An Overview of Swarm Robotics: Swarm Intelligence Applied to Multi-robotics

Abstract

As an emergent research area by which swarm intelligence is applied to multi-robot systems; swarm robotics (a very particular and peculiar sub-area of collective robotics) studies how to coordinate large groups of relatively simple robots through the use of local rules. It focuses on studying the design of large amount of relatively simple robots, their physical bodies and their controlling behaviors. Since its introduction in 2000, several successful experimentations had been realized, and till now more projects are under investigations. This paper seeks to give an overview of this domain research; for the aim to orientate the readers, especially those who are newly coming to this research field. General Terms Swarm robotics, swarm intelligence, multi-robot systems.

Full text

International Journal of Computer Applications (0975 – 8887)
Volume 126 – No.2, September 2015
31
An Overview of Swarm Robotics: Swarm Intelligence
Applied to Multi-robotics
Belkacem Khaldi, Foudil Cherif
Department of Computer Science.
LESIA Laboratory,
University of Biskra, Algeria.
ABSTRACT
As an emergent research area by which swarm intelligence is
applied to multi-robot systems; swarm robotics (a very
particular and peculiar sub-area of collective robotics) studies
how to coordinate large groups of relatively simple robots
through the use of local rules. It focuses on studying the
design of large amount of relatively simple robots, their
physical bodies and their controlling behaviors. Since its
introduction in 2000, several successful experimentations had
been realized, and till now more projects are under
investigations. This paper seeks to give an overview of this
domain research; for the aim to orientate the readers,
especially those who are newly coming to this research field.
General Terms
Swarm robotics, swarm intelligence, multi-robot systems.
Keywords
Swarm robotics applications, swarm robotics simulators,
swarm robotics problems classification.
1. INTRODUCTION
Inspired from the complex behaviors observed in natural
swarm systems (e.g., social insects and order living animals),
swarm intelligence (SI) is a new field that aims to build fully
distributed de-centralized systems in which overall system
functionality emerges from the interaction of individual
agents with each other and with their environment. As a
result to try applying the insight gained from this domain
research into multi-robotics, an emerging research area called
swarm robotics (SR) has been issued.
SR is the study of how to coordinate large groups of relatively
simple robots through the use of local rules. It focuses on
studying the design of large amount of relatively simple
robots, their physical bodies and their controlling behaviors
[1]. SR is closely related to the idea of SI and it shares its
interest in self-organized decentralized systems. Hence, it
offers several advantages for robotic applications such as
scalability, and robustness due to redundancy [2].
This paper seeks to give an overview of SR for the aim to
orientate the readers, especially those who are newly coming
to this research field, the paper highlights the grand lines of
the different main focuses areas in this domain research.
In the upcoming sections, we introduce in section 2 the SI as
an emergent research domain inspired from nature swarms,
followed by overviewing Multi-robot systems (MRS) in
section 3. In section 4 we introduce SR as an application of SI
technics to MRS. The remaining sections (section 5 to section
8) are for more details about SR, these sections involve:
definition of SR and its features, its potential applications in
real world, the classification of the problems being focused
on, and finally exploring some real successful projects and
simulations being realized in real experimentation.
2. SWARM INTELLIGENCE
Who among us haven’t been amazed by the individually
simple but collectively complex behavior exhibited by natural
grouping systems including social insects such as: ant’
colonies, termites, bees, wasps …etc., and high order living
animals such as: flocks of birds, fish schooling, and packs of
wolves …etc.? Inspired by the robustness, scalability, and
distributed self-organization principles observed in these
amazing natural collective complex behaviors emerged from
individual simple local interactions rules, an attempt to apply
the insight gained through this research to artificial systems
(e.g., massively distributed computer systems and robotics)
has given rise to a new research topic called (SI) [3]. This
increasing domain research that is firstly introduced in the
context of cellular robotic systems by Beni and Wang [4] is
considered as a sub-field of artificial intelligence based around
on the study of collective behaviors in de-centralized, self-
organized systems [5]. Although there is no a specific
definition for swarm intelligence, we adopt heir the one
denoted by Dorigo & Birattari [6]:
‘The discipline that deals with natural and artificial systems
composed of many individuals that coordinate using de-
centralized control and self-organization. In particular, the
discipline focuses on the collective behaviors that result from
the local interactions of the individuals with each other and
with their environment’.
