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Triangular Networks for Resilient Formations

  • David Saldaña
  • Amanda Prorok
  • Mario F. M. Campos
  • Vijay Kumar
Chapter
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 6)

Abstract

Consensus algorithms allow multiple robots to achieve agreement on estimates of variables in a distributed manner, hereby coordinating the robots as a team, and enabling applications such as formation control and cooperative area coverage. These algorithms achieve agreement by relying only on local, nearest-neighbor communication. The problem with distributed consensus, however, is that a single malicious or faulty robot can control and manipulate the whole network. The objective of this paper is to propose a formation topology that is resilient to one malicious node, and that satisfies two important properties for distributed systems: (i) it can be constructed incrementally by adding one node at a time in such a way that the conditions for attachment can be computed locally, and (ii) its robustness can be verified through a distributed method by using only neighborhood-based information. Our topology is characterized by triangular robust graphs, consists of a modular structure, is fully scalable, and is well suited for applications of large-scale networks. We describe how our proposed topology can be used to deploy networks of robots. Results show how triangular robust networks guarantee asymptotic consensus in the face of a malicious agent.

Notes

Acknowledgements

We gratefully acknowledge the support of the Colombian Innovation Agency (COLCIENCIAS), and the Brazilian agencies CAPES, CNPq, FAPEMIG. We also acknowledge the support of ONR grants N00014-15-1-2115 and N00014-14-1-0510, ARL grant W911NF-08-2-0004, NSF grant IIS-1426840, and TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • David Saldaña
    • 1
  • Amanda Prorok
    • 1
  • Mario F. M. Campos
    • 2
  • Vijay Kumar
    • 1
  1. 1.University of PennsylvaniaPhiladelphiaUSA
  2. 2.Universidade Federal de Minas GeraisBelo HorizonteBrazil

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