The SWARM-BOTS Project
Marco Dorigo1, Elio Tuci1, Roderich Groß1, Vito Trianni1,
Thomas Halva Labella1, Shervin Nouyan1, Christos Ampatzis1,
Jean-Louis Deneubourg2, Gianluca Baldassarre3, Stefano Nolfi3,
Francesco Mondada4, Dario Floreano4, and Luca Maria Gambardella5
1IRIDIA, Universit´ e Libre de Bruxelles, Belgium
2CENOLI, Universit´ e Libre de Bruxelles, Belgium
3Institute of Cognitive Science and Technology, CNR, Rome, Italy
4ASL,´Ecole Polytechnique F´ ed´ erale de Lausanne, Switzerland
5IDSIA, Lugano, Switzerland
Abstract. This paper provides an overview of the SWARM-BOTS
project, a robotic project sponsored by the Future and Emerging Tech-
nologies program of the European Commission. The paper illustrates the
goals of the project, the robot prototype and the 3D simulator we built.
It also reports on the results of experimental work in which distributed
adaptive controllers are used to control a group of real, or simulated,
robots so that they perform a variety of tasks which require cooperation
This paper introduces and illustrates the theoretical underpinning and the re-
search agenda of the SWARM-BOTS project, a robotic project sponsored by
the Future and Emerging Technologies program of the European Commission
(IST-2000-31010). The aim of this project is the development of a new robotic
system, called a swarm-bot, based on swarm robotics techniques.
Swarm robotics is an emergent field of collective robotics that studies robotic
systems composed of swarms of robots tightly interacting and cooperating to
reach their goals . Swarm robotics finds its theoretical roots in recent studies
of animal societies, such as ants and bees. Social insects are a valuable source of
inspiration for designing collectively intelligent systems comprising many agents.
E. S ¸ahin and W.M. Spears (Eds.): Swarm Robotics WS 2004, LNCS 3342, pp. 31–44, 2005.
c ? Springer-Verlag Berlin Heidelberg 2005
32 Marco Dorigo et al.
Despite noise in the environment, errors in processing information and perform-
ing tasks, and no global information, social insects are quite successful at per-
forming group-level tasks. Based on the social insect metaphor, swarm robotics
emphasises aspects such as decentralisation of the control, limited communica-
tion abilities among robots, use of local information, emergence of global be-
haviour and robustness .
The work carried out within the SWARM-BOTS project is directly inspired
by the collective behaviour of social insects colonies and other animal societies,
and in particular it focuses on the study of the mechanisms which govern the
processes of self-organisation and self-assembling in artificial autonomous agents.
In order to pursue these objectives, a new type of robot, referred to as s-bot, has
been developed. Hardware development runs in parallel with the development of
distributed adaptive architectures that make the s-bots capable of autonomously
carrying out individual and collective behaviour by exploiting local interactions
among the s-bots and between the s-bots and their environment.
The s-bots are mobile robots with the ability to connect to and disconnect
from each other [3,4]. A swarm-bot is defined as an artifact composed of a
swarm of assembled s-bots (see Figure 1). S-bots have relatively simple sensors
and motors and limited computational capabilities. Their physical links are used
to assemble into a swarm-bot able to solve problems that cannot be solved by
a single s-bot. In the swarm-bot form, the s-bots are attached to each other
as a single robotic system that can move and reconfigure. Physical connections
between s-bots are essential for solving many collective tasks. For example, s-bots
can form pulling chains to retrieve a heavy object (see Figure 1a). Also, during
navigation on rough terrain, physical links can serve as support if the swarm-bot
has to pass over a hole larger than a single s-bot (see Figure 1b), or when it has
to pass through a steep concave region. However, for tasks such as searching for
a goal location or tracing a path to a goal, a swarm of unconnected s-bots can
be more efficient.
Fig.1. Graphic visualisation of how the rigid gripper can be used to connect in a secure
way s-bots among themselves so that they form a swarm-bot for (a) retrieving heavy
objects or (b) passing over holes.
The SWARM-BOTS Project 33
The design and realisation of both the hardware and the software of such a
robotic system represents the scientific challenge of the SWARM-BOTS project.
