Ricardo Gimenes
Polytechnic School - University of S˜ao Paulo, Av. Prof. Luciano Gualberto, trav. 3, n. 158
CEP 05508-010, S˜ao Paulo, Brazil
Daniel Castro Silva, Lu´ıs Paulo Reis, Eug´enio Oliveira
FEUP - DEI / LIACC, Rua Dr. Roberto Frias, s/n 4200-465, Porto, Portugal
Simulation Environments, Multi-Agent Systems, UAVs.
Developed countries have made significant efforts to integrate Unmanned Aerial Vehicle (UAV) operations in
controlled aerial space due to a rising interest of using UAVs for civilian as well as military purposes. This
paper focuses on looking for reliable solutions and a way to validate an autonomous multiagent control system
through a different variety of Flight Simulations. This study has two main lines, the first being the use of
multiagent systems in UAVs, aiming at a fully autonomous control. The second and focal line is a survey
about the variety of simulation systems dedicated to aerodynamics and aircrafts, comparing them and their
feasibility to validate the developed multiagent control system. One critical factor is hazard situations, like
emergency landing without runway or equipment failure, which should be predicted in an automated system.
Most flight simulators are not realistic enough to validate a multiagent algorithm in hazard situations. At the
same time, it is impossible to predict every type of failure in real world. The boundaries of simulation should
be very well enclosured in order to present results using simulation.
Ever since the construction of the first vehicles, men
havedreamed of automating its operations. While this
autonomy has been restricted to limited functions, it
is desirable to elevate it to a full scale, allowing vehi-
cles to be fully autonomous. When full autonomy is
reached, vehicles will have to cooperate and coordi-
nate their actions with one another, in order to ensure
both security and an optimal use of resources. The
choice of an agent-oriented approach to control au-
tonomous vehicles is intended to save both time and
resources, enabling the vehicles to communicate, and
make their own mission planning and real-time deci-
In order to better develop and test such agent-
oriented approaches and coordination methods, a sim-
ulation environment must be used. Such environment
also saves time and resources, since it would be pro-
hibitory to test these systems using real vehicles.
This paper focuses on several existing simulation
environments that can be adapted to serve the objec-
tive at hand.
1.1 Unmanned Aerial Vehicles
UAV, acronym for Unmanned Aerial Vehicle, is con-
sidered, in this paper, as a vehicle able to fly au-
tonomously without any external remote control.
There are already some examples of civilian UAV
usage, either operating or in developmental stage,
namely those related to surveillance or in need of ac-
cess to risky areas (Air & Space Europe, 1999). These
do not have significant influence on the air traffic sys-
tem, since the UAVs fly over clearly defined areas at
low altitude and speed.
The use of UAVs still involves a few uncertain-
ties and legal limitations as to where and how they
can be used. The majority of projects involving UAVs
are strictly military. Opposing to the military vision,
crewless civilian passenger aircraft raise a completely
diverse paradigm in terms of reliability. Because of
this, new hurdles come in the way of a future catego-
rization of passenger carrying UAV, as well as of non-
passenger carrying UAV sharing airspace with con-
ventional aircrafts.
Thus, operating UAV on international airspace
Gimenes R., Castro Silva D., Paulo Reis L. and Oliveira E. (2008).
In Proceedings of the Tenth International Conference on Enterprise Information Systems - SAIC, pages 243-246
DOI: 10.5220/0001710802430246
must entail two closely related conditions (Allouche,
2000): the UAV must be safe and reliable enough to
fly over densely populated areas and must be safely
operated through airspace.
1.2 Multiagent Systems
A multi-agent system (MAS) can be seen as a system
where entities are represented by independent agents,
which in turn communicate with each other, coordi-
nating their activities.
The UAV operation can surely be considered a
MAS in which every aircraft is an agent with its own
goals (destination, time frame of arrival, service stan-
dards, etc.), independent of the goals of other air-
craft. With this approach, it is possible to apply ne-
gotiation techniques, which will allow the aircraft to
cooperate in solving airspace conflicts. Answers in-
volving artificial intelligence software or MAS have
been researched on, in the search for decision-making
systems capable of avoiding aircraft encounters in a
given airspace (Lian and Deshmukh, 2006).
In section two of this paper, some of the require-
ments that were taken into account when evaluat-
ing the simulation environments are briefly described.