So, a swarm intelligence system consists typically of a
population of relatively simple agents (relatively homogenous
or there are a few types of them [6]) interacting only locally
with themselves and with their environment, without having a
global knowledge about their own state and of the state of the
world. Moreover, the overall observed behavior is emerged in
response to the local environment and to local interactions
between the agents that follow often very simple rules [7].
Natural swarm based theories have been applied to solve
analogous engineering problems in several domains
engineering from combinatorial optimization to rooting
communication network as well as robotics applications, etc.
(for a recent comprehensive review, readers can refer to [8]).
The most well-known swarm based algorithms are: Ant
Colony Optimization Algorithms (ACO), Particle Swarm
Optimization Algorithms (PSO), Artificial Fish Swarm
Algorithm (AFSA) and Bee based Algorithms. The ACO
algorithm is inspired from the foraging behavior of ant
colonies in finding shortest paths from their nests to food
sources. The source of inspiration of PSO based algorithms
comes especially from the behavior observed in bird flocking
or fish schooling when they are moving together for long

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32
distances to search for food sources, whereas The AFSO
algorithm is inspired from the collective movement observed
in the different behaviors exhibited by fishes such as
searching for food, following other fishes, protecting the
group against dangers and stochastic search [9]. Bee based
algorithms can be classified into three different main groups:
(1) the honeybee' foraging behavior based algorithms, (2) the
ones based on mating behavior in honeybee, and (3) the queen
bee evolution process based algorithms (more details can be
find in [10].
3. MULTI-ROBOT SYSTEMS (MRS)
Multi-robot systems (MRS) are born to overcome the lack in
information processing capability and many other aspects of
single robots that are not capable to dial with special tasks;
which, in order to be efficiently completed, need cooperation
and collaboration between groups of robots [11]. Since its
introduction in the late 1980s, various works (such as: cellular
robotics, collective robotics, and distributed robotics) have
been issued to describe group of simple physical robots
collaborating together to perform specific tasks. MRS have
also achieve a great success and made a great progress in
many areas such as cooperative transportation and
aggregation, environmental monitoring, search-and-rescue
missions, foraging, and space exploration [12].
In such task; even the simplicity in design and the low-cost in
productivity, as well as the increase in capabilities, flexibility,
and fault tolerance advantages gained when using multi-robots
instead of a single one; however with the new arising
challenges such as decentralization in control and self-
organization, researchers in multi robotic field begun to make
attention to the increase progress known in swarm intelligence
systems giving birth to the new sub-domain research “swarm
robotics”.
4. SWARM ROBOTICS (SR)
Swarm robotics is a very particular and peculiar sub-area of
collective robotics in which swarm intelligence techniques are
applied. The 2000-year has witnesses the first project “swarm-
bot” [13] that has been marked as the real period of the
development of swarm robotics. Marco Dorigo, the inventor
of ACO algorithm; shared this project for the aim to study
new approaches to the design and implementation of self-
organizing and self-assembling artifacts.
Marco Dorigo and Erol Sahin [13],[14] ones of the founders
of swarm robotics gave a definition to this research domain as
follow: ‘Swarm robotics can be loosely defined as the study of
how collectively intelligent behavior can emerge from local
interactions of a large number of relatively simple physically
embodied agents’. The main idea of the approach behind this
domain research is to build relatively many small and low-
cost robots that are supposed to accomplish the same task as a
single complex robot or a small group of complex robots [15].
The approach also takes into account studying the design of
robots (both their physical body and their controlling
behaviors) in a way that a desired collective behavior emerges
from the inter-robot interactions and the interactions of the
robots with the environment [16].