In what follows, we first give a brief description of the robot hardware, and of
the experimental methodology employed to develop the s-bots controllers (see
Section 2). Then, in Section 3 we describe the results of several experiments
in which controllers have been designed to allow the s-bots to autonomously
perform a variety of individual and collective behaviours in partially or totally
unknown environments. Discussion and conclusions can be found in Section 4.
Fig.2. Graphic visualisation of the s-bot concept. (a) The main body (turret), which
has a diameter of 116 mm, is equipped with passive and active gripping facilities,
sensors and electronics. (b) The lower body (traction system) is equipped with tracks
and hosts the batteries.
2 The Hardware and the Simulation Environment
The construction of a number of artifacts (30-35) capable of self-assembling and
self-organising represents one of the most significant scientific challenges faced by
the SWARM-BOTS project. In subsection 2.1, we briefly describe the hardware
of the s-bots, with particular reference to its sensor and motor apparatus. A
more detailed description of the hardware components can be found in . In
subsection 2.2, we briefly introduce the main features of swarmbot3d, a simulation
environment employed to design the software which controls the s-bots1.
An s-bot is the basic elementary unit of the swarm-bot (see Figure 2). Each s-bot
is a fully autonomous mobile robot capable of performing simple tasks such as
autonomous navigation, perception of the environment and grasping of objects.
In addition to these features, one s-bot is able to communicate with other s-bots
1Details regarding the hardware and simulation of the swarm-bot can also be found
on the project web-site (www.swarm-bots.org).
34 Marco Dorigo et al.
Fig.3. Pictures of s-bots transporting an object that can not be moved by a single s-
bot. (a) A swarm-bot comprising four s-bots pulls an object. (b) Three s-bots pull/push
an object to which they are directly attached.
and physically connect to them, thus forming a so-called swarm-bot. A swarm-
bot is able to perform tasks in which a single s-bot has major problems, such
as exploration, navigation, and transportation of heavy objects on rough terrain
(see Figure 3).
As far as it concerns the mobility of the s-bot, an innovative system has been
developed which makes use of both tracks and wheels as illustrated in Figure 2.
The wheel and the track on a same side are driven by the same motor, building
a differential drive system controlled by two motors. This combination of tracks
and wheels is labelled Differential Treelsc ?Drive2. Such a combination has two
advantages. First, it allows an efficient rotation on the spot due to the larger
diameter and position of the wheels. Second, it gives to the traction system a
shape close to the cylindrical one of the main body (turret), avoiding in this
way the typical rectangular shape of simple tracks and thus improving the s-bot
The s-bot’s traction system can rotate with respect to the main body by
means of a motorised axis. Above the traction system, a rotating turret holds
many sensory systems and the two grippers for making connections with other
robots or objects. In particular, each s-bot is equipped with sensors necessary
for navigation, such as infrared proximity sensors, light and humidity sensors,
accelerometers and incremental encoders on each degree of freedom. Each robot
is also equipped with sensors and communication devices to detect and com-
municate with other s-bots, such as an omni-directional camera, coloured LEDs
around the robot’s turret, and sound emitters and receivers. In addition to a
large number of sensors for perceiving the environment, several sensors provide
each s-bot with information about physical contacts, efforts, and reactions at the
interconnection joints with other s-bots. These include torque sensors on most
joints as well as traction sensors to measure the pulling/pushing forces exerted
on the s-bot’s turret.
2Treels is a contraction of TRacks and whEELS.
The SWARM-BOTS Project 35
S-bots have two types of possible physical interconnections for self-assembling
into a swarm-bot configuration: rigid and semi-flexible. Rigid connections be-
tween two s-bots are established by a gripper mounted on a horizontal active
axis (see Figure 2). Such a gripper has a very large acceptance area allowing it
to realize a secure grasp at any angle and, if necessary, allowing it to lift another
s-bot. Semi-flexible connections are implemented by a gripper positioned at the
end of a flexible arm actuated by three servo-motors.