Section three presents some of the analyzed simula-
tors, game engines, flight simulators and middleware
engines. In the last section, some conclusions are
withdrawn and future work is outlined.
The choice of a simulation environment is dependent
on the objectives of the project. In this section, some
key features that may be desirable in the simulation
environment are briefly described. These features can
be grouped into categories, such as graphical, phys-
ical or openness characteristics, among others, for a
better analysis.
The openness of the software is an important as-
pect. Openness can be defined in terms of the possi-
bility to view and alter the source code, as well as ex-
pansion capabilities, such as the possibility to develop
add-ons, external modules and different agents that
can be linked to the environment, via defined APIs,
interaction models or communication protocols, the
accessibility and format of the data that can be input to
the environment, and the output data format as well.
Accurate physical simulation is very important,
when dealing with robotic mobile agents, such as
UAVs. The following subsections explain in more de-
tail the importance of a correct physical modeling and
simulation of the aircraft and the environment.
2.1 Specific Aviation-Related
Requirements in Simulated
Realistic simulation of robotic sensors and actuators
in a complex, unstructured, dynamic environment,
such as fluids, constitutes a research challenge. Most
simulators consider only the four basic vectors that
compose a flight: lift, weight, drag and thrust. (Schiff,
1978). However, in real flight, an aircraft has to deal
with numerous factors, not only pertaining to the air-
craft itself, but also external, environmental factors.
This way, to choose a flight simulator system, it is
very important to define what is more relevant in the
research. Each simulator has its characteristics suit-
able (or not) with a specific kind of research. Con-
sidering developing an autonomous multiagent envi-
ronment as the main goal, the relevant parameters in
the flight system can have three major lines: Accept-
able flight model, considering the application under
investigation; Flexibility to interact with the agent
via programming interfaces; Possibility to have the
flight model or simulated elements changed by exter-
nal software.
Agent algorithms which have to control an aircraft
will need a flight simulator model as real as possi-
ble. As the number of simulated elements offered by
the flight simulator increases, so does its adequacy to
serve as a suitable tool for simulated validation of the
agent-based UAVs.
2.2 Fault Injection in Flight Simulation
The firm safety requirements related with aviation re-
quire the studies of any kind of flight simulation to
keep in mind failure considerations. Fault injection is
a complex research line to reach a reliable and safe
flight model in flight simulation.
A fault injection method is essentially hardware
fault injection. In a flight simulation, faults can be in-
jected into different inputs or outputs of the aircraft.
Considering the UAV completely controlled by soft-
ware, the range of failure possibilities determines the
way the faults have to be injected. The quantity of
injected faults is a quality parameter of any fault in-
jection method. Basically, the fault injection module
is a software module which interrupts the original in-
puts and outputs of the simulated aircraft.
A critical problem for fault injection using COTS
ICEIS 2008 - International Conference on Enterprise Information Systems
flight simulators is how they are opened to interact
with their simulated hardware of an aircraft. Thus,
open source flight simulators or open API from pro-
prietary software should be analyzed as decisive fac-
tors in a flight environments choice.
2.3 UAV Flight Simulation
The way the flight simulation is developed determines
its possibilities and, consequentially, the possibilities
and limitations the research has to deal with. Real-
time visual simulation is not necessarily a relevant
parameter. Simulations without visual interface of-
fer the possibility to produce a quantity of different
situations quicker than the visual simulation does.
The simulation of physics, kinematics and aerody-
namics is fundamental in a reliable flight simulation.
In contrast, structural forces are not relevant in UAV
flight model studies.
There are two main simulator categories: Game En-
gines and Flight Simulators.
In game engines, the most important aspect is an
appealing visualization. Flight Simulators have the
main focus in the flight simulation itself. There is
some focus on the visual aspect as well, but the main
efforts are dedicated to aerodynamics and flight fac-
tors present in real world.
3.1 Game Engines
First-person games offer a possibility to researchers
play, test, and evaluate their robots with a different
range of sensors. An accessible 3D environment with
physics and kinematics opens new robotic research
perspectives. Conversely, when aerodynamics is the
main focus, game engines normally do not attend the
requirements (Lewis and Jacobson, 2002).
3.2 COTS Flight Simulators
There is a great variety of COTS Flight Simulators.
