'''Swarm robotics''' studies how to design large groups of robots that operate without relying on any external infrastructure and on any form of centralized control. In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act. The design of robot swarms is guided by [[swarm intelligence]] principles. Such principles promote the realization of systems that are fault tolerant, scalable and flexible.
Swarm robotics appears to be a promising approach when different activities must be performed concurrently, when high redundancy and the lack of a single point of failure are desired, and when
it is technically unfeasible to setup an infrastructure to control the robots in a centralized way. Examples of tasks that could be profitably tackled using swarm robotics are demining, search and rescue, planetary or underwater exploration, and surveillance.

==Origins==

Swarm robotics has its origins in [[swarm intelligence]] and, in fact, could be defined as "embodied swarm intelligence". Initially, the main focus of swarm robotics research was to study and validate biological research (Beni, 2005). Collaboration between roboticists and biologists was vital to make swarm robotics a relevant research field. However, in recent years the focus of swarm robotics has been shifting: from a bio-inspired field of robotics, swarm robotics is becoming more and more an engineering field whose focus is on the development of tools and methods to solve real problems (Brambilla et al., 2013).

==Characteristics of swarm robotics==

A robot swarm is a type of multi-robot system characterized by being a highly redundant group of autonomous robots that act in a [[Self-organization|self-organized]] way and cooperate to accomplish a given task. Robotsâ€™ sensing and communication capabilities are local and robots do not have access to global information. The collective behavior of the robot swarm emerges from the interactions of each individual robot with its neighboring peers and with the environment. Typically, a robot swarm is composed of homogeneous robots, but examples of heterogeneous robot swarms exist (Dorigo et al., 2013).

==Desirable properties of swarm robotics systems==

The aforementioned characteristics of swarm robotics are deemed to promote the realization of systems that are fault tolerant, scalable and flexible.

Swarm robotics promotes the development of systems that are able to cope well with the failure of one or more of its individuals: the loss of individuals does not imply the failure of the whole swarm. Fault tolerance is enabled by the high redundancy of the swarm: the swarm does not rely on any centralized control entity, leaders, or any individual robot playing a predefined role.

Swarm robotics promotes also the development of systems that are able to cope well with changes in their group size: ideally, the introduction or removal of individuals does not cause a drastic change in the performance of the swarm. Scalability is enabled by local sensing and communication: provided that the introduction and removal of robots does not dramatically modify the density of the robots, each individual robot will keep interacting with approximately the same number of peers, those that are in its sensing and communication range.

Finally, swarm robotics promotes the development of systems that are able to deal with a broad spectrum of environments and operating conditions. Flexibility is enabled by the distributed and self-organized nature of a robot swarm: in a swarm, robots dynamically allocate themselves to different tasks to match the requirements of the specific environment and operating conditions; moreover, robots operate on the basis of local sensing and communication without the need of pre-existing infrastructures and of global information on the environment.

== Potential applications of swarm robotics ==

The properties of swarm robotics systems make them appealing in several potential fields of application.

The use of robots for tackling dangerous tasks is clearly appealing as it eliminates or reduces risks for humans. The dangerous nature of these tasks implies a high risk of losing robots. Therefore, a fault-tolerant approach is required, making dangerous tasks an ideal field of application for robot swarms. Example of dangerous tasks that could be tackled using robot swarms are demining, search and rescue, and toxic cleaning.

Other potential applications for robot swarms are those in which it is difficult or even impossible to estimate in advance the resources needed to accomplish the task. For instance, allocating resources to manage an oil leak can be very hard because it is often difficult to estimate the oil output and to foresee its development. In these cases, a solution is needed that is scalable and flexible. A robot swarm could be an appealing solution: robots can be added or removed in time to provide the appropriate amount of resources and meet the requirements of the specific task. Example of tasks that might require an ''a priori'' unknown amount of resources are search and rescue, transportation of large objects, tracking, and cleaning.

Another potential field of application for swarm robotics are tasks that have to be accomplished in large or unstructured environments, in which there is no available infrastructure that can be used to control the robots - e.g., no available communication network or global localization system. Robot swarms could be employed for such applications because they are able to act autonomously without the need of any infrastructure or any form of external coordination. Examples of tasks in unstructured and large environments are planetary or underwater exploration, surveillance, demining, cleaning, and search and rescue.

Some environments might change rapidly over time. For instance, in a post earthquake situation, buildings might collapse changing the layout of the environment and creating new hazards. In these cases, it is necessary to adopt solutions that are flexible and can react fast to events. Swarm robotics could be used to develop flexible systems that can rapidly adapt to new operating conditions. Example of tasks in environments that change over time are patrolling, disaster recovery, search and rescue, cleaning.

==Scientific implications of swarm robotics==

Beside being relevant to engineering applications, swarm robotics is also a valuable scientific tool. Indeed, several models of natural swarm intelligence systems have been refined and validated using robot swarms. For example, Garnier et al. (2005) validated the model of a collective decision-making behavior in cockroaches using robot swarms.

Swarm robotics has also been used to investigate, via controlled experiments, the conditions under which some complex social behaviors might result out of an evolutionary process. For example, robot swarms have been used to study the evolution of communication (Mitri et al., 2009) and collective decision making (Halloy et al., 2007).

==Current research axes==

In this section, we present the main research axes of the current research in swarm robotics. We follow the taxonomy presented in Brambilla et al. (2013).

====Design====

The design of a robot swarm is a difficult endeavor: requirements are usually expressed at the collective level, but, eventually, the designer needs to define what the individual robots should do so that their interactions result in the desired collective behavior.
Approaches to the design problem in swarm robotics can be divided in two categories: manual design and automatic design.

In '''manual design''', the designer follows a trial-and-error process in which the behaviors of the individual robot are developed, tested and improved until the desired collective behavior is obtained. The software architecture that is most commonly adopted in swarm robotics is the ''probabilistic finite state machine''. Probabilistic finite state machines have been used to obtain several collective behaviors, including aggregation (Soysal and Sahin, 2005), chain formation (Nouyan et al., 2009), and task allocation (Liu and Winfield, 2010). Another common approach is based on ''virtual physics''. In this approach, robots and environment interact through virtual forces. This approach is particularly suited for spatially organizing collective behaviors, such as pattern formation (Spears et al. 2004) and collective motion (Ferrante et al., 2012). Currently, the main limit of manual design is that it completely relies on the ingenuity and expertise of the human designer: designing a robot swarm is more of an art than a science. A systematic and general way to design robot swarms is still missing, even though a few preliminary proposals have been made (Hamann and Worn, 2008; Berman et al., 2011; Brambilla et al., 2012).

In swarm robotics, '''automatic design''' has been mostly performed using the evolutionary robotics approach (Nolfi and Floreano, 2004). Typically, individual robots are controlled by a neural network whose parameters are obtained via [[Genetic algorithms|artificial evolution]] (Trianni and Nolfi, 2011). Evolutionary robotics has been used to develop several collective behaviors including collective transport (GroÃŸ and Dorigo, 2008) and development of communication networks (Huaert et al., 2008). One of the main limits of evolutionary robotics is that it is often difficult to define all the elements of the evolutionary process that will produce control software that allows the robot swarm to effectively accomplish the given task.

====Analysis====
The analysis of a robot swarm usually relies on models. A model of a robot swarm can be realized at two levels: the microscopic level, that is modeling the behaviors of the individual robots; or the macroscopic level, that is modeling the collective behavior of the swarm.