2.2 The Simulation Environment: Swarmbot3d
Swarmbot3d is a 3D dynamics simulator of our multi-agent system of cooperating
robots, based on the SDK VortexTMtoolkit3, which provides realistic simulations
of dynamics and collisions of rigid bodies in 3D. Swarmbot3d provides s-bot
models with the functionalities available on the real s-bots (see  for details).
It can simulate different sensor devices such as IR proximity sensors, an omni-
directional camera, an inclinometer, sound, and light sensors.
A fundamental feature of the swarmbot3d simulator is that it provides robot
simulation modules at different levels of detail. In particular, it provides a hi-
erarchy of four s-bot reference models with increasing levels of detail. The less
detailed models have been employed to speed up the process of designing neu-
ral controllers through evolutionary algorithms. The most detailed models have
been employed to validate the evolved controllers before porting them on real
hardware. The advantages of such a simulation environment are multiple: it
works as an aiding tool for accurately predicting 3D kinematics and dynamics
of a single s-bot in a swarm-bot; it has been employed to evaluate possible new
options for hardware parts; it represents a “plastic” world model which allows
the design of new experimental setups in 3D; it has been employed to quickly
evaluate new distributed control ideas before porting them to the real hardware.
Furthermore, the simulator provides on-line interactive control during simula-
tion, useful for rapid prototyping of new control algorithms. Swarmbot3d allows
to handle a group of robots either as independent units or in a swarm-bot con-
figuration, which can be thought of as a graph, in which each node represents
a connected s-bot. The connections can be created and released dynamically at
simulation time. Connections may be of a rigid nature giving to the resulting
structure the solidity of a whole entity.
In this section, we briefly summarise the methods and the results of experimental
work in which controllers have been designed to allow the s-bots to autonomously
display a variety of individual and collective behaviours in partially and totally
unknown environments. These basic behaviours represent different lines of in-
vestigation which are pursued in parallel, and are focused on: 1) aggregation;
2) coordinated motion; 3) collective and cooperative transport of a prey item;
3Critical Mass Labs, Canada (www.criticalmasslabs.com).
36 Marco Dorigo et al.
4) exploration; 5) adaptive task allocation; 6) navigation on rough terrain; 7)
functional self-assembling. These research lines have been identified by looking
at the kind of requirements that either a single s-bot or an aggregation of s-bots
must fulfil in order to successfully perform the tasks involved in a complex sce-
nario. The latter requires a swarm of up to 35 s-bots to transport heavy objects
from their initial location to a goal location in an environment which presents
difficulties of various nature, such as obstacles and holes on the ground. More-
over, the weight and/or size of the objects to be transported are such that these
objects can not be transported by a single s-bot (see Figure 4).
To be capable of accomplishing the scenario, the s-bots must be equipped
with controllers that allow them to successfully navigate in a totally or partially
unknown environment in order to find and retrieve a target. The s-bots must
also be capable of aggregating and self-assembling in a swarm-bot formation.
The swarm-bot might be of fundamental importance for passing over a hole
larger than a single s-bot, or to retrieve objects that can not be transported
by a single s-bot. Finally, a group of s-bots should be capable of adaptively
allocating resources to different tasks to be carried out either sequentially or
in parallel. For example, if two heavy objects must be transported, a group of
s-bots must be capable of splitting into two sub-groups each of which is formed
by the number of s-bots appropriately chosen with respect to the nature of
the object to be transported. The following subsections illustrate the research
activities concerning the development of the basic behavioural capabilities above
Within the SWARM-BOTS project, aggregation is of particular interest since
it stands as a prerequisite for other forms of cooperation. For instance, in order
to assemble into a swarm-bot, s-bots should first be able to aggregate. Several
experiments have focused on the design of scalable aggregation behaviours by
means of sound signalling (see [6,7] for details). Artificial neural networks shaped
by evolutionary algorithms control the behaviour of a homogeneous group of
s-bots (i.e., within a group, all the s-bots share the same controller). During
the evolutionary phase, the groups are randomly placed in a square arena. The
agents are equipped with a simulated speaker that can emit a tone for long range
signalling. S-bots can perceive the intensity of sound using three sound sensors
that simulate three directional microphones. The s-bot controller takes as input
the state of the s-bot proximity sensors, and the state of the sound sensors. Two
output nodes control the s-bot’s motors. Controllers that exploit sound to let
a group of s-bots aggregate are evolved using a fitness function that selectively
rewards those groups which minimise the average distance of all the s-bots from
the group centre of mass.