Further; as the key properties (pointed out in [2]) of a typical
SI system can be applied to ether MRS and SRs; a set of
criteria has been highlighted by Dorigo and Shahine [17] to
overcome the confusions raised about the use of the term
“swarm” and the overlapping meanings applied to multi-robot
research. Dorigo and Shahin’ set criteria; which are not meant
to be used as a checklist, rather they help evaluating the
degree to which SR might be applied and how it might be
different from other MRSs; are described as follow [12]:
 Autonomy: the swarm-robotic system is made up of
autonomous robots that are able to physically interact
with the environment and affect it.
 Large number: The swarm-robotic system should be
consisted of limited homogeneous groups of robots in
which each group contains of large number of
members. Hence, highly heterogeneous robot groups
tend to fall outside swarm robotics).
 Limited capabilities: the SR system is composed of
robots relatively incapable or inefficient to carry out
tasks on their own but they are highly efficient when
they cooperate.
 Scalability and robustness: A swarm-robotic system
should be scalable and robust. Increasing the number
of unites will improve the performance of the overall
system and on the other hand, reducing some units
will not yield to a breakdown of the system.
 Distributed coordination: in SR, the coordination
between robots is distributed; each robot should only
have local and limited sensing and communication
abilities.
Based on these criteria; SRS are more beneficent than MRS
which might be used whenever several robotic platforms are
applied to achieve a mission. The main benefits when using
SR reside on [15]: (1) the robustness feature: explained by the
coherency of the whole system when losing some robots; this
can gain us money investment in hundreds of small swarm
robots, rather than investing the same amount of money or
greater in a single complex robot that can leads to the failure
of the all over project if a single failure is persisted. (2) The
flexibility feature: enlightened by rather needing a hardware
reconfiguration of complex robots to accomplish a task, the
same task is achieved by coordinated swarm robots that are
not essentially personalized to a given task. (3) The
scalability feature described by the fact that relying only on
local information; a swarm robotic algorithm can be applied
unchanged to a group of any (reasonable) size. The table
below as it’s deducted from [18] summarizes these critters of
differentiation:
Table 1. Comparison of SR and MRS
Swarm robotics
MRS
Population Size
Variation in great range
Small
Control
Decentralized and
autonomous
Centralized/
remote
Homogeneity
Homogeneous
heterogeneous
Flexibility
High
Low
Scalability
High
Low
Environment
Unknown
Known/unknown
Motion
Yes
Yes
SI techniques as ACO and PSO can be used as a control
algorithm for distributed robot swarms, but a good problem-
solving system does not have to be biologically relevant.
However, the remarkable success of social insects in surviving
and colonizing our planet can serve as a starting point for new
metaphors in engineering and computer science [16].

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5. POTENTIAL APPLICATION OF
SWARM ROBOTICS
Since the emergent of swarm robotics research field, several
works have been issued to explain how we can benefit from
the properties of swarm robotics systems that make them
appealing in several potential application domains. Swarm
robotics have been involved in many tasks [1] such as the
ones demanding miniaturization, like distributed sensing tasks
in micro-machinery or the human body; those demanding
cheap designs, such as mining task or agricultural foraging
task; those requiring large space and time cost, and are
dangerous to the human being or the robots themselves, such
as post-disaster relief, target searching, military applications,
etc. Refers to Ying TAN and Zhong-yang ZHENG [1],[18],
swarm robotics is mostly used in:
5.1 Tasks covering large area:
Swarm robotics can be applied in tasks that require a large
region of space. Heir; the robots are specialized for large
coverage tasks (e.g. surveillance, demining, and search and
rescue) and they are distributed in an unstructured or large
environment (e.g. underwater or extraterrestrial planetary
exploration) in which no available infrastructure can be used
to control the robots. In such tasks, robot swarms are well-
matched because they are able to: act autonomously without
the need of any infrastructure or any form of external
coordination, detect and monitor the dynamic change of the
entire area, locate the source, move towards the area and take
quick actions. Moreover the robots, in such urgent situation,
can aggregate into a patch in order to block the source as a
temporary solution.