Modeling the microscopic level allows one to represent in a detailed way the actions of all the individual robots composing the swarm. Unfortunately, microscopic modeling is problematic due to the large number of robots involved. Often, microscopic modeling relies on computer-based simulations (Kramer and Scheutz, 2007; Pinciroli et al., 2012).

Macroscopic models avoid the complexity and scalability issues of having to model each individual robot by considering only the collective behavior of the swarm. One of the most common macroscopic modeling approaches is the use of ''rate'' or ''differential equations'' (Martinoli et al., 2004; Lerman et al., 2005). Rate equations describe the time evolution of the ratio of robots in a particular state, that is, of robots that are performing a specific action or are in a specific area of the environment. Rate equations have been used to model many collective behaviors, including object clustering (Martinoli et al., 1999) and adaptive foraging (Liu and Winfield, 2010). Another common approach is the use of ''Markov chains'', which allow researchers to formally verify properties of a robot swarm (Dixon et al., 2011; Konur et al., 2012; Massink et al., 2013). Control theory has also been used to analyze whether a robot swarm eventually converges to a desired macroscopic state (Liu and Passino, 2004; Hsieh et al., 2008).

A hybrid way of modeling robot swarms is based on ''Fokker-Plank'' and ''Langevin'' equations (Hamann and Worn, 2008; Berman et al., 2009; Prorok et al., 2011). Using these equations, one can model both the behavior of the individual robot, in the form of a deterministic component of the model; and the collective level of the swarm, in the form of a stochastic component of the model.

====Collective behaviors====
A large part of the research effort in swarm robotics is directed towards the study of collective behaviors.
Collective behaviors can be categorized into five main groups: spatially organizing behaviors, navigation behaviors, decision-making behaviors, human interaction behaviors, and other behaviors.

Spatially-organizing behaviors focus on how to organize and distribute robots and objects in space.
Examples of such behaviors are aggregation (Soysal and á¹¢ahin, 2005), pattern formation (Spears et al. 2004), chain formation (Nouyan et al. 2009), self-assembly (O'Grady et al., 2010), and object clustering/assembling (Werfel et al., 2011).

Navigation behaviors focus on how to coordinate the movement of a robot swarm.
Examples of such behaviors are collective exploration (Ducatelle et al., 2014), collective motion (Turgut et al., 2008), and collective transport (Baldassarre et al., 2006).

Collective decision-making focuses on how robots influence each other in making decisions.
In particular, collective decision-making can be used to achieve consensus on a single alternative (Garnier et al., 2005; Campo et al. 2011) or allocation to different alternatives (Pini et al., 2011).

Human-swarm interaction focus on how a human operator can control a swarm and receive feedback information from it. For example, robots can distributedly recognize the gestures of human operator (Giusti et al., 2012) or form groups based on visual and vocal inputs (Pourmehr et al., 2013).

Other behaviors that do not fall in the previously mentioned categories are collective fault detection (Christensen et al. 2009) and group size regulation (Pinciroli et al, 2013).

==Open issues==

Despite its potential to promote robustness, scalability and flexibility, swarm robotics has yet to be adopted for solving real-world problems. This is possibly due to the hardware limitations of the currently available robots, to the lack of effective ways to let a human operator interact with a robot swarm and to the lack of an engineering approach for swarm robotics. A further issue is the lack of any compelling demonstrators for outdoor swarm robotic systems (e.g., waste collection), and the lack of any business case or business model that demonstrates that the swarm robotics approach would be more cost effective than other approaches. In particular, the main open issues are the lack of a general methodology for designing robot swarms, the lack of well defined metrics and testbed to assess their performance, and the lack of formal ways to verify and guarantee their properties.
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== References ==

G. Baldassarre, D. Parisi, and S. Nolfi. Distributed coordination of simulated robots based on self-organization. ''Artificial Life'', 12(3):289â€“311, 2006.

G. Beni. From swarm intelligence to swarm robotics. In ''Swarm Robotics'', LNCS 3342, pp. 1-9, 2005. Springer.

S. Berman, Ã. M. HalÃ¡sz, M. A. Hsieh, and V. Kumar. Optimized stochastic policies for task allocation in swarms of robots. ''IEEE Transactions on Robotics'', 25(4):927â€“937, 2009.

S. Berman, V. Kumar, and R. Nagpal. Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. ''IEEE International Conference on Robotics and Automation (ICRA)'', pp 378â€“385. 2011. IEEE press.

M. Brambilla, C. Pinciroli, M. Birattari, and M. Dorigo. Property-driven design for swarm robotics. In ''Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)'', pp 139â€“146, 2012. IFAAMAS press.

M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo. Swarm robotics: a review from the swarm engineering perspective. ''Swarm Intelligence'', 7(1):1â€“41, 2013.

A. Campo, S. Garnier, O. DÃ©driche, M. Zekkri & M. Dorigo (2011). Self-organized discrimination of resources. ''PLOS One'', 6(5): e19888.

A. L. Christensen, R. Oâ€™Grady, and M. Dorigo. From fireflies to fault-tolerant swarms of robots. ''IEEE Transactions on Evolutionary Computation'', 13(4):754â€“766, 2009.

C. Dixon, A. F. T. Winfield, M. Fisher, and C. Zheng. Towards Temporal Verification of Swarm Robotic Systems. ''Robotics and Autonomous Systems'', 60(11):1429-1441, 2012.

M. Dorigo, D. Floreano, L. M. Gambardella, F. Mondada, S. Nolfi, T. Baaboura, M. Birattari, M. Bonani, M. Brambilla, A. Brutschy, D. Burnier, A. Campo, A. L. Christensen, A. Decugniere, G. Di Caro, F. Ducatelle, E. Ferrante, A. Forster, J. Martinez Gonzales, J. Guzzi, V. Longchamp, S. Magnenat, N. Mathews, M. Montes de Oca, R. O'Grady, C. Pinciroli, G. Pini, P. Retornaz, J. Roberts, V. Sperati, T. Stirling, A. Stranieri, T. Stutzle, V. Trianni, E. Tuci, A.E. Turgut, F. Vaussard. Swarmanoid: A Novel Concept for the Study of Heterogeneous Robotic Swarms. ''IEEE Robotics & Automation Magazine'', 20(4):60-71, 2013.

F. Ducatelle, G. A. Di Caro, C. Pinciroli, F. Mondada, and L. M. Gambardella. Cooperative navigation in robotic swarms. ''Swarm Intelligence'', 8(1):in press, 2014.

E. Ferrante, A. E. Turgut, C. Huepe, A. Stranieri, C. Pinciroli, and M. Dorigo. Self-organized flocking with a mobile robot swarm: a novel motion control method. ''Adaptive Behavior'', 20(6):460-477, 2012.

S. Garnier, C. Jost, R. Jeanson, J. Gautrais, M. Asadpour, G. Caprari, and G. Theraulaz. Aggregation behaviour as a source of collective decision in a group of cockroach-like robots. In ''Advances in Artificial Life'', LNAI 3630, pp. 169â€“178, 2005. Springer.

A. Giusti, J. Nagi, L. Gambardella, S. Bonardi, and G. A. Di Caro. Human-Swarm Interaction through Distributed Cooperative Gesture Recognition. 7th ACM/IEEE International Conference on Human-Robot Interaction (Video Session), 2012.

J. Halloy, G. Sempo, G. Caprari, C. Rivault, M. Asadpour, F. TÃ¢che, I. Said, V. Durier, S. Canonge, J.M. AmÃ©, C. Detrain, N. Correll, A. Martinoli, F. Mondada, R. Siegwart, J.-L. Deneubourg. Social integration of robots into groups of cockroaches to control self-organized choices. ''Science'', 318(5853):1155â€“1158, 2007.