The evolved controllers show quite robust aggregation strategies. In particu-
lar, the s-bots exploit the sound signal both to get closer to each other, and to
remain aggregated. In general, all evolved strategies rely on a delicate balance
between attraction to sound sources and repulsion from other robots, the former
The SWARM-BOTS Project37
Fig.4. The scenario: a swarm of up to 35 s-bots must transport a heavy object from
an initial to a goal location. The cylinder on the left side represents the object to be
transported; the landmark on the right side represents the target location where the
object has to be transported. The four s-bots between the cylindrical object and the
target location form a path which logically connects the former to the latter. This path
is exploited by other s-bots to move back and forth between the target location and
the object to be retrieved. Also visible are two types of obstacles: walls and holes.
being perceived by sound sensors, the latter by proximity sensors. A qualitative
analysis of the evolved controllers reveals that different replications result in
slightly different behaviours. In particular, the evolved solutions differ mainly in
the behaviour of s-bots when they are close to each other.
Further evaluation tests concerning scalability of the evolved solutions have
shown that controllers evolved for groups of four s-bots can successfully bring
forth aggregation in groups with a higher number of s-bots (up to 40 s-bots). The
best scalable strategy was the one in which the controller creates an aggregate
that moves across the arena. This is a result of the complex motion of s-bots
within the aggregate, which in turn is the result of the interaction between
attraction to sound sources and repulsion from other robots. The slow motion of
the aggregate across the arena leads to scalability, as an aggregate can continue
to move joining solitary s-bots or other already formed aggregates, eventually
forming a single cluster of s-bots.
Coordinated motion represents another basic ability for a swarm-bot formed of
connected s-bots that, being independent of each other in their control, must co-
ordinate their actions to choose a common direction of motion. The coordinated
motion ability is essential for an efficient motion of the swarm-bot as a whole,
and it is achieved mainly through the exploitation of the information coming
from the traction sensor, which is placed at the turret-chassis junction of an
s-bot. The traction sensor returns the direction (i.e., the angle with respect to
the chassis’ orientation) and the intensity of the force of traction (henceforth
called “traction”) that the turret exerts on the chassis. Traction is caused by the
movement of both the connected s-bots and the s-bot’s chassis. Note that the
38 Marco Dorigo et al.
turret of each s-bot physically integrates the forces that are applied to the s-bot
by the other s-bots. As a consequence, the traction sensor provides the s-bot with
an indication of the average direction toward which the group is trying to move
as a whole. More precisely, it measures the mismatch between the directions
toward which the entire group and the s-bot’s chassis are trying to move. The
intensity of traction measures the size of this mismatch.
Our experimental work has focused on the evolution of artificial neural net-
works capable of coordinately controlling the behaviour of a swarm-bot (a col-
lection of assembled s-bots). In this kind of experiments, the problem that the
s-bots have to solve is that their wheels might have different initial directions
or might mismatch while moving. In order to coordinate, s-bots should be able
to collectively choose a common direction of movement having access only to
local information (see Figure 5). Each s-bot’s controller (i.e., an artificial neu-
ral network), takes as input the reading of its traction sensor and other sensor
readings, and sets the status of the s-bot’ actuators.
The results show that evolution can find simple and effective solutions that
allow the s-bots to move in a coordinate way independently of the topology
of the swarm-bot and of the type of link with which the s-bots are connected
(semi-flexible or rigid). Moreover, it is shown that the evolved s-bots also exhibit
obstacle avoidance behaviour (when placed in an environment with obstacles)
and object pulling/pushing behaviour (when assembled to or around an object,
see Figure 6), and scale well to swarm-bots of a larger size (see [8,9] for details).
