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Sparsification of motion-planning roadmaps by edge contraction

  • Oren Salzman*
  • , Doron Shaharabani
  • , Pankaj K. Agarwal
  • , Dan Halperin
  • *Corresponding author for this work
  • Tel Aviv University
  • Duke University

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

Roadmaps constructed by the recently introduced PRM∗ algorithm for asymptotically-optimal motion planning encode high-quality paths yet can be extremely dense. We consider the problem of sparsifying the roadmap, i.e. reducing its size, while minimizing the degradation of the quality of paths that can be extracted from the resulting roadmap. We present a simple, effective sparsifying algorithm, called roadmap sparsificationby edgecontraction (RSEC). The primitive operation used by RSEC is edgecontraction - the contraction of a roadmap edge (v',v'') to a new vertex v' and the connection of the new vertex v to the neighboring vertices of the contracted edge's vertices (i.e. to all neighbors of v' and v'). For certain scenarios, we compress more than 97% of the edges and vertices of a given roadmap at the cost of degradation of average shortest path length by at most 4%.

Original languageEnglish
Pages (from-to)1711-1725
Number of pages15
JournalInternational Journal of Robotics Research
Volume33
Issue number14
DOIs
StatePublished - 3 Dec 2014

Funding

FundersFunder number
7th Framework Programme for Research of the European Commission
FET-Open255827
National Science FoundationCCF-10-12254, CCF-09-40671, CCF-11-61359
Army Research OfficeW911NF-13-P-0018
Engineer Research and Development CenterW9132V-11-C-0003
German-Israeli Foundation for Scientific Research and Development1150-82.6/2011
United States-Israel Binational Science Foundation2012/229
Israel Science Foundation1102/11
Tel Aviv University

    Keywords

    • Motion control
    • design and control
    • manipulation
    • manipulation planning
    • mechanics
    • path planning for manipulators

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