H. Hamann and H. WÃ¶rn. A framework of space-time continuous models for algorithm design in swarm robotics. ''Swarm Intelligence'', 2(2â€“4):209â€“239, 2008.

S. Hauert, J.-C. Zufferey, and D. Floreano. Evolved swarming without positioning information: an application in aerial communication relay. ''Autonomous Robots'', 26(1):21â€“32, 2008.

M. A. Hsieh, Ã. HalÃ¡sz, S. Berman, and V. Kumar. Biologically inspired redistribution of a swarm of robots among multiple sites. ''Swarm Intelligence'', 2(2â€“4):121â€“141, 2008.

S. Konur, C. Dixon, and M. Fisher. Analysing robot swarm behaviour via probabilistic model checking. ''Robotics and Autonomous Systems'', 60(2):199â€“213, 2012.

J. Kramer and M. Scheutz. Development environments for autonomous mobile robots: a survey. ''Autonomous Robots'', 22(2), 101â€“132, 2007.

K. Lerman, A. Martinoli, and A. Galstyan. A review of probabilistic macroscopic models for swarm robotic systems. In ''Swarm Robotics'', LNCS 3342, pp 143â€“152, 2005. Springer.

Y. Liu and K. M. Passino. Stable social foraging swarms in a noisy environment. ''IEEE Transactions on Automatic Control'', 49(1):30â€“44, 2004.

W. Liu, A. F. T. Winfield. A Macroscopic Probabilistic Model for Collective Foraging with Adaptation. ''International Journal of Robotics Research'', 29(14):1743-1760, 2010.

M. Massink, M. Brambilla, D. Latella, M. Dorigo, and M. Birattari. On the use of bio-pepa for modelling and analysing collective behaviours in swarm robotics. ''Swarm Intelligence'', 7(2-3):201-228, 2013.

A. Martinoli, A. J. Ijspeert, and F. Mondada. Understanding collective aggregation mechanisms: from
probabilistic modelling to experiments with real robots. ''Robotics and Autonomous Systems'', 29(1):51â€“
63, 1999.

A. Martinoli, K. Easton, and W. Agassounon. Modeling swarm robotic systems: a case study in collaborative distributed manipulation. ''The International Journal of Robotics Research'', 23(4â€“5):415â€“436, 2004.

S. Mitri, D. Floreano, and L. Keller. The evolution of information suppression in communicating robots with conflicting interests. ''PNAS'', 106(37):15786-15790, 2009.

A. Naghsh, J. Gancet, A. Tanoto, and C. Roast. Analysis and design of human-robot swarm interaction in firefighting. In ''Proceedings of the 17th IEEE International Symposium on the Robot and Human Interactive Communication (Ro-man)'', pp. 255â€“260, 2008. IEEE press.

S. Nolfi and D. Floreano. ''Evolutionary robotics: intelligent robots and autonomous agents''. MIT Press, 2000.

S. Nouyan, R. GroÃŸ, M. Bonani, F. Mondada, and M. Dorigo. Teamwork in self-organized robot colonies. ''IEEE Transactions on Evolutionary Computation'', 13(4):695â€“711, 2009.

R. Oâ€™Grady, R. GroÃŸ, A. L. Christensen, and M. Dorigo. Self-assembly strategies in a group of autonomous mobile robots. ''Autonomous Robots'', 28(4):439â€“455, 2010.

C. Pinciroli, V. Trianni, R. O'Grady, G. Pini, A. Brutschy, M. Brambilla, N. Mathews, E. Ferrante, G. A. Di Caro, F. Ducatelle, M. Birattari, L. M. Gambardella and M. Dorigo. ARGoS: a modular, parallel, multi-Engine simulator for multi-robot systems. ''Swarm Intelligence'', 6(4):271-295, 2012.

C. Pinciroli, R. O'Grady, A. L. Christensen, M. Birattari and M. Dorigo. Parallel formation of differently sized groups in a robotic swarm. ''SICE Journal of the Society of Instrument and Control Engineers'', 52(3):213-226, 2013.

G. Pini, A. Brutschy, M. Frison, A. Roli, M. Dorigo, and M. Birattari. Task partitioning in swarms of robots: An adaptive method for strategy selection. ''Swarm Intelligence'', 5(3-4):283-304, 2011.

S. Pourmehr, V. M. Monajjemi, R. T. Vaughan, and G. Mori. "You two! Take off!": Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands. In'' Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS)'', 2013. IEEE press.

A. Prorok, N. Correll, and A. Martinoli. Multi-level spatial modeling for stochastic distributed robotic systems. ''The International Journal of Robotics Research'', 30(5):574â€“589, 2011.

O. Soysal and E. Åžahin. Probabilistic aggregation strategies in swarm robotic systems. In ''Proceedings of the IEEE Swarm Intelligence Symposium (SIS)'', pp. 325â€“332, 2005. IEEE press.

W. M. Spears, D. F. Spears, J. C. Hamann, and R. Heil. Distributed, physics-based control of swarms of vehicles. ''Autonomous Robots'', 17(2â€“3):137â€“162. 2004.

V. Trianni and S. Nolfi. Engineering the evolution of self-organizing behaviors in swarm robotics: A case study. ''Artificial Life'', 17(3):183â€“202, 2011.

A. E. Turgut, H. Ã‡elikkanat, F. GÃ¶kÃ§e, and E. á¹¢ahin. Self-organized flocking in mobile robot swarms. ''Swarm Intelligence'', 2(2â€“4): 97â€“120, 2008.

J. Werfel, K. Petersen, and R. Nagpal. Distributed multi-robot algorithms for the TERMES 3D collective construction system. In ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)'', 2011. IEEE press.

== External links ==

* [http://www.springer.com/11721 Swarm Intelligence]: The main journal in the field.
* [http://iridia.ulb.ac.be/~ants ''ANTS - International Conference on Swarm Intelligence'']: This series of conferences, held for the first time in 1998, is the oldest in the swarm intelligence field.
* [http://www.computelligence.org/sis/2006/past.php ''IEEE Swarm Intelligence Symposium'']: Another series of conferences dedicated to swarm intelligence, started in 2003.
* [http://www.swarm-bots.org/ Swarm-bots]: research project 2001-2005
* [http://iridia.ulb.ac.be/argos/ ARGoS]: A multi-robot, multi-engine simulator for heterogeneous swarm robotics
* [http://www.swarms.org/ Swarms]: research project 2003-2007
* [http://www.i-swarm.org/ i-Swarm project]: research project 2005-2008
* [http://www.swarmanoid.org/ Swarmanoid]: research project 2006-2010
* [http://symbrion.org/tiki-index.php Symbrion]: research project 2008-2013
* [http://www.e-swarm.org/ E-Swarm]: research project 2010-2015
* [http://www.eecs.harvard.edu/ssr/projects/progSA/kilobot.html Kilobots]: a low-cost robot for swarm robotics

[[Category:Artificial Life]]
[[Category:Robotics]]
[[Category:Computational intelligence]]

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Here are my review comments on the article Swarm Robotics.

===1. Overall definition.===

I think this is fine, except for the third sentence.

"The design of robot swarms is guided by swarm intelligence principles, which promote the realization of systems that are fault tolerant, scalable and flexible. "

This doesn't make sense, since swarm intelligence principles are in essence as observed/deduced from biology, whereas the 2nd part of the sentence is about possible engineering benefits. I recommend to split the sentence, i.e.