3.3 Collective and Cooperative Transport of a Prey Item
By taking inspiration from the behaviour of ants, the SWARM-BOTS project
aims to build autonomous agents which by solely relying on local information,
are capable of cooperatively and collectively carrying objects which can not be
Fig.5. (a) Four physically linked s-bots forming a linear structure. The lines between
two s-bots represent the physical link between them. The white line above each s-bot
indicates the direction and intensity of the traction. (b) Eight s-bots connected by rigid
links into a “star formation”.
The SWARM-BOTS Project39
moved by a single agent. The members of a group have to coordinate their actions
to achieve the desired outcome. In particular, due to the nature of the object
(i.e., its shape, dimension, and weight) the s-bots might be required to connect
to each other in swarm-bot formation and/or to the object itself for transporting
it (i.e., gripping the object with the fixed gripper, see Figure 7).
In a series of experimental works, artificial neural networks have been evolved
to control the actions of a single homogeneous group of s-bots which is required
to pull and/or push an object in an arbitrarily chosen direction. During the evo-
lutionary phase, the s-bots are located in a boundless arena, in the proximity of
objects of various shape, dimension, and weight. Only indirect communication
through the environment can be exploited to attain coordination. The evolved
controllers exhibit rather good transport performances. Certain controllers show
scaling properties: they can be applied to larger groups of s-bots to move big-
ger and heavier prey objects. However, the controllers’ performances are very
sensitive to the size of the prey (see ).
A follow-up work focused on the self-organisation of s-bots into assembled
structures and on the transport of heavy prey by groups of assembled s-bots to
a target. To facilitate the process of assembling, the s-bots are provided with the
ability to detect teammates; in addition, the presence of assembled structures is
favoured by the fitness function employed. The best evolved controller proved
fairly robust with respect to different combinations of size and shape of the prey
Recently, the situation has been studied in which some s-bots are given the
opportunity to localise the transport target, while the others (called the blind
ones) are not. To enable a blind s-bot to contribute to the group’s performance,
it has been equipped with sensors to perceive both whether or not it is moving,
and traction forces on its turret. For group sizes ranging from 2 to 16, it has
been shown that blind s-bots make an essential contribution to the group’s per-
Fig.6. (a) Eight s-bots connected to an object through rigid links. (b) Traces left by
the s-bots (thin lines) and the object (thick line) during 150 simulation cycles. The
gray and black circles represent the initial positions of the s-bots and of the object.
40 Marco Dorigo et al.
Fig.7. (a) S-bots connected to each other and to an object. (b) A closer view on the
formance. For the best evolved solution the performance scales well with group
size, making possible the transport of heavier prey by larger swarms of blind and
non-blind s-bots (see ).
This subsection illustrates the mechanisms employed by the s-bots to efficiently
explore a partially or totally unknown environment. Our approach is based on
the exploitation of the collectivity, and it requires that some s-bots – referred
to as s-bot beacons – be capable of positioning themselves in the environment
in order to work as beacons for other s-bots – referred to as s-bot explorers –
that move back and forth from a starting position to a goal location. The s-bot
beacons should form a chain which connects different locations that cannot be
perceived simultaneously by a single s-bot. In this way, a path between a goal
and a home location is established, and it can be subsequently exploited by the
s-bot explorers. The main advantage of this exploration strategy is that it does
not require the s-bots to create a map-like representation of the world.
The status of these experiments, in which a behaviour-based approach is
employed to design the s-bots controllers, is still preliminary. However, simply
by varying two parameters of the s-bots controller (i.e., the probability of each
single agent to become a beacon and the probability of a robot beacon to become
an explorer) it is possible to bring forth a variety of exploration strategies each
of which results more adaptive in certain types of environment than in others.
Up to date, two different strategies have been implemented. In the simplest
setup, we have static chains: the s-bots beacons do not move. In the other setup,
the s-bots that form a chain move coordinately without breaking the chain.
We are currently working on the development of an adaptive mechanism which
autonomously sets these parameters with respect to the characteristics of the
environment experienced by the s-bots.
