Title
Toward fault-tolerant multi-robot networks
Abstract
AbstractApplications based on groups of self-organized mobile robots are becoming pervasive in communication networks, monitoring, traffic, and transportation systems. Their advantage is the possibility of providing services without the existence of a previously defined infrastructure. However, physical agents are prone to failures that add uncertainty and unpredictability in the environments in which they operate. Therefore, a robust topology regarding failures is an imperative requirement. In this article, we show that mechanisms based solely on connectivity maintenance are not enough to obtain a sufficiently resilient network, and a robustness-oriented approach is necessary. Thus, we propose a local combined control law that aims at maintaining the overall network connectivity while improving the network robustness via actions that reduce vulnerability to failures that might lead to network disconnection. We demonstrate, from a theoretical point of view, that the combined control law maintains connectivity, and experimentally validate it under diverse failure distributions, from two perspectives: as a reactive and as a proactive mechanism. As a reactive mechanism, it was able to accommodate ongoing failures and postpone or avoid network fragmentation, including cases where failures are concentrated over short time spans. As a proactive mechanism, the network topology was able to evolve from potentially vulnerable with respect to failures to a more robust one. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 704, 388-400 2017
Year
DOI
Venue
2017
10.1002/net.21784
Periodicals
Keywords
Field
DocType
fault-tolerant networks,multicooperative robot control,adaptive networks,resilient systems,complex networks,multi-robot networks
Information system,Mathematical optimization,Software,Fault tolerance,Complex network,Robot,Mathematics,Distributed computing
Journal
Volume
Issue
ISSN
70
4
0028-3045
Citations 
PageRank 
References 
3
0.40
9
Authors
3
Name
Order
Citations
PageRank
Cinara Guellner Ghedini192.90
Carlos H. C. Ribeiro216934.25
Lorenzo Sabattini339336.65