"The design of robot swarms is guided by swarm intelligence principles. Such principles may lead to engineering benefits including artificial systems that are fault tolerant, scalable and flexible. "

===Answer===
The phrase was restructured as suggested. Note that we kept "promote the realization" as we think that "may lead to" is too weak and does not convey the fact that the swarm intelligence principles are followed to obtain these engineering benefits; "may lead to" seems to convey more the idea that the engineering benefits are just an uncontrolled/unwanted effect.

===2. Characteristics===

Recommend removal of 'large and', so "A robot swarm is a highly redundant group of..." This avoids problems of how robots many is large, etc?

Recommend replacing the work results with 'emerges', in the final sentence of this para.

===Answer===
Reworded as suggested.

===3. Desirable properties===

Recommend replacing 'are deemed to' with 'may' in the first sentence.

Recommend adding the word 'ideally' in 1st sentence of 3rd para, i.e. "...in their group size: ideally the introduction of..."

===Answer===
Regarding the first comment, we kept "are deemed to", for the same reason explained in the answer to 1.

We followed the second comment as suggested.

===4. Potential applications===

Recommend rewording 2nd sentence in 2nd para:

Therefore, a solution that is fault tolerant is necessary, ...
as
Therefore, a fault-tolerant approach is required, ...

===Answer===
Reworded as suggested.

===5. Current Research Axes===

Since you start this section by referring the reader to Brambilla et al, you *must* ensure that this paper is accessible to all and not behind a paywall, preferably with a link from here, to a pdf.

===Answer===
We rephrased the way the paper is introduced. Unfortunately, we cannot ensure that the paper is open access.

===5.1 Analysis===

Recommend remove 'very' from 2nd line of 2nd para, i.e. "...due to the large number of robots involved"

In the section on Macroscopic models section you might consider adding a reference to
Liu W and Winfield AFT, 'A Macroscopic Probabilistic Model for Collective Foraging with Adaptation', International Journal of Robotics Research, 29 (14), 1743-1760, 2010.
Since this work is one of very few examples of successfully modelling an *adaptive* swarm.

===Answer===
Done, thanks for the suggested literature.

===5.2 Collective behaviours===

Although you briefly mention human-swarm interaction, I *strongly* recommend that this merits a section of its own within Current Research Axes. I believe one of the important missing elements in swarm robotics in human-swarm interaction -since even though the indvidual robots may be autonomous the swarm, there still needs to be an effective means for commanding, monitoring and intervening (should things go wrong) with the swarm as a whole, and recommend you highlight the excellent work of both Vaughan et al, and Gambardella et al. I.e.

Shokoofeh Pourmehr and Valiallah Mani Monajjemi and Richard T. Vaughan and Greg Mori. "You two! Take off!": Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'13), Tokyo, Japan 2013

A. Giusti, J. Nagi, L. Gambardella, S. Bonardi, G. A. Di Caro, Human-Swarm Interaction through Distributed Cooperative Gesture Recognition 7th ACM/IEEE International Conference on Human-Robot Interaction (Video Session) (HRI), Boston, MA, USA, March 5-8, 2012

===Answer===
We enlarged the part dedicated to human-swarm interaction and added the suggested literature.

===6. Open Issues===

I think this section needs to be strengthened. For instance I think that effective Human Swarm Interaction (HSI) is an impediment to real world application. Others are the lack of any compelling demonstrators for outdoor swarm robotic systems (i.e. waste collection), and the lack of any business case or business model that demonstrates the swarm robotics approach would be more cost effective that any conventional robotics - or none robotics - approaches.

===Answer===
We modified the section as suggested.

===7. References===

Please replace this:
C. Dixon, A. Winfield, and M. Fisher. Towards temporal verification of emergent behaviours in swarm robotic systems. In Towards Autonomous Robotic Systems, LNCS 6856, pp. 336â€“347, 2011. Springer.
With a more recent paper:
Dixon C, Winfield A, Fisher M and Zheng C, Towards Temporal Verification of Swarm Robotic Systems, Robotics and Autonomous Systems, 60 (11), 1429-1441, Nov 2012.

===Answer===
Done, thanks for the suggestion.

===8. Additional General Comments===

i. Someone familiar with conventional multi-robot systems would be puzzled to find no mention here. It would be good to constrast the swarm robotics approach with traditional multi-robot systems (which are sometimes mistakenly called swarm systems).

ii. I'm surprised there is no mention of homogeneity and heterogeneity, i.e. that most existing lab swarm robotics systems are homogeneous, but that the approach does encompass heterogeneous systems. Of course Swarmanoids is a great example.

iii. It may be interesting to include a section on the history of swarm robotics.

iv. the article could be improved by some explanation of the rationale, i.e. the close and symbiotic relationship between the study of social insects/animals and swarm robotics - perhaps this could be included in the Scientific Implications section..?

Note: I was assisted in this review by Dr W Liu.

===Answer===
i. We added a mention of multi-robot systems in Section 1. We think that a formal comparison between swarm robotics and other multi-robot systems would be out of the scope of this article. Our intent is to describe swarm robotics through its characteristics, which are also the characteristics that distinguish swarm robotics from other robotics systems

ii. We added a mention to this in Section 1.

iii. and iv. We added a section on the origins of swarm robotics in which we also presented briefly the historical relationship between biology and swarm robotics.

SRSP

2014-01-09T14:08:12Z

Manubrambi:

Ffmpeg

2013-12-12T15:54:52Z

Manubrambi: /* Examples */

These are the steps to convert a set of frames (in the format frame_xxxxx.bmp) to a video.

==== Some options ====
* -f image2 : to start from a set of frames (not a video)
* -i input : the input value
* -r xx.xxxx : frames per second (that is, fps given by the camera when recording)
* -b xxxxk : specify the bitrate (higher = better quality, larger file size)
* -threads 0 : optimal thread usage
* -an : no audio
* -vcodec libx264 : uses the best and newest codec (on ubuntu it's in the libx264-106 packet)
* -vpre xxxx : codec quality (see [http://juliensimon.blogspot.com/2009/01/howto-ffmpeg-x264-presets.html here] )
* -vf : video filtes for adding filters (to have a list of available filters type 'ffmpeg -filters'). here below useful filters:
** "transpose=1" : rotates the video (1 rotates by 90 degrees clockwise; 2 rotates by 90 degrees counterclockwise)
* -ss: starting time (e.g. -ss 00:00:45.0 it cut the first 45 seconds of the video)
* -t: time (length) of the video (e.g. -t 00:00:15.0 it takes only 15 seconds of the input video)
* output.ext : output is the name of the file, ext is the extension. Note that selecting mp4 or avi produces different files (is not just a name!)

==== Examples ====

* Low quality video, fast result:
avconv -f image2 -r 12.0205 -i frame_%05d.bmp -vcodec libx264 -threads 0 -an -preset ultrafast video_fast.avi

* High quality (slow, high quality)::
ffmpeg -f image2 -r 12.0205 -i frame_%05d.bmp -vcodec libx264 -threads 0 -an -vpre hq -b:v 2000k video2000k.avi

* High quality, 2 pass (very slow, even higher quality):
** note that there are actually two commands (hence the &&), the first generates the first pass to get the info necessary for the second pass. The command generates 4 files: video_2pass1.avi (the first pass, useless, can be deleted), video_2pass2.avi (the video itself, to keep) and 2 log files (delete them if you need to create another 2pass video)
ffmpeg -f image2 -r 12.0205 -i frame_%05d.bmp -an -vcodec libx264 -threads 0 -vpre fastfirstpass -b 2000k -pass 1 video_2pass1.avi &&
ffmpeg -f image2 -r 12.0205 -i frame_%05d.bmp -an -vcodec libx264 -threads 0 -vpre hq -b 2000k -pass 2 video_2pass2.avi

A note about doing videos from ARGoS2. The size of the images depends on the size of the window, so make sure that the size of the window is the one you like and also that both sizes are divisible by 2, otherwise x264 might complain.