The SWARM-BOTS Project41
3.5 Adaptive Task Allocation
Task allocation and division of labour are two important research areas within
collective robotics. Previous studies have shown that small groups of robots
might perform a collective task similar or better than a larger group. However,
this efficiency loss can be avoided if large groups of robots are equipped with an
adaptive task allocation mechanism which distributes the resources of the group
with respect to the nature of the task and the diversity among the individuals
of the group. Within the SWARM-BOTS project we are obviously interested in
designing an adaptive task allocation mechanism which allocates to each task a
sufficient number of s-bots without reducing the efficiency of the entire group.
In particular, we have been working on a mechanism which adaptively tunes the
number of active agents in a foraging task: that is, searching for objects and
retrieving them to a nest location. The agents, controlled by a behaviour-based
architecture, use a simple adaptive mechanism which adjusts the probability
of each agent to be a forager with respect to the current success rate of the
individual on the task. Owing to this simple adaptive mechanism, a self-organised
task allocation is observed at the global level. That is, not all the agents end
up being active foragers. The same mechanism is also effective in exploiting
mechanical differences among the robots inducing specialisation in the robots’
activities. More details are given in [13,14].
3.6 Navigation on Rough Terrain
Navigating on rough terrain is an important feature for an adaptive autonomous
system. It can apply to many possible application scenarios, like space explo-
ration or rescue in a collapsed building. Within the SWARM-BOTS project,
several experiments have been run on an instance of the family of navigation
on rough terrain tasks, that is, hole avoidance. A swarm-bot is required to per-
form coordinated motion in an environment that presents holes too large to be
traversed. Thus, holes must be recognised and avoided, so that the swarm-bot
does not fall into them. The difficulty of this task is twofold: first, s-bots should
coordinate their motion. Second, s-bots have to recognise the presence of a hole,
communicate it to the whole group and re-organise to choose a safer direction
of motion. The results demonstrate that the evolved controllers (i.e., artificial
neural networks) manage to efficiently manoeuvre a swarm-bot in the proximity
of holes in the ground. Evolution is able to produce a self-organising system
that relies on simple and general rules, a system that is consequently robust to
environmental changes and to the number of s-bots involved in the experiment.
The evolved strategies strongly rely on the traction forces produced by those
s-bots that feel the presence of a hazard (see  for details).
3.7 Functional Self-assembling
These studies focus on the design of controllers for a group of s-bots required
to connect to each other, each time environmental contingencies prevent a sin-
42 Marco Dorigo et al.
gle s-bot to achieve its goal . We refer to this capability as functional self-
assembling, since the self-organised creation of a physically connected structure
has to be functional to the accomplishment of a particular task.
The complexity of functional self-assembling resides in the nature of the
individual mechanisms required to bring forth the coordinated movements that
lead firstly to the formation of the assembled structure, and subsequently to the
collective motion of the assembled structure.
In a preliminary set of studies, we have focused on the evolution of neural
controllers for self-assembling s-bots required to solve a simple scenario. In par-
ticular, we have investigated a scenario which requires the s-bots to approach a
light source located at the end of a corridor. Assembling is required to navigate
in a “low temperature” area in which a swarm-bot can navigate more effectively
than a group of disconnected s-bots. When located in the low temperature area,
the aggregation of the s-bots should facilitate the subsequent assembling through
their gripper element. This experimental setup allows us to investigate the basic
mechanisms that underpin functional self-assembling.
The results of our empirical work show that integrated (i.e., not modularised)
artificial neural networks can be successfully synthesised by evolutionary algo-
rithms in order to allow a group of s-bots to display individual and collective
obstacle avoidance, individual and collective phototaxis, aggregation and self-
assembling. To the best of our knowledge, our experiments represent one of the
first works in which (i) functional self-assembling in a homogeneous group of
robots has been achieved and (ii) evolved neural controllers successfully cope
with a complex scenario, producing different individual and collective responses
based on the appropriate control of the state of various actuators triggered by
the local information coming from various sensors.
