This work presents a distributed method for control centers in a power
network to estimate the operating condition of the power plant, and to
ultimately determine the occurrence of threatening situations. State estimation
has been recognized to be a fundamental task for network control centers to
ensure correct and safe functionalities of power grids. We consider (static)
state estimation problems, in which the state vector consists of the voltage
magnitude and angle at all network buses.

The paper studies the visibility maintenance problem (VMP) for a
leader-follower pair of Dubins-like vehicles with input constraints, and
proposes an original solution based on the notion of controlled invariance. The
nonlinear model describing the relative dynamics of the vehicles is interpreted
as linear uncertain system, with the leader robot acting as an external
disturbance. The VMP is then reformulated as a linear constrained regulation
problem with additive disturbances (DLCRP).

This article presents a distributed algorithm for a group of robotic agents
with omnidirectional vision to deploy into nonconvex polygonal environments
with holes. Agents begin deployment from a common point, possess no prior
knowledge of the environment, and operate only under line-of-sight sensing and
communication. The objective of the deployment is for the agents to achieve
full visibility coverage of the environment while maintaining line-of-sight
connectivity with each other.

Distributed abstract programs are a novel class of distributed optimization
problems where (i) the number of variables is much smaller than the number of
constraints and (ii) each constraint is associated to a network node. Abstract
optimization programs are a generalization of linear programs that captures
numerous geometric optimization problems.

We consider the problem of sensor selection for time-optimal detection of a
hypothesis. We consider a group of sensors transmitting their observations to a
fusion center. The fusion center considers the output of only one randomly
chosen sensor at the time, and performs a sequential hypothesis test. We
consider the class of sequential tests which are easy to implement,
asymptotically optimal, and computationally amenable. For three distinct
performance metrics, we show that, for a generic set of sensors and binary
hypothesis, the fusion center needs to consider at most two sensors.