* Rotate a video, remove audio and convert to avi
ffmpeg -vf "transpose=2" -an -sameq -i 00000.MTS video.avi

* Cut a video (from second 15 to second 45)
ffmpeg -ss 00:00:15.0 -t 00:00:30.0 -i inputVideo.avi outputVideo.avi

==== How to embed a streaming movie into your supplementary material webpage ====

1. Convert Avi (or any video) to Ogg, MP4 and Webm:

ffmpeg -i video.avi -b 1024k video.ogv
ffmpeg -i video.avi -b 1024k video.mp4
ffmpeg -i video.avi -b 1024k video.webm

2. Use the following video tag:

<video controls="controls" height="480" width="320" >
<source id="mp4_src" src="video.mp4" type="video/mp4"> </source>
<source id="ogg_src" src="video.ogv" type="video/ogg"> </source>
<source id="webm_src" src="video.webm" type="video/webm"> </source>
your browser does not support the video tag
</video>

The browser will try, one by one sequentially, the formats listed in the list of sources, and will adopt the first one that works.

==== Useful links ====
* http://juliensimon.blogspot.com/2009/01/howto-ffmpeg-x264-presets.html
* http://rodrigopolo.com/ffmpeg/cheats.html

--[[User:Manubrambi|Manubrambi]] 09:05, 10 October 2011 (UTC)

Task Abstraction Module

2013-11-29T12:56:31Z

Manubrambi: /* Connect to the right coordinator */

== Task Abstraction Module ==

=== Related pages ===

* [[TAM/Status|Status of all TAMs at IRIDIA]]
* [[TAM/Publications|List of publications using the TAM]]
* [[TAM/Hardware|Description of hardware]]
* See http://arnuschky.github.io/iridia-tam/ for final "release" info
* Supplementary materials: http://iridia.ulb.ac.be/supp/IridiaSupp2012-002/

=== How-to cite ===

Will change soon.

@techreport{BruPinBai-etal2010:IridiaTAM,
Author = {Arne Brutschy and Giovanni Pini and Nadir Baiboun and Antal Decugni{\`e}re and Mauro Birattari},
Title = {The {IRIDIA} \textsf{TAM}: A device for task abstraction for the e-puck robot},
Institution = {IRIDIA, Universit\'e Libre de Bruxelles},
Year = {2010},
Number = {TR/IRIDIA/2010-015},
Address = {Brussels, Belgium},
}

=== Sources/GIT repositories ===

* Final "release" git: https://github.com/arnuschky/iridia-tam
* IRIDIA development git: https://iridia-dev.ulb.ac.be/projects/iridia-tam.git (includes paper sources, experiments etc)

=== The coordinator ===
TBD
==== Write a controller ====
TBD

=== The Firmware ===
TBD
==== Flash a TAM ====
TBD

== Troubleshooting ==

=== Problems with librxtxSerial.so ===
Check that the link in the "coordinator" directory is pointing to the correct version.
Example for a 32 bits pc:
librxtxSerial.so -> libs-dist/rxtx-2.2pre2-bins/i686-pc-linux-gnu/librxtxSerial.so

=== Could not find port: /dev/ttyUSB0 ===

Check that you have a file /dev/ttyUSB0 or /dev/ttyUSB1 and change the setting in the main of the experiment.

Check that you have permission to read from them, e.g.:
cat /dev/ttyUSB0
should not give a "denied" message

In case you don't, add yourself to the correct group. Also, you can try doing
chmod 666 /dev/ttyUSB0

=== Connect to the right coordinator ===
Check the pins on the TAM.
The diagram can be confusing: the black dot is where the pin should be.

For example, to use coordinator 1, the pins should be like this:
X =
= =
= X
Where X is the pin and = means empty

Task Abstraction Module

2013-11-29T12:55:48Z

Manubrambi: /* Task Abstraction Module */

== Task Abstraction Module ==

=== Related pages ===

* [[TAM/Status|Status of all TAMs at IRIDIA]]
* [[TAM/Publications|List of publications using the TAM]]
* [[TAM/Hardware|Description of hardware]]
* See http://arnuschky.github.io/iridia-tam/ for final "release" info
* Supplementary materials: http://iridia.ulb.ac.be/supp/IridiaSupp2012-002/

=== How-to cite ===

Will change soon.

@techreport{BruPinBai-etal2010:IridiaTAM,
Author = {Arne Brutschy and Giovanni Pini and Nadir Baiboun and Antal Decugni{\`e}re and Mauro Birattari},
Title = {The {IRIDIA} \textsf{TAM}: A device for task abstraction for the e-puck robot},
Institution = {IRIDIA, Universit\'e Libre de Bruxelles},
Year = {2010},
Number = {TR/IRIDIA/2010-015},
Address = {Brussels, Belgium},
}

=== Sources/GIT repositories ===

* Final "release" git: https://github.com/arnuschky/iridia-tam
* IRIDIA development git: https://iridia-dev.ulb.ac.be/projects/iridia-tam.git (includes paper sources, experiments etc)

=== The coordinator ===
TBD
==== Write a controller ====
TBD

=== The Firmware ===
TBD
==== Flash a TAM ====
TBD

== Troubleshooting ==

=== Problems with librxtxSerial.so ===
Check that the link in the "coordinator" directory is pointing to the correct version.
Example for a 32 bits pc:
librxtxSerial.so -> libs-dist/rxtx-2.2pre2-bins/i686-pc-linux-gnu/librxtxSerial.so

=== Could not find port: /dev/ttyUSB0 ===

Check that you have a file /dev/ttyUSB0 or /dev/ttyUSB1 and change the setting in the main of the experiment.

Check that you have permission to read from them, e.g.:
cat /dev/ttyUSB0
should not give a "denied" message

In case you don't, add yourself to the correct group. Also, you can try doing
chmod 666 /dev/ttyUSB0

=== Connect to the right coordinator ===
Check the pins on the TAM.
The diagram can be confusing: the black dot is where the pin should be.
For example, to use coordinator 1, the pins should be like this:
X =
= =
= X
Where X is the pin and = means empty

Robotic Arena

2013-11-29T12:48:32Z

Manubrambi: Created page with "== Connect both to the Internet and the e-puck network == edit /etc/network/interfaces put this: auto lo eth0 eth0:1 iface lo inet loopback iface eth0 inet static …"

== Connect both to the Internet and the e-puck network ==

edit /etc/network/interfaces
put this:

auto lo eth0 eth0:1
iface lo inet loopback
iface eth0 inet static
address 10.0.0.XXX #(high value > 160)
network 10.0.0.0
netwmask 255.255.255.0
broadcast 10.0.0.255
gateway 10.0.0.1
iface eth0:1 inet static
address 10.0.1.X #(low value < 10)
network 10.0.1.0
netmask 255.255.255.0
broadcast 10.0.1.255