4 Discussion and Conclusions
In this paper we have illustrated the most important features of a novel robotic
concept, called swarm-bot. A swarm-bot is a self-organising, self-assembling ar-
tifact composed of a variable number of autonomous elementary units, called
s-bots. As illustrated in Section 2, each s-bot is a fully autonomous agent capa-
ble of displacement, sensing and acting based on local information. Moreover,
the self-assembling ability of the s-bots enables a group of agents to execute tasks
that are beyond the capabilities of the single robot.
Concerning the hardware, the presence of many of such autonomous entities
that can self-assemble in a single body and disband any time the union is no
longer required, makes the system extremely versatile and robust to failures.
Contrary to the swarm-bot, other robotic systems composed of small elementary
units capable of reconfiguring themselves are less versatile and less robust, due
to the fact that each unit has no or very limited mobility, very limited sensing
capabilities, and acts often under the control of a central unit (see [17–19]).
Concerning the s-bots’ controllers, we have developed them making an ex-
tensive use of artificial neural networks shaped by evolutionary algorithms. The
solutions found by evolution are simple and in many cases they generalise to
The SWARM-BOTS Project 43
different environmental situations. This demonstrates that evolution is able to
produce a self-organised system that relies on simple and general rules, a system
that is consequently robust to environmental changes and that scales well with
the number of s-bots involved in the experiments.
This work was supported by the “SWARM-BOTS” project, funded by the Fu-
ture and Emerging Technologies programme (IST-FET) of the European Com-
mission, under grant IST-2000-31010. The information provided is the sole re-
sponsibility of the authors and does not reflect the Community’s opinion. The
Community is not responsible for any use that might be made of data appearing
in this publication. Marco Dorigo acknowledges support from the Belgian FNRS,
of which he is a Research Director, through the grant “Virtual Swarm-bots”,
contract no. 9.4515.03, and from the “ANTS” project, an “Action de Recherche
Concert´ ee” funded by the Scientific Research Directorate of the French Commu-
nity of Belgium.
1. Dorigo, M., S ¸ahin, E.:
Robots 17 (2004) 111–113
2. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to
Artificial Systems. Oxford University Press, New York, NY (1999)
3. S ¸ahin, E., Labella, T.H., Trianni, V., Deneubourg, J.L., Rasse, P., Floreano, D.,
Gambardella, L.M., Mondada, F., Nolfi, S., Dorigo, M.: SWARM-BOT: Pattern
formation in a swarm of self-assembling mobile robots. In: Proceedings of the
IEEE International Conference on Systems, Man and Cybernetics, IEEE Press,
Piscataway, NJ (2002)
4. Mondada, F., Pettinaro, G.C., Kwee, I.W., Guignard, A., Gambardella, L.M., Flo-
reano, D., Nolfi, S., Deneubourg, J.L., Dorigo, M.: SWARM-BOT: A swarm of
autonomous mobile robots with self-assembling capabilities.
Bonabeau, E., eds.: Proceedings of the International Workshop on Self-organisation
and Evolution of Social Behaviour, Monte Verit` a, Ascona, Switzerland (2002) 307–
5. Mondada, F., Pettinaro, G.C., Guignard, A., Kwee, I.V., Floreano,
Deneubourg, J.L., Nolfi, S., Gambardella, L.M., Dorigo, M.: SWARM-BOT: A
new distributed robotic concept. Autonomous Robots 17 (2004) 193–221
6. Baldassarre, G., Nolfi, S., Parisi, D.: Evolving mobile robots able to display col-
lective behaviour. Artificial Life 9 (2003) 255–267
7. Trianni, V., Groß, R., Labella, T.H., S ¸ahin, E., Dorigo, M.: Evolving aggregation
behaviors in a swarm of robots. In Banzhaf, W., Christaller, T., Dittrich, P.,
Kim, J.T., Ziegler, J., eds.: Proceedings of the Seventh European Conference on