5.2 Tasks dangerous to robot:
In several dangerous tasks such as mine rescue and recovery,
robots may be irretrievable after the task is accomplished;
thus, it’s economically acceptable to use swarm robotics with
simple and cheap individuals rather than using complex and
expensive robots. Moreover it’s reasonably tolerable to apply
swarm robots that provide redundancy for dealing with such
dangerous tasks.
5.3 Tasks require scaling population and
redundancy:
Swarm robotics can be also applied in situations in which it is
difficult or even impossible to estimate in advance the
resources needed to accomplish tasks such as search and
rescue, tracking, and cleaning. An example for this situation
is: clearing oil leakage after tank accidents; heir at the
beginning of the task the population of swarm is highly
maintained when the oil leaks fast and it’s gradually reduced
when the leak source is plugged and the leaking area is almost
cleared. The solution needed in these cases should be scalable
and flexible; therefore a robot swarm could be an appealing
solution: robots can be added or removed in time without any
significant impact on the performance to provide the
appropriate amount of resources and meet the requirements of
the specific task. This can be respected by the robustness
feature of swarm robotics that is the main benefits from
redundancy of the swarm.
6. SWARM ROBOTICS PROBLEMS
FOCUS
In the last decade, swarm robotics researches has known a
significant progress due to the advantages gained when using
such technology to solve many problems that are beyond the
capabilities of classical multi-robots systems. The problems
involves in swarm robotics research can be classified into [1]:
those mainly based on the patterns (e.g. aggregation,
cartography, migration, self-organizing grids, deployment of
distributed agents and area coverage); those focused on the
entities in the environment (e.g. Searching for the targets,
detecting the odor sources, locating the ore veins in wild field,
foraging, rescuing the victims in disaster areas and); and those
mostly hybrid of the two previous problems (e.g. cooperative
transportation, demining, exploring a planet and navigating in
large area).
[19] Illustrates another classification of the problems involved
in swarm robotics based on the collective behavior problems
focus. In (Table 2) we summaries his study basing on giving: a
short definition of the problem to be solved, its source of
inspiration, the approaches used to model the problem,
examples of the current researches that belongs to the problem,
and finally the classification of the problem.
7. INVOLVED PROJECTS AND
SIMULATIONS
7.1 Swarm robotics involved projects
From the emergent of swarm robotics as a novel research
domain, several successful projects have been created in order
to face the challenges raised in this area of research. The most
but not list known projects are presented in (Table 3). The list
is not exhaustive for all the available projects but it shows the
most used swarm robots platforms.
7.2 Swarm robotics simulation platforms:
Using plenty of physical robots in swarm robotics researches
is hardly difficult to afford; thus computer simulations are
developed to visually test the structures and algorithms on
computer before engaging in real physical robots tests. The
use of computer simulations; which are generally easier to
setup less expensive, normally faster and more convenient to
use than physical swarms; is often very useful to perform prior
to the investigation of real robots. In the section below we
highlight the well-known widely used simulation platforms in
swarm robotics researches.
7.2.1 Player/stage
Player/stage
1
is a combined package of free Software tools for
robot and sensor applications developed by the international
team of robotics researchers under the GNU license.
Player component is one of the most widely used robot
control interface in the world that provides a network interface
to a variety of robot and sensor hardware. The control of
robots can be programmed throw multi-programming
language that can be run in any computer with a network
connection to the robot. Stage component is a multiple robot
simulator interfaced to Player, it simulates a population of
mobile robots moving in and sensing a two-dimensional 2D
bitmapped environment.
7.2.2 Gazebo
Gazebo
2
is a simulator that extends Stage for 3D outdoor
environments. It includes an accurate simulation of rigid-body
physics; hence the both realistic sensor feedback and possible
interactions between objects can be then generated. Gazebo
presents a standard Player interface in addition to its own
1
http://playerstage.sourceforge.net
2
http://gazebosim.org/

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native interface. In this way, the controllers written for Stage
can be used in Gazebo and vice-versa.