Put the correct static IPs and remove the comments

Restart the network
sudo /etc/init.d/networking restart

Main Page

2013-11-29T12:45:37Z

Manubrambi: /* Hardware */

{| id="mp-topbanner" style="width:100%; background:#fcfcfc; margin-top:1.2em; border:1px solid #ccc;" align=center
| style="width:56%; color:#000;" |
{| style="width:100%; border:none; background:none;"
| style="width:100%; text-align:center; white-space:nowrap; color:#000;" |
<div style="font-size:162%; border:none; margin:0; padding:.1em; color:#000;">Welcome to [http://iridia.ulb.ac.be IRIDIA]'s wiki.</div>
[[Image:Iridia_logo_fancy.png|link=|480px|center]]
|}
__NOTOC__

<div id="Top"></div>
{| cellspacing="20" cellpadding="5" align=center
|-

|valign="top" width="50%" style="border:1px solid #cef2e0; background:#f5fffa;"|
<h2 id="mp-tfa-h2" style="margin:3px; background:#cef2e0; font-size:120%; font-weight:bold; border:1px solid #a3bfb1; text-align:left;
color:#000; padding:0.2em 0.4em;">[[#anc1 | Practicalities]]</h2>
* [[#anc1_1 | Moving and surviving in Brussels]]
* [[#anc1_2 | Information for students]]
* [[#anc1_3 | Useful informations]] (menu, pizza, etc...)
* [[Lab responsibilities]]

|valign="top" width="50%" style="border:1px solid #cedff2; background:#f5faff; vertical-align:top;"|

<h2 id="mp-itn-h2" style="margin:3px; background:#cedff2; font-size:120%; font-weight:bold; border:1px solid #a3b0bf; text-align:left; color:#000; padding:0.2em 0.4em;">[[#anc2 | Research]]</h2>

* [[#anc2_1 | Robotics]] (S-Bots, E-pucks, Software, Links)

* [[#anc2_2 | Optimization]] (Links)

* [[IRIDIA Technical reports]]

* [http://iridia.ulb.ac.be/seminars IRIDIA seminars]

* [[#anc2_3 | Publications]] (how to publish IRIDIA reports, digital libraries)
|-

|valign="top" width="50%" style="border:1px solid #ddcef2; background:#faf5ff; vertical-align:top; color:#000;"|
<h2 id="mp-tfp-h2" style="margin:3px; background:#ddcef2; font-size:120%; font-weight:bold; border:1px solid #afa3bf; text-align:left; color:#000; padding:0.2em 0.4em">[[#anc3 | Infrastructure]]</h2>

* [[#anc3_1 | Hardware]] (configuration, backup, cluster, etc.)
* [[#anc3_2 | Software]] (HOWTOs, homepage, etc.)
* [[#material | Diverse material]] (Logos, graphic material, proceedings, etc.)

|valign="top" width="50%" style="padding: 2px; border:1px solid #FFEDCC; color: #000000; background: #FFFCE6;"|
<h2 id="mp-tfp-h2" style="margin:3px; background: #FFEDCC; font-size:120%; font-weight:bold; border:1px solid #cccccc; text-align:left; color:#000; padding:0.2em 0.4em">Weekly meetings</h2>


* Wednesdays at 15h00 : [[Robotics weekly meetings | Robotics meetings]]
* Thursdays at 10h30 : [[Administration weekly meetings | Administration weekly meeting]]
* Thursdays at 11h00 : [[Optimization Group Meetings]]

|}

'''WARNING''' : we have changed the access policy of the wiki. To consult some sensitive information, you need a login.

== Adding information ==

A Wiki is an excellent tool to maintain dynamic information. If you want to add something to this Wiki feel very free contact the system administrator and get an account. It is straight-forward to add and change the information in MediaWiki. Simply press ''edit'' on the top of this page to see how it was done. If you create a link to a non-existing page within this Wiki you can create that page by following the link - of course you would need a login to do so!. Pictures and documents have to be [[Special:Upload|uploaded]] before they can be used on pages. All popular image formats are supported and pdf and ps documents are allowed .

For more information on the Wiki mark-up language see [http://en.wikipedia.org/wiki/Wikipedia:How_to_edit_a_page Wikipedia's page about How to edit a page]. When you become good at it, you can make cool looking pages.

== <div id="anc1">Practicalities</div> ==

* <div id="anc1_1">'''Brussels'''</div>
** [[Moving to Brussels]] - What you need to know for moving to Brussels (housing, transport, health care, etc.).
** [[Surviving in Brussels]] - French lessons, cultural events, etc.
** [[Allocations de chomage]] - Allocations de chomage.

* <div id="anc1_2">'''Students'''</div>
** [[What you need to begin to work in IRIDIA]] - Resources to activate to work at IRIDIA.
** [[For students visiting IRIDIA]] - Information for new students coming to IRIDIA.
** [[University Administration]] - (IMPORTANT!) The what, the where and the how of navigating university bureaucracy - inscription, regulations etc.
** [[Equivalence]] - Step by step explanations to request an equivalence of diplomas from Belgium.
** [[Funding]] - Information on getting funding for projects, scholarships and unemployment benefits in Belgium once the Ph.D. grant runs out.
** [[StudentsIRIDIA | Students]] - Information on students in IRIDIA.
** [http://www.ulb.ac.be/dep/financier/perso-avantages/index.html Intranet for ULB employees] - Web site with list of benefit for being ULB employees
** [[Insurance while travelling abroad]] - ULB's insurance
** [https://iridia.ulb.ac.be/cgi-bin/mailman/listinfo/ IRIDIA mailing lists]
** [[ECTS formation doctorale]] - a description of what you can use as ECTS for your DEA/formation doctorale

* <div id="anc1_3">'''Useful information'''</div>
** [[Working late | Pizza]] - menu for the local pizza place, it is a good idea to order in advance.
** [http://wwwdev.ulb.ac.be/restaurants/solbosch_s/r_pub.php3 Weekly menu at ULB restaurant]
** [http://www.snack44.net/ Snack 44] Menu for sandwich bar dans l'avenue de l'UniversitÃ©

* <div id="lab_responsibilities">'''Lab responsibilities'''</div>
** [[Lab responsibilities]] - list of labs responsibilities/tasks and who takes care of them.

<center>[[#Top | Back to top]]</center>

== <div id="anc2">Research</div> ==

=== <div id="anc2_1">Robotics</div> ===

* [[Robots | Main page for robots at IRIDIA]] - List of robots, technical information, how to access
* [[Arena time slots]] - Reserve arena time
* [[E-Pucks]] - All the information on how to use the e-pucks are here
* [[Task Abstraction Module|The TAMs -- Task Abstraction Modules]] - All the information on how to use the TAMs are here

* '''Software'''
** [[ffmpeg | Using ffmpeg]] - Some examples on how to convert a set of frames to a video

* '''Links & news'''
** [http://iridia.ulb.ac.be/comp2sys/ COMP2SYS project website]
** [[Robot labs around the World]]
** [http://robots.net Read more news on robots.net...]
** [[Old news]]

<center>[[#Top | Back to top]]</center>

=== <div id="anc2_2">Optimization</div> ===

* '''Links & news'''
** [http://www.informs.org/Resources/ INFORMS OR/MS]
** [http://143.129.203.3/eume/php/eume.main.php EU/ME]
** [http://www.prism.uvsq.fr/~vdc/ECCO/ ECCO]
* '''Multi-objective Algorithms Software'''
** [http://www.tik.ee.ethz.ch/sop/pisa/ ETH - SOP - PISA]
** [http://paradiseo.gforge.inria.fr/index.php?n=Main.MOEO ParadisEO-MOEO]
** [http://shark-project.sourceforge.net/MOO-EALib/index.html Shark - MOO-EAlib]

