  Teams of networked autonomous agents have been used in a number of
applications, such as mobile sensor networks and intelligent transportation
systems. However, in such systems, the effect of faults and errors in one or
more of the sub-systems can easily spread throughout the network, quickly
degrading the performance of the entire system. In consensus-driven dynamics,
the effects of faults are particularly relevant because of the presence of
unconstrained rigid modes in the transfer function of the system. Here, we
propose a two-stage technique for the identification and accommodation of a
biased-measurements agent, in a network of mobile robots with time invariant
interaction topology. We assume these interactions to only take place in the
form of relative position measurements. A fault identification filter deployed
on a single observer agent is used to estimate a single fault occurring
anywhere in the network. Once the fault is detected, an optimal leader-based
accommodation strategy is initiated. Results are presented by means of
numerical simulations and robot experiments.
