On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages

Oren Salzman, Michael Hemmer, Dan Halperin

Research output: Contribution to journalArticlepeer-review


We extend our study of Motion Planning via Manifold Samples (MMS), a general algorithmic framework that combines geometric methods for the exact and complete analysis of low-dimensional configuration spaces with sampling-based approaches that are appropriate for higher dimensions. The framework explores the configuration space by taking samples that are low-dimensional manifolds of the configuration space capturing its connectivity much better than isolated point samples. The scheme is particularly suitable for applications in manufacturing, such as assembly planning, where typically motion planning needs to be carried out in very tight quarters. The contributions of this paper are as follows: (i) We present a recursive application of MMS in a six-dimensional configuration space, enabling the coordination of two polygonal robots translating and rotating amidst polygonal obstacles. In the adduced experiments for the more demanding test cases MMS clearly outperforms Probabilistic Roadmaps (PRM), with over 40-fold speedup in a six-dimensional coordination-tight setting. (ii) A probabilistic completeness proof for the case of MMS with samples that are affine subspaces. (iii) A closer examination of the test cases reveals that MMS has, in comparison to standard sampling-based algorithms, a significant advantage in scenarios containing high-dimensional narrow passages. This provokes a novel characterization of narrow passages, which attempts to capture their dimensionality, an attribute that had been (to a large extent) unattended in previous definitions.

Original languageEnglish
Article number6866915
Pages (from-to)529-538
Number of pages10
JournalIEEE Transactions on Automation Science and Engineering
Issue number2
StatePublished - 1 Apr 2015
Externally publishedYes


  • Robot motion planning
  • computational geometry algorithms library (CGAL)
  • manifolds
  • narrow passage
  • probabilistic roadmaps (PRM)


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