Artificial Life. Volume 2801 of Lecture Notes in Artificial Intelligence, Springer
Verlag, Berlin, Germany (2003) 865–874
8. Baldassarre, G., Nolfi, S., Parisi, D.: Evolution of collective behaviour in a team of
physically linked robots. In G¨ unther, R., Guillot, A., Meyer, J.A., eds.: Proceedings
of the Second European Workshop on Evolutionary Robotics (EvoWorkshops2003:
EvoROB). Volume 2611 of Lecture Notes in Computer Science, Springer Verlag,
Berlin, Germany (2003) 581–592
Swarm robotics – special issue editorial.Autonomous
In Hemelrijk, C.,
44 Marco Dorigo et al. Download full-text
9. Dorigo, M., Trianni, V., S ¸ahin, E., Groß, R., Labella, T.H., Baldassarre, G., Nolfi,
S., Deneubourg, J.L., Mondada, F., Floreano, D., Gambardella, L.M.: Evolving
self-organizing behaviors for a swarm-bot. Autonomous Robots 17 (2004) 223–245
10. Groß, R., Dorigo, M.: Evolving a cooperative transport behavior for two simple
robots. In Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M., eds.:
Artificial Evolution – 6th International Conference, Evolution Artificielle. Volume
2936 of Lecture Notes in Computer Science., Springer Verlag, Berlin, Germany
11. Groß, R., Dorigo, M.: Cooperative transport of objects of different shapes and
sizes. In Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F.,
St¨ utzle, T., eds.: Proceedings of ANTS 2004 – Fourth International Workshop on
Ant Colony Optimization and Swarm Intelligence. Volume 3172 of Lecture Notes
in Computer Science., Springer Verlag, Berlin, Germany (2004) 107–118
12. Groß, R., Dorigo, M.: Group transport of an object to a target that only some
group members may sense. In X. Yao et al., eds.: Proceedings of PPSN-VIII, Eighth
International Conference on Parallel Problem Solving from Nature. Volume 3242
of Lecture Notes in Computer Science., Springer Verlag, Berlin, Germany (2004)
13. Labella, T., Dorigo, M., Deneubourg, J.L.: Efficiency and task allocation in prey
retrieval. In Ijspeert, A., Murata, M., Wakamiya, N., eds.: Proceedings of the First
International Workshop on Biologically Inspired Approaches to Advanced Infor-
mation Technology (Bio-ADIT2004). Volume 3141 of Lecture Notes in Computer
Science., Springer Verlag, Heidelberg, Germany (2004) 32–47
14. Labella, T., Dorigo, M., Deneubourg, J.L.:
swarm of robots. Technical Report TR/IRIDIA/2004-6, Universit´ e Libre de Brux-
elles, Belgium (2004) To appear in the 7th International Symposium on Distributed
Autonomous Robotic Systems (DARS04), June 23-25, 2004, Toulouse, France.
15. Trianni, V., Nolfi, S., Dorigo, M.: Hole avoidance: Experiments in coordinated
motion on rough terrain. In Groen, F., Amato, N., Bonarini, A., Yoshida, E.,
Kr¨ ose, B., eds.: Intelligent Autonomous Systems 8, IOS Press, Amsterdam, The
Netherlands (2004) 29–36
16. Trianni, V., Tuci, E., Dorigo, M.: Evolving functional self-assembling in a swarm
of autonomous robots. In Schaal, S., Ijspeert, A., Billard, A., Vijayakamur, S.,
Hallam, J., Meyer, J.A., eds.: From Animals to Animats 8. Proceedings of the
Eight International Conference on Simulation of Adaptive Behavior (SAB04), MIT
Press, Cambridge, MA (2004) 405–414
17. Yim, M., Duff, D.G., Roufas, K.D.: PolyBot: a modular reconfigurable robot. In:
Proceedings of the 2000 IEEE International Conference on Robotics and Automa-
tion (ICRA 2000). Volume 1, IEEE Press, Piscataway, NJ (2000) 514–520
18. Castano, A., Shen, W.M., Will, P.: CONRO: Towards deployable robots with
inter-robot metamorphic capabilities. Autonomous Robots 8 (2000) 309–324
19. Khosla, P., Brown, B., Paredis, C., Grabowski, B., Navarro, L., Bererton, C., Van-
deweghe, M.: Millibot Report.Report on millibot project, DARPA contract
DABT63-97-1-0003, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
Self-organised task allocation in a