7.2.3 UberSim
The UberSim
3
is a simulator developed at Carnegie Mellon
for a rapid validation before loading the program to real robot
soccer scenarios. UberSim uses ODE physics engine for
realistic motions and interactions. Although originally
designed for Soccer robots, the custom robots and sensors can
be written in C in the simulator and the program can be
3
http://www.cs.cmu.edu/~robosoccer/ubersim
uploaded to the robots using TCP/IP.
7.2.4 USARSim
USARSim
4
, shorted for Unified System for Automation and
Robot Simulation, is a high fidelity multi-robot simulator
originally developed for search and rescue (SAR) research
activities of the Robocup contest. It has now become one of
the most complete general purpose tools for robotics research
and education. It is built upon a widely used commercial game
engine, Unreal Engine 2.0. The simulator takes full advantage
4
http://sourceforge.net/apps/mediawiki/usarsim/
Table 2. Classifications of problems being studied in Swarm robotics
Problem to be solved
Sources of inspiration
Modeling approaches
Research literatures samples
(for more details refers to
[19])
Problem
classification
Aggregation
- Clustering swarm robots in a
region of the environment.
- Nature (e.g. Aggregation
bacteria, cockroaches, bees, fish
and penguins).
- Probabilistic finite
state machines.
- Artificial evolution.
- [20] and [21]
Spatially organizing behaviors
Pattern formation
- Deploying robots in a regular
and repetitive manner from
which specific distances are
kept between each other in
order to create a desired
pattern.
- Biology (e.g. the spatial
disposition of bacterial colonies
and the chromatic patterns on
some animals).
- Physics (e.g. molecules
distribution and crystal
formation).
- Virtual physics-based
design.
- [22]
Chain formation
- Auto-Positioning robots to
connect into two points. The
chain that they form can then
be used as a guide for
navigation or for
surveillance.
- Foraging ants.
- Probabilistic finite
state machines.
- Virtual physics based
design.
- Artificial evolution.
- [23] and [24]
Self-assembly and
morphogenesis
- Connecting physically swarm
robots to each other to create
structures (morphologies).
- Ants (bridges, rafts, walls…).
- Probabilistic finite
state machines.
- Artificial evolution.
- [25]
Collective exploration
- Social animals (ants, bees…).
- Probabilistic finite
state machines.
- Virtual physics-based
design.
- Network routing.
- [26] and [27]
Navigation behaviors
Coordination motion
- Moving in formation
similarly to schools of fish or
flocks of birds.
- Flocking in-group of birds.
- Schooling in group of fish.
- Virtual physics-based
design.
- Artificial evolution.
- [28]
Collective transport
- Cooperating in order to
transport an object.
- Cooperative carry prey in ant
colonies.
- Probabilistic finite
state machines.
- Artificial evolution.
- [29]
Consensus achievement
- Reaching consensus on one
choice among different
alternatives.
- Ants’ decision between the
shorter of two paths using
pheromones.
- Bees’ decision between the best
foraging area and the best nest
location.
- Aggregation in Cockroaches.
- Direct
communication.
- Indirect
communication.
- [30] and [31]
Collective decision making
Task allocation
- Auto-distribution of swarm
robots over different tasks.
To maximize the
performance of the system.
- Task allocation in ant and bee
colonies.
- Probabilistic finite
state machines.
- [32]

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of high accuracy physics, noise simulation and numerous
geometrics and models from the engine. Evaluations have
shown that USARSim can simulate the real time robots well
enough for researchers due to the high fidelity physics engine.
7.2.5 Enki
Enki
5
is an open source fast 2D physics based robot simulator
5
http://home.gna.org/enki/
written in C++. It is able to simulate the robot swarms
hundred times faster on the desktop computer than real-time
robots. It is also able to simulate the kinematics, collision,
sensors and cameras of robots working on a flat surface. Enki
is built to support several existing real robot systems,
including swarm-bots and E-puck, while user can customize
their own robots into the platform.
