A Planning Algorithm for Dynamic Motions

  • Pedro S. Huang
  • Michiel van de Panne
Part of the Eurographics book series (EUROGRAPH)


Motions such as flips and jumps are challenging to animate and to perform in real life. The difficulty arises from the dynamic nature of the movements and the precise timing required for their successful execution. This paper presents a decision-tree search algorithm for planning the control for these types of motion. Several types of results are presented, including cartwheels, flips and hops for a two-link gymnastic ‘acrobot’. It is also shown that the same search algorithm is effective at a macroscopic scale for planning dynamic motions across rugged terrain.


Search Algorithm Joint Angle Search Tree Dynamic Motion Rugged Terrain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [BF92]
    Matthew D. Berkemeier and Ronald S. Fearing. Control of a twolink robot to achieve sliding and hopping gaits. IEEE Conference on Robotics and Automation, 1:286–291, 1992.Google Scholar
  2. [BF94]
    Matthew D. Berkemeier and Ronald S. Fearing. Control experiments on an underactuated robot with applications to legged locomotion. Proceedings, IEEE International Conference on Robotics and Automation, pages 149–154, 1994.Google Scholar
  3. [Bor92]
    S. A. Bortoff. Pseudolinearization Using Spline Functions with Application to the Acrobot. PhD thesis, University of Illinois at Urbana-Champaign, 1992.Google Scholar
  4. [BS92]
    S. A. Bortoff and M. W. Spong. Pseudolinearization of the acrobot using spline functions. Proceedings, 31st Conference on Decision and Control, pages 593–598, 1992.Google Scholar
  5. [GT95]
    Radek Grzeszczuk and Demetri Terzopoulos. Automated learning of muscle-actuated locomotion through control abstraction. Proceedings of SIGGRAPH’ 95, ACM Computer Graphics, pages 63–70, April 1995.Google Scholar
  6. [HM90]
    J. Hauser and R. M. Murray. Nonlinear controllers for non-integrable systems: The acrobot example. Proceedings, American Control Conference, pages 669-671, 1990.Google Scholar
  7. [HR90]
    J. K. Hodgins and M. H. Raibert. Biped gymnastics. International Journal of Robotics Research, 2:115–132, 1990.Google Scholar
  8. [NM93]
    J. T. Ngo and J. Marks. Spacetime Constraints Revisited. Proceedings of SIGGRAPH’ 93, ACM Computer Graphics, pages 343–350, 1993.Google Scholar
  9. [PAH92]
    M. G. Pandy, F. C. Anderson, and D. G. Hull. A parameter optimization approach for the optimal control of large-scale musculoskeletal systems. Transactions of the ASME, 114:450–459, November 1992.CrossRefGoogle Scholar
  10. [RH91]
    Marc Raibert and Jessica Hodgins. Animation of dynamic legged locomotion. Proceedings of SIGGRAPH’ 91, ACM Computer Graphics, pages 349–358, 1991.Google Scholar
  11. [Sim94]
    Karl Sims. Evolving Virtual Creatures. Proceedings of SIGGRAPH’ 94, ACM Computer Graphics, pages 15–22, 1994.Google Scholar
  12. [vF93]
    Michiel van de Panne and Eugene Fiume. Sensor-actuator networks. Proceedings of SIGGRAPH’ 93, ACM Computer Graphics, pages 335–342, 1993.Google Scholar
  13. [vFV93]
    Michiel van de Panne, Eugene Fiume, and Zvonko Vranesic. Physically-Based Modeling and Control of Turning. Computer Vision, Graphics, and Image Processing: Graphical Models and Image Processing, Vol. 55, No. 6, November 1993, 507–521.Google Scholar
  14. [vKF94]
    Michiel van de Panne, Ryan Kim, and Eugene Fiume. Virtual wind-up toys for animation. Graphics Interface, pages 208–215, 1994.Google Scholar
  15. [Win84]
    Patrick H. Winston. Artifical Intelligence, 2nd Edition, Addison-Wesley, 1984.Google Scholar

Copyright information

© Springer-Verlag/Wien 1996

Authors and Affiliations

  • Pedro S. Huang
    • 1
  • Michiel van de Panne
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoCanada

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