorov, E., Popović, Z.: Discovery of complex behaviors through contact-invariant optimization. ACM Trans. Graph. 31, 43 (2012)CrossRefGoogle Scholar
  11. 11.
    Deits, R., Tedrake, R.: Footstep planning on uneven terrain with mixed-integer convex optimization. In: IEEE-RAS International Conference on Humanoid Robots (2014)Google Scholar
  12. 12.
    Aceituno Cabezas, B., et al.: Simultaneous contact, gait and motion planning for robust multi-legged locomotion via mixed-integer convex optimization. IEEE Robot. Autom. Lett. 3, 2531–2538 (2018)Google Scholar
  13. 13.
    Kuffner, J., Nishiwaki, K., Kagami, S., Inaba, M., Inoue, H.: Footstep planning among obstacles for biped robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2001)Google Scholar
  14. 14.
    Perrin, N., Stasse, O., Lamiraux, F., Yoshida, E.: Humanoid motion generation and swept volumes: theoretical bounds for safe steps. Adv. Robot. 27, 1045–1058 (2013)CrossRefGoogle Scholar
  15. 15.
    Winkler, A., Mastalli, C., Havoutis, I., Focchi, M., Caldwell, D., Semini, C.: Planning and execution of dynamic whole-body locomotion for a hydraulic quadruped on challenging terrain. In: IEEE-RAS International Conference on Robotics and Automation (2015)Google Scholar
  16. 16.
    Holden, D., Komura, T., Saito, J.: Phase-functioned neural networks for character control. ACM Trans. Graph. 36, 42 (2017)CrossRefGoogle Scholar
  17. 17.
    DeepMind: Producing flexible behaviours in simulated environments (2017). https://deepmind.com/blog/producing-flexible-behaviours-simulated-environments/
  18. 18.
    Hwangbo, J., et al.: Learning agile and dynamic motor skills for legged robots. Sci. Robot. 4(26) (2019).  https://doi.org/10.1126/scirobotics.aau5872
  19. 19.
    Tonneau, S., Del Prete, A., Pettré, J., Park, C., Manocha, D., Mansard, N.: An efficient acyclic contact planner for multiped robots. IEEE Trans. Robot. 34, 586–601 (2018)CrossRefGoogle Scholar
  20. 20.
    Hutter, M., et al.: Anymal - a highly mobile and dynamic quadrupedal robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2016)Google Scholar
  21. 21.
    Bouyarmane, K., Escande, A., Lamiraux, F., Kheddar, A.: Potential field guide for humanoid multicontacts acyclic motion planning. In: IEEE International Conference on Robotics and Automation (2009)Google Scholar
  22. 22.
    Fernbach, P., Tonneau, S., Del Prete, A., Taïx, M.: A Kinodynamic steering-method for legged multi-contact locomotion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2017)Google Scholar
  23. 23.
    Fernbach, P., Tonneau, S., Taïx, M.: CROC: Convex Resolution Of Centroidal dynamics trajectories to provide a feasibility criterion for the multi contact planning problem. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2018)Google Scholar
  24. 24.
    Mirabel, J., et al.: HPP: a new software for constrained motion planning. In: IEEE/RJS International Conference on Intelligent Robots and Systems (2016)Google Scholar
  25. 25.
  26. 26.
    Fankhauser, P., Bellicoso, D., Gehring, C., Dube, R., Gawel, A., Hutter, M.: Free gait an architecture for the versatile control of legged robots. In: IEEE-RAS International Conference on Humanoid Robots (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mathieu Geisert
    • 1
    Email author
  • Thomas Yates
    • 1
  • Asil Orgen
    • 2
  • Pierre Fernbach
    • 3
  • Ioannis Havoutis
    • 1
  1. 1.Oxford Robotics InstituteUniversity of OxfordOxfordUK
  2. 2.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey
  3. 3.Laboratoire d’Analyse et d’Architecture Système, CNRSToulouseFrance

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