Paper by Erik D. Demaine

Reference:

James McLurkin and Erik D. Demaine, “A Distributed Boundary Detection Algorithm for Multi-Robot Systems”, in Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, Missouri, October 11–15, 2009, pages 4791–4798.

BibTeX

@InProceedings{BoundaryDetection_IROS2009,
  AUTHOR        = {James McLurkin and Erik D. Demaine},
  TITLE         = {A Distributed Boundary Detection Algorithm for Multi-Robot Systems},
  BOOKTITLE     = {Proceedings of the 2009 IEEE/RSJ International Conference on
                   Intelligent Robots and Systems (IROS 2009)},
  bookurl       = {http://www.iros09.mtu.edu/},
  ADDRESS       = {St.\ Louis, Missouri},
  MONTH         = {October 11--15},
  YEAR          = 2009,
  PAGES         = {4791--4798},

  doi           = {https://dx.doi.org/10.1109/IROS.2009.5354296},
  dblp          = {https://dblp.org/rec/conf/iros/McLurkinD09},
  comments      = {This paper is also available from <A HREF="http://dx.doi.org/10.1109/IROS.2009.5354296">IEEE</A>.},
  updates       = {The conjecture on page 4 (boundary error detection eventually lead to the true boundary) has been disproved: <A HREF="https://github.com/SwarmRoboticResearch/BachelorThesis">Maximilian Ernestus and Dominik Krupke's bachelor's thesis</A> gives a <A HREF="https://github.com/SwarmRoboticResearch/BachelorThesis/blob/master/figures/04_Boundaries/circlefalse.svg">counterexample</A>.},
  withstudent   = 1,
}

Abstract:

We describe a distributed boundary detection algorithm suitable for use on multi-robot systems with dynamic network topologies. We assume that each robot has access to its local network geometry, which is the combination of a robot's network connectivity and the positions of its neighbors measured relative to itself. Our algorithm uses this information to classify robots as boundary or interior in one communications round, which is fast enough for rapidly changing networks. We use the local boundary classifications to create a robust boundary subgraph, and to determine if the boundary is an interior void or the exterior boundary. A proof of the key property of the boundary detection algorithm is provided, and all the algorithms are extensively tested on a swarm of 25-35 robots in rapidly changing network topologies.

Comments:

This paper is also available from IEEE.

Updates:

The conjecture on page 4 (boundary error detection eventually lead to the true boundary) has been disproved: Maximilian Ernestus and Dominik Krupke's bachelor's thesis gives a counterexample.

Availability:

The paper is available in PDF (1954k).

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