* '''Metaheuristics'''
** [http://www.aco-metaheuristic.org Ant Colony Optimization]
** [http://iew3.technion.ac.il/CE/about.php Cross Entropy Method]
** [http://evonet.lri.fr/ Evolutionary Computing]
** [http://www.research.att.com/~mgcr/grasp/gannbib/gannbib.html GRASP]
** [http://www.tabusearch.net/ Tabu Search]
* '''OR Software'''
** [http://code.google.com/p/or-tools/ OR-Tools by Google]
** [http://scip.zib.de/ SCIP]
* '''Projects'''
** [http://iridia.ulb.ac.be/comp2sys/ COMP2SYS]
** [http://www.metaheuristics.org/ Metaheuristics Network]
* '''Applications'''
** [http://www.nada.kth.se/~viggo/wwwcompendium/ A compendium of NP optimization problems]
** [http://www.tsp.gatech.edu/ TSP]
** [http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/ TSPLIB95] - A Traveling Salesman Problem Library
** [http://www.seas.upenn.edu/qaplib/ QAPLIB] - A Quadratic Assignment Problem Library
** [http://people.brunel.ac.uk/~mastjjb/jeb/info.html OR-Library]
** [http://iridia.ulb.ac.be/~manuel/tsptw-instances Benchmark Instances for the Travelling Salesman Problem with Time Windows (TSPTW)]
** [[Concorde]]
* '''Experimental Analysis and Statistics'''
** [http://www.r-project.org/ R project]
** [[Tutorials_and_links_about_C%2C_R%2C_LateX_and_bash_(unix)#R | Tutorials and links about R]]
** [http://www.statsoft.com/textbook/stathome.html Electronic Statistics Textbook. StatSoft, Inc. (2006)]
** [http://www.itl.nist.gov/div898/handbook/ NIST/SEMATECH e-Handbook of Statistical Methods]
**[http://www.keithbower.com/datasets/Audio%20Recordings.htm Videos on Statistical Concepts]
* '''Software Development'''
** [http://gcc.gnu.org/c99status.html C99 standard and use GCC compiler tools] - Suggested compiler
** [http://www.gnu.org/prep/standards/standards.pdf GNU Coding Standards] - Suggested coding style guidelines
** [http://www.gnu.org/software/gsl/ GSL] - GNU Scientific Library
** [http://subversion.tigris.org/ Subversion] - Suggested version control tool (see the page on the IRIDIA [[Development Server]] how to use it inside the lab)
** [http://www.stack.nl/~dimitri/doxygen/ Doxygen] - Suggested documentation tool for source code
<center>[[#Top | Back to top]]</center>

=== Generic Tutorials and Links ===
* [[Tutorials and links about C, R, LateX and bash (unix)]]

=== Neural Networks ===
* [[Neural Network Tutorials]]

=== <div id="anc2_3">Publications</div> ===

* [[IRIDIA Technical reports]] - Information on publishing a technical report and LaTeX package(s).
* [[Access to Digital Libraries]] - Which on-line papers archive we have access from IRIDIA network.
* [[Iridia Supplementary Information Instructions page]] - Information on HOW TO use the Iridia Supplementary Information page.
* [[Write Better English]] - Basic pointers on how to raise the level of english in your paper.
* [[Embed Fonts in your PDFs]] - A guide on how to embed fonts on your PDFs for submission to conferences.

<center>[[#Top | Back to top]]</center>

== <div id="anc3">Infrastructure</div> ==

* [[Infrastructure]] - Generalities, meetings, TODOS, etc.

=== <div id="anc3_1">'''Hardware'''</div> ===
* [[Workstation configuration]] - How to setup your personal workstation and laptop, printers, etc.
* [[Backup Server]] - How to use the backup server of IRIDIA
* [[Development Server]] - How to use the IRIDIA development server and how to use subversion repositories
* IRIDIA Cluster: See the [http://majorana.ulb.ac.be/wordpress/ Cluster Website] for usage information, tips and updates.
* [[Using the IRIDIA Experimental room]] - All you need to know to easily run real experiments.
* [[Workshop]] - Everything you wanted to know about the workshop but were afraid to ask
* [[Laser Cutter]]
* [[Shakers]]
* [[Tracking System]]
* [[Robotic Arena]]

=== <div id="anc3_2">'''Software'''</div> ===
* [[Creating your own home page]] - How to create your own home.
* See all [[Software HOWTOs]]
* Suggested software for [[Workstation_configuration#For_Mac_OS_X_users | Mac OS X]] users
* List of [[Available_commercial_software | commercial software]] we have in IRIDIA
* Howto create a [[howto_create_new_svn_repos | new svn repository ]] on the iridia server
* Tune your algorithm on the [[Tuning_your_algorithm_with_irace_on_the_IRIDIA_cluster|IRIDIA cluster with irace]]

=== <div id="material">'''Diverse Material'''</div> ===
* [[Logos]]
* [https://iridia-dev.ulb.ac.be/projects/material/svn SVN repository of graphical material] (for access see [[Development Server]])
* [https://iridia-dev.ulb.ac.be/projects/optbib/svn SVN repository of BiBTeX files] (for access see [[Development Server]])
* [https://iridia-dev.ulb.ac.be/projects/optsrc/svn SVN repository of optimization source code and benchmark problems] (for access see [[Development Server]])
* [[ProceedingsRepository| Repository of Proceedings]]
<center>[[#Top | Back to top]]</center>

Tracking System

2013-11-26T18:07:44Z

Manubrambi:

IRIDIA is currently developing a Tracking System for the robots experiments taking place in the Robotics Arena. The purpose of the Tracking System is to detect the position of all the robots in the Arena, together with recording and storing the frames of the whole experiments.

==Hardware==
The tracking system is composed by a set of 16 cameras Prosilica GC1600 C, disposed as shown in [[File:Figure 1]]. The cameras are set in order to have a collective field of view that covers the entire area of the Arena. The cameras are connected to a dedicated computer, the Arena Tracking System Server, through Ethernet connection. The Arena Tracking System Server hosts the API that the users of the Tracking System can exploit to configure, run and record their experiments. (Ref: Alessandro Stranieri, Arena Tracking System)

==Software==
The Arena Tracking System's API are built on top of the Halcon library. Halcon is a library by MVTec Software GmbH, used for scientific and industrial computer vision applications. It provides an extensive set of optimized operators and it comes along with a full featured IDE that allows for fast prototyping of computer vision programs, camera calibration and configuration utilities. The library also provides drivers to easily interface with a broad range of cameras. (Ref: Alessandro Stranieri, Arena Tracking System)

==Licence==
IRIDIA owns a Floating Development Licence. That means that the library can be installed on any computer in the local network, but can be used by at most one of them at any time.

==How to access==
The Halcon library is hosted in the Arena Traking System Server. In order to use the library is necessary to access the node throgh Secure Shell. There are two available interfaces for this server:

IP alias bandwidth.

164.15.10.153 liebig.ulb.ac.be 1 Gbit.

169.254.0.200 10 Gbit

==Adapter for the cameras==
In case you need an adaptor for the cameras to attach them to a standard camera mount (e.g., Manfrotto), you can create one using the laser cutter.
Here are the files: [http://iridia.ulb.ac.be/~mbrambilla/files/camera_adapter.ecp ecp version] [http://iridia.ulb.ac.be/~mbrambilla/files/camera_adapter.dxf dxf version]

TAM/Status

2013-11-14T11:45:25Z

Manubrambi: /* Status of all TAMs at IRIDIA */

== Status of all TAMs at IRIDIA ==

This list gives the status of all TAMs at IRIDIA.

