Motion Planning via Manifold Samples

  • Oren Salzman
  • Michael Hemmer
  • Barak Raveh
  • Dan Halperin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6942)


We present a general and modular algorithmic framework for path planning of robots. Our framework combines geometric methods for exact and complete analysis of low-dimensional configuration spaces, together with sampling-based approaches that are appropriate for higher dimensions. We suggest taking samples that are entire low-dimensional manifolds of the configuration space. These samples capture the connectivity of the configuration space much better than isolated point samples. Geometric algorithms then provide powerful primitive operations for complete analysis of the low-dimensional manifolds. We have implemented our framework for the concrete case of a polygonal robot translating and rotating amidst polygonal obstacles. To this end, we have developed a primitive operation for the analysis of an appropriate set of manifolds using arrangements of curves of rational functions. This modular integration of several carefully engineered components has lead to a significant speedup over the PRM sampling-based algorithm, which represents an approach that is prevalent in practice.


Motion Planning Motion Planning Problem Polygonal Obstacle Trait Class Free Space Cell 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Oren Salzman
    • 1
  • Michael Hemmer
    • 1
  • Barak Raveh
    • 1
    • 2
  • Dan Halperin
    • 1
  1. 1.Tel-Aviv UniversityIsrael
  2. 2.Hebrew UniversityIsrael

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