The Magazine of IEEE-Eta Kappa Nu October 2017 - 17

Connectivity in Networked Unmanned Aerial Vehicles

I. INTRODUCTION
With the steady arrival of smaller more powerful
embedded computers at reduced costs, Unmanned
Aerial Vehicles (UAVs) have recently emerged as an
inexpensive way to perform a wide variety of tasks,
ranging from power line inspection in commercial
applications [1], [2], [3], to intelligence, surveillance and
reconnaissance (ISR) missions in military operations [4],
[5], [6]. Many of these tasks are well suited for a network
of UAVs, which provides greater flexibility, redundancy,
efficiency, and overall operational capability over a
single UAV. For example, a network of UAVs can share
information on previously explored areas in a surveillance
mission, reducing the overlap of areas already searched
and further expanding the search area. If some of the
UAVs in the network fail due to a battery shortage or
unforeseen breakdown of software or hardware systems,
the networked UAVs can be reconfigured to compensate
for the loss or system failure.
In a team of cooperating UAVs, wireless communication
plays a pivotal role in the efficiency and successful
completion of distributed tasks, where shared
information is necessary for the cooperation and
coordination of UAVs. However, due to environmental
obstacles and mission needs for UAVs to move beyond
the wireless communication range of other units,
consistent and stable communications often fail. For this
reason, cooperative control techniques that strongly rely
on multi-hop communication for distributed consensus
must take into consideration the communication network
topology for reliable performance and convergence. In
these circumstances, the radio signal strength can be
used for the guidance and control of UAV positioning to
ensure network connectivity.
A common approach to topology control of networked
UAVs uses a graph, where vertices represent individual
UAVs and edges the communication capability between
UAVs, i.e. there is an edge between two UAVs, if they
are within a communication range of each other
characterized by a pre-specified communication model.
In this context, network connectivity is defined as the
ability to successfully transmit information between
any pair of nodes in the graph. A primary research
area for cooperative control focuses on exploiting
local information received from neighboring UAVs for
rendezvous, flocking or formation. The goal of rendezvous
is for UAVs to reach consensus on a single location and
the time, while the goal of flocking is for UAVs to match
THE BRIDGE // Issue 3 2017

velocities with their neighbors. Maintaining connectivity
through formation control, which is the subject matter of
this article, focuses on keeping a fixed relative distance
between UAVs in the network.
Formation control of networked UAVs generally fall into
two categories: leader-based and leaderless. Leaderbased techniques either designate some UAVs as leaders
or define virtual leaders that neighboring UAVs use as
a reference. In leader-based formation, maintaining
network connectivity is reduced to ensuring all existing
communication connections are preserved throughout a
mission. Leaderless approaches, on the other hand, are
typically based on the algebraic connectivity of the graph
[7] corresponding to the network topology. The algebraic
connectivity, or sometimes called Fiedler value [7], is
a connectivity metric based on the second smallest
eigenvalue of a matrix that describes the connectivity
of networked entities, called the Laplacian matrix [8].
The Laplacian matrix of a graph captures the network
structure while the corresponding Fiedler value provides
a measure of information exchange capability of the
networked UAVs.
In general, there are three main existing approaches
to control network connectivity: (1) optimization,
(2) feedback and (3) hybrid. Optimization based
approaches maximize the Fiedler value by applying
non-increasing weights to the edges of the graph [9],
[10], [11], [12]. Optimization techniques are well suited
for UAV rendezvous missions, but are not appropriate
for formation as maximizing the Fiedler value will
continuously bring the team of UAVs together which
may violate the desired spatial separation. The common
approach in network feedback connectivity control is to
design a closed loop system, where the input to the
system is defined as the gradient of some potential
field that treats connectivity violation as an obstacle.
The network feedback connectivity approach ensures
all edges present in an initially connected network
are preserved for all time [13], [14], [15], [16]. Many
of the recent methods that focus on maintaining
communication connectivity utilize a variation of this
idea. In the hybrid feedback approach, UAVs decide if
edges may be deleted based on the network topology
which give UAVs more freedom in control objectives
such as exploration or coverage. A review of these
approaches can be found in [17].
While there has been a considerable amount of research
regarding the connectivity maintenance of cooperative
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