Table 3. Some of successful Swarm Robotics Projects
Project
Objective
Prototype
Open-source micro-robotic
Project
University of Stuttgart, Sergey
Kornienko and University of
Karlsruhe, Marc Szymanski,
Ramon Estane
http://www.swarmrobot.org
Develop a cheap, reliable and swarm-capable micro-robot that can be
easily reproduced even at home. This robot allows building a large-scale
swarm system (100 and more robots) to investigate artificial self-
organization, emergent phenomena, and control in large robotic groups
and so on. This research is important to understand underlying principle
of information and knowledge processing, adaptation and learning for the
design and development of very limited autonomous systems.
Jasmine
Cost: £80
Sensor: distance, light, bearing
Motion/Speed: wheel , N/A
Size: 3 cm
Autonomy: 1-2 h
Swarm-bots project
IRIDIA, Université Libre de
Bruxelles
www.swarm-bots.org
The project explores the design, implementation and simulation of self-
organizing and self-assembling artifacts. The project after it was
successfully completed in 2005; it has been extended by the swarmanoid
project, a project that proposes a highly innovative way to build
robots that can successfully and adaptively act in human made
environments. The swarm-bot prototype has been also used in e-swarm
project
swarm-bot
Cost: N/A
Sensor: range, bearing, camera,
bump, light
Motion/Speed: wheel, 50 cm/s
Size: 12.7 cm
Autonomy: 3 h
E-puck education robot
École Polytechnique Fédérale De
Lausanne EPFL
http://www.e-puck.org
The project develops a miniature mobile robot for education use. The
robots have several features specialized for such purpose. The robots
have a clean mechanical structure simple to understand, operate and
maintain. The robots are cheap and flexible, and can cover a large
spectrum of educational activities thanks to a large potential in sensors,
processing power and extension
e-puck
Cost: £580
Sensor: distance, camera,
bearing, accelerometer, mic
Motion/Speed: wheel, 13 cm/s
Size: 7.5 cm
Autonomy: 1-10 h
R-one project
Multi-Robot Systems Lab, Rice
University
http://mrsl.rice.edu/projects/r-one
The project aims to provide an advanced low-cost mobile robots designed
for research, teaching and outreach, the developed robots was
successfully implicated in several projects such as multi robot
manipulation, distributed approach for exploring and triangulating an
unknown region, and distributed boundary detection.
R-one
Cost: N/A
Sensor: distance, light, bump,
accelerometer, IR, localization
Motion/Speed: wheel,25 cm/s
Size:11 cm
Autonomy: 4 h
Kilobot project
School of Engineering and
Applied Sciences
Wyss Institute for Biologically
Inspired Engineering
Harvard University
http://www.eecs.harvard.edu/ssr/
projects/progSA/kilobot.html
The project aims to design a robot system for testing the collective
algorithms with a population of hundreds or thousands of robots. Each
robot is made of low-cost parts and takes 5 min to be fully assembled.
The system also provides several overall operations for a large swarm,
such as updating programs, powering on, charging all robots and
returning home
Kilobot
Cost: £12
Sensor: distance, light
Motion/Speed: vibration, 1 cm/s
Size: 3.3 cm
Autonomy: 3-24 h
Khepera III robot
K-Team Corporation
http://www.k-team.com/mobile-
robotics-products/khepera-iii
Produced by K-Team corporation, the robot provides a new standard tool
for robotic experiments and demonstrations such as: artificial
intelligence, navigation, multi-Agents System, real-time programming,
control collective behavior, and advanced electronics demonstration.
Khepera III
Cost: N/A
Sensor: range, bearing, camera,
bump, IR, ultrasonic, light
Motion/Speed: wheel, 50 cm/s
Size: 13x7 cm
Autonomy:8 h
Colias robot
Computational Intelligence Lab
(CIL), School of Computer
Science, University of Lincoln
http://colias.uk/index.htm
The robot developed in this project has been designed as a complete
platform with supporting software development tools for robotics
education and research. The robot is a low-cost, open-platform,
autonomous micro-robot for robotic swarm applications. It has been
tested in both individual and swarm scenarios, and the observed results
demonstrate its feasibility for use as a micro-sized mobile robot and as a
low-cost platform for robot swarm applications.
