@inbook{dars18,

title = {Resilient Backbones in Hexagonal Robot Formations},

author = {David Saldaña and Luis Guerrero-Bonilla and Vijay Kumar},

isbn = {978-3-030-05816-6},

year  = {2018},

date = {2018-11-15},

urldate = {2018-11-15},

booktitle = {Distributed Autonomous Robotic Systems: The 14th International Symposium},

pages = {427--440},

address = {Cham},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

@inbook{Saldaña2018,

title = {Triangular Networks for Resilient Formations},

author = {David Saldaña and Amanda Prorok and Mario F. M. Campos and Vijay Kumar},

editor = {Roderich Groß and Andreas Kolling and Spring Berman and Emilio Frazzoli and Alcherio Martinoli and Fumitoshi Matsuno and Melvin Gauci},

url = {https://doi.org/10.1007/978-3-319-73008-0_11},

doi = {10.1007/978-3-319-73008-0_11},

isbn = {978-3-319-73008-0},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Distributed Autonomous Robotic Systems: The 13th International Symposium},

pages = {147--159},

publisher = {Springer International Publishing},

address = {Cham},

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.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}