Algorithmic Foundation of Robotics VII pp 507-522

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 47) | Cite as

Using Motion Primitives in Probabilistic Sample-Based Planning for Humanoid Robots

  • Kris Hauser
  • Timothy Bretl
  • Kensuke Harada
  • Jean-Claude Latombe

Abstract

This paper presents a method of computing efficient and natural-looking motions for humanoid robots walking on varied terrain. It uses a small set of high-quality motion primitives (such as a fixed gait on flat ground) that have been generated offline. But rather than restrict motion to these primitives, it uses them to derive a sampling strategy for a probabilistic, sample-based planner. Results in simulation on several different terrains demonstrate a reduction in planning time and a marked increase in motion quality.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kris Hauser
    • 1
  • Timothy Bretl
    • 1
  • Kensuke Harada
    • 2
  • Jean-Claude Latombe
    • 1
  1. 1.Computer Science DepartmentStanford University 
  2. 2.National Institute of Advanced Industrial Science and Technology (AIST)  

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