Colias
Cost: £25
Sensor: distance, light, bump,
bearing, range
Motion/Speed: wheel, 35 cm/s
Size: 4 cm
Autonomy: 1-3

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7.2.6 Webots
Webots
6
is a development environment used to model,
program and simulate the mobile robots available for more
than 10 years. With Webots, the user can design the complex
robotic setups, with one or several, similar or different robots
with a large choice of pre-defined sensors and actuators. The
objects in the environment can be customized by the user.
Webots also provides a remote controller for testing the real
robots. Until now, Webots robot simulator has been used in
more than 1018 universities and research centers in the
worldwide.
7.2.7 Breve
Breve
7
is a free, open-source software package, which makes
it easy to build 3D simulations of multi-agent systems and
artificial life. Behaviors and interactions of agents are defined
using Python. Breve uses ODE physics engine and OpenGL
library that allows the observers to view the simulation in the
3D world from any position and direction. Users can interact
at run time with the simulation using a web interface. Multiple
simulations can interact and exchange individuals over the
network.
7.2.8 V-REP
V- REP
8
is an open source 3D robot simulator that allows
creating entire robotic systems, simulating and interacting
with dedicated hardware. V-REP is based on distributed
control architecture: each object/model can be individually
controlled via an embedded script, a plugin, a remote API
client, or a custom solution. In V-REP, Controllers can be
written in C/C++, Python, Java, Lua, Matlab, Octave or Urbi
and can be directly attached to the objects in the scene and run
simultaneously in both threaded and non-threaded fashions.
This makes it very versatile and ideal for multi-robot
application. V-REP is used for fast algorithm development,
factory automation simulations, fast prototyping and
verification, robotics related education, remote monitoring,
safety double-checking, etc.
7.2.9 ARGoS
ARGoS
9
was the official simulator of the Swarmanoid
project; it is currently the main robot simulation tool for many
European projects. ARGoS is a new pluggable, multi-physics
engine for simulating the massive heterogeneous swarm
robotics in real time. Contrary to other simulators, every entity
in ARGoS is described as a plug-in one and easy to implement
and use. In this way, the multiple physics engines can be used
in one experiment, and the robots can migrate from one to
another in a transparent way. Results have shown that ARGoS
can simulate about 10 000 wheeled robots with full dynamics
in real-time. ARGoS is also able to be implemented in parallel
in the simulation.
7.2.10 TeamBots
TeamBots
10
is a collection of Java applications and java
packages for multi-agent mobile robotics research. The
simulation environment is fully based on java, however some
execution on mobile robots sometimes requires low-level
libraries in C. It supports the prototyping, simulation and
6
http://www.cyberbotics.com/
7
www.spiderland.org/breve/
8
http://www.coppeliarobotics.com/
9
http://iridia.ulb.ac.be/argos/
10
www.teambots.org
execution of multi-robot control systems; which might be run
either in simulation using the TBSim simulation application,
or on mobile robots using the TBHard robot execution
environment.
7.2.11 MORSE
MORSE
11
is a Blender Game Engine based simulator
designed to provide a realistic 3D simulation of small to large
environments, indoor or outdoor, with the ability to
simulate one to tenths of autonomous robots. It comes
with a set of robots base model (such as quadrotors,
ATRV, Pioneer3DX, generic 4 wheel vehicle, PR2,...),
with the possibility to add new ones.
8. SUMMARY
Swarm robotics is a relatively new research area that takes its
inspiration from swarm intelligence and robotics. It is the
result of applying swarm intelligence technics into multi-
robotics. Although a number of researches have been
proposed, it’s still quite far for practical application. In the
present paper, an overview of swarm robotics has been given
for better understanding of this multi-robot domain research
and for clarifying the grand lines being focused on this
domain. Interests that are newly coming to this topic research
can be easily guided throw the different sections presented in
this paper.
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