The graphics in modern flight simulators are very
realistic. However, scientific research has to worry
about the flight dynamics of an aircraft. Therefore, an
evaluation on a few low cost COTS flight simulators
is presented below.
3.2.1 Microsoft Flight Simulator
The current version of the Microsoft Flight Simula-
tor series (FSX) is probably the most realistic on the
market, in graphical terms, in part thanks to the use
of the new DirectX 10 technology. Its flight model
is based on a set of tables, and is independent of the
visual model (Stock, 2007). The failure-modeling in-
cludes equipment failure, but without variation of the
manner in which the equipment will fail.
FSX presents the SimConnect API, an FSUIPC-
like access to functions and variables, which allows
developers to create new add-ons to add or replace
functionalities, as well as monitor activities (Mi-
crosoft Corporation, 2006).
3.2.2 X-Plane
X-Plane uses the geometric approach and its model is
defined as structural. An engineering process called
”blade element theory” calculates the flight model,
breaking the aircraft down into many small elements
and then finding the forces on each element, which are
then converted into accelerations, velocities and po-
sitions (Laminar Research, 2007). X-Plane also has
failure-modeling, but as in FSX, it is not allowed to
vary the way the equipments fail (McManus et al.,
A creditability factor about X-Plane is its profes-
sional use and approval by the FAA for training to-
wards airline transport certificate. However, Stock
(Stock, 2007) compared X-Plane and FSX, and the re-
sults show how some conceptual flaws, and how they
have serious impressions in their models.
3.2.3 FlightGear
FlightGear is a free, open-source, multi-platform, co-
operative flight simulator development project, with
the goal of creating a sophisticated flight simulator
framework for use in research or academic environ-
ments, once it allows access to a very large number
of internal state variables (FlightGear, 2007). It al-
lows to remotely control FlightGear from an external
script and to use an external flight dynamics module
(including hardware autopilot) (Aeronautical Devel-
opment Agency, 2001).
FlightGear allows the users to choose between
three primary Flight Dynamics Models. More than
this, it is possible to add new models or even inter-
face to external flight dynamics models source, such
as from Matlab. The three available models are JS-
BSim, YASim and UIUC (based on LaRCsim origi-
nally written by NASA (Jackson, 1995)).
3.2.4 Piccolo
Piccolo is a known auto-pilot system for small air-
craft, by CloudCap Technology (CloudCap Technol-
ogy, 2007). The main product is commercialized as
hardware components that can be mounted on a small
aircraft, allowing autonomous flight. There is also
a software release of a simulation environment, for
software-in-the-loop tests. The graphical component
is minimal, but in terms of flight simulation it is very
realistic, simulating many of the forces involved in a
flight. It can also, by default, output its data to Mi-
crosoft Flight Simulator and FlightGear.
3.3 Dedicated Middleware Engines
There are hundreds of middleware engines which
could be used in scientific research. But, consider-
ing the UAV scenario controlled by multi-agents al-
gorithms, two are highlighted in this paper.
OpenFlight, MultiGen-Paradigm’s native 3D con-
tent, is the leading visual database standard in the
world and has become the standard format in the vi-
sual simulation industry (MultiGen-Paradigm, Inc.,
2007). AeroSim is a Matlab block library which pro-
vides components for development of nonlinear (with
six degrees of freedom) aircraft dynamic models (Un-
manned Dynamics, 2007).
Since the choice of an appropriate environment de-
pends on specific needs, no single environment is
elected, but the choices of a suitable environment
have been defined concerning specific characteristics
chosen according to the objective in sight.
Some conclusions can be withdrawn from the
analysis made to the simulating environments in the
previous section, where characteristics like simulation
of graphical presentation, kinematics, physical inter-
pretation of the object, weather simulation and sim-
ulation cycle method were considered. In terms of
software openness, each presents its advantages, FSX
presenting for the first time an open API, and other
products with their long-time known interfaces. This
flexibility allows researchers to choose the combina-
tion of simulator modules that best suits their inter-
ests, thus being able to focus on the development or
improvement of either the graphical content, the air-
planes’ model, or, in our case, the creation of auto-
mated aircraft able to communicate with one another
and coordinate actions.
A variety of software able to simulate dynamics
and graphical interfaces challenges the researchers in
order to choose the best solution to their investiga-
tions. Further works need to better related and param-
eterize the solutions in defined scientific parametersin
order to assist choices in new researches.
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