Finding a Needle in an Exponential Haystack: Discrete RRT for Exploration of Implicit Roadmaps in Multi-robot Motion Planning

  • Kiril Solovey
  • Oren Salzman
  • Dan Halperin
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 107)


We present a sampling-based framework for multi-robot motion planning which combines an implicit representation of a roadmap with a novel approach for pathfinding in geometrically embedded graphs tailored for our setting. Our pathfinding algorithm, discrete-RRT (dRRT), is an adaptation of the celebrated RRT algorithm for the discrete case of a graph, and it enables a rapid exploration of the high-dimensional configuration space by carefully walking through an implicit representation of a tensor product of roadmaps for the individual robots. We demonstrate our approach experimentally on scenarios of up to 60 degrees of freedom where our algorithm is faster by a factor of at least ten when compared to existing algorithms that we are aware of.



We wish to thank Glenn Wagner for advising on the M* algorithm and Ariel Felner for advice regarding pathfinding algorithms on graphs. We note that the title “Finding a Needle in an Exponential Haystack” has been previously used in a talk by Joel Spencer in a different context.


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Blavatnik School of Computer ScienceTel-Aviv UniversityTel AvivIsrael

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