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Motion Planning for Legged Robots on Varied TerrainDepartment of Computer Science Stanford University Stanford,CA 94305-5447, USA, khauser{at}cs.stanford.edu
University of Illinois at Urbana-Champaign, Urbana,IL 61801-2935, USA, tbretl{at}illinois.edu
Department of Computer Science Stanford University Stanford,CA 94305-5447, USA, latombe{at}cs.stanford.edu
Humanoid Research Group Intelligent Systems ResearchInstitute National Institute of Advanced Industrial Science and Technology(AIST) Tsukuba, Ibaraki 305-8568, Japan, kensuke.harada{at}aist.go.jp
Jet Propulsion Laboratory California Institute of TechnologyPasadena, CA 91109, Brian.H.Wilcox{at}jpl.nasa.gov In this paper we study the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain's local geometric and physical properties. In this paper we present a planner that computes these motions by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls. To improve motion quality, the probabilistic planner derives its sampling strategy from a small set of motion primitives that have been generated offline. The viability of this approach is demonstrated in simulation for the six-legged Lunar vehicle ATHLETE and the humanoid HRP-2 on several example terrains, including one that requires both hand and foot contacts and another that requires rappelling.
Key Words: Motion planning legged robots humanoids probabilistic sample-based planning motion primitives
The International Journal of Robotics Research, Vol. 27, No. 11-12,
1325-1349 (2008) This article has been cited by other articles:
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