If you use the TAMs, '''PLEASE KEEP THIS LIST UP-TO DATE'''

{| class="wikitable" style="align: left; border: 1px solid grey"
! # !! Status !! Notes
|-
| 01 || OK
|-
| 02 || OK
|-
| 03 || OK
|-
| 04 || OK
|-
| 05 || OK
|-
| 06 || OK
|-
| 07 || OK || diffuser slightly too small
|-
| 08 || OK
|-
| 09 || OK
|-
| 10 || OK
|-
| 11 || OK
|-
| 12 || OK
|-
| 13 || OK
|-
| 14 || OK
|-
| 15 || OK
|-
| 16 || OK
|-
| 17 || OK
|-
| 18 || OK
|-
| 19 || OK
|-
| 20 || OK
|-
| 21 || OK
|-
| 22 || OK
|-
| 23 || OK
|-
| 24 || OK
|-
| 25 || OK
|-
| 26 || OK
|-
| 27 || OK
|-
| 28 || OK
|-
| 29 || OK
|-
| 30 || OK
|-
| 31 || OK
|-
| 32 || OK
|-
| 33 || OK
|-
| 34 || OK
|-
| 35 || OK
|-
| 36 || OK
|-
| 37 || OK
|-
| 38 || OK
|-
| 39 || OK
|-
| 40 || OK
|-
| 41 || OK
|-
| 42 || OK
|-
| 43 || OK
|-
| 44 || OK
|-
| 45 || OK
|-
| 46 || OK
|-
| 47 || OK
|-
| 48 || OK
|-
| 49 || OK
|-
| 50 || OK
|-
|}

Laser Cutter

SRSP

2013-10-18T15:23:28Z

Manubrambi: /* External Links */

'''Swarm robotics''' studies how to design a large group of robots that operate without relying on any external infrastructure and on any form of centralized control. In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act. The design of robot swarms is guided by swarm intelligence principles, which promote the realization of systems that are robust, scalable and flexible.
Swarm robotics appears to be a promising approach when different activities must be performed concurrently, when high redundancy and the lack of a single point of failure are desired, and when
it is technically unfeasible to setup an infrastructure to control the robots in a centralized way. Examples of tasks that could be profitably tackled using swarm robotics are demining, search and rescue, planetary or underwater exploration, and surveillance.

==Characteristics of swarm robotics==

A robot swarm is a large and highly redundant group of autonomous robots that act in a self-organized way and cooperate to accomplish a given task. Robotsâ€™ sensing and communication capabilities are local and robots do not have access to global information. The collective behavior of the robot swarm results from the interactions of each individual robot with its neighboring peers and with the environment.

==Desirable properties of swarm robotics systems==

The aforementioned characteristics of swarm robotics are deemed to promote the realization of systems that are robust, scalable and flexible.

A robot swarm is able to cope well with the failure of one or more of its individuals: the loss of individuals does not imply the failure of the whole swarm. Robustness to failures is promoted by the high redundancy of the system: the swarm does not rely on any centralized control entity, leaders, or any individual robot playing a predefined role.

A robot swarms is also able to cope well with changes in its group size: the introduction or removal of individuals does not result in a drastic change in the performance of the swarm. Scalability is promoted by local sensing and communication: provided that the introduction and removal does not dramatically change the density of the robots, each individual robot will keep interacting with approximately the same number of robots, those that are in its sensing and communication range.

Finally, robot swarms are flexible, that is, are able to cope well with a broad spectrum of different environment and tasks. Flexibility is promoted by relying only on local sensing and communication and behavioral mechanisms as, for example, task allocation.

== Potential applications of swarm robotics ==

The properties of swarm robotics systems make them appealing in several potential fields of application.


The use of robots for tackling dangerous tasks is clearly appealing as it eliminates or reduces the risks for humans. The dangerous nature of these tasks implies a high risk of losing robots. Therefore, a solution that is robust to failure is necessary, making dangerous tasks an ideal field of application for robot swarms. Example of dangerous tasks that can be tackled using robot swarms are demining, search and rescue, and toxic cleaning.


Other potential applications for robot swarms are those in which it is difficult or even impossible to estimate in advance the resources needed to accomplish the task. For instance, estimating the resources needed to manage an oil leak can be very hard because it is often difficult to estimate the oil output and foreseen its development. In these cases, a solution is needed that can scale and easily adapt. A robot swarm could be a appealing solution: robots can be added or removed in time to provide the appropriate amount of resources and fit the requirements of the specific task. Example of tasks that might require an ''a priori'' unknown amount of resources are search and rescue, transportation of large objects, tracking, cleaning.


Another potential field of application for swarm robotics are tasks that have to be accomplished in large or unstructured environments, in which there is no available infrastructure that can be used to control the robots - e.g., no available communication network or global localization system. Robot swarms are well suited for such applications because they are able to act autonomously without the need of any infrastructure or any form of external coordination. Examples of tasks in unstructured and large environments are planetary or underwater exploration, surveillance, demining, cleaning, search and rescue.


Some applications take place in environments that might rapidly change over time. For instance, in a post earthquake situation, buildings might collapse changing the layout of the environment and creating new hazards. In these cases, it is necessary to adopt solutions that are flexible and can react fast to events. Robot swarms are flexible systems that can rapidly adapt to new operating conditions. Example of applications in environments that change over time are patrolling, disaster recovery, search and rescue, cleaning.

==Scientific implications of swarm robotics==

Beside being relevant to engineering applications, swarm robotics is also a valuable scientific tool. Indeed, several models of natural swarm intelligence systems have been refined and validated using robot swarms. For example, Garnier et al. (2005) validated the model of a collective decision-making behavior in cockroaches using robot swarms.

Swarm robotics has been used also to investigate, via controlled experiments, the conditions under which some complex social behaviors might result out of an evolutionary process. For example, robot swarms have been used to study the evolution of communication (Mitri et al., 2009) and collective decision making (Halloy et al., 2007).

==Current research axis==

In this section, we present the main research axis of the current research in swarm robotics. For a comprehensive review of the literature, see Brambilla et al. (2013).

====Design====

The design of a robot swarm is a difficult endeavor: requirements are usually expressed at the collective level, but, eventually, the designer needs to define what the individual robots should do so that their interactions result in the desired collective behavior.
Approaches to the design problem in swarm robotics can be divided in two categories: manual design and automatic design.

In '''manual design''', the designer follows a trial-and-error process in which the behaviors of the individual robot are developed, tested and improved until the desired collective behavior is obtained. The software architecture that is most commonly adopted in swarm robotics is the ''probabilistic finite state machine''. Probabilistic finite state machines have been used to obtain several collective behaviors, including aggregation (Soysal and Sahin, 2005), chain formation (Nouyan et al., 2009), and task allocation (Liu et al., 2007). Another common approach is based on ''virtual physics''. In this approach, robots and environment interact through virtual forces. This approach is particularly suited for spatially organizing collective behaviors, such as pattern formation (Spears et al. 2004) and collective motion (Ferrante et al., 2012). Currently, the main limit of manual design is that it completely relies on the ingenuity and expertise of the human designer: designing a robot swarm is more of an art than a science. A systematic and general way to design robot swarms is still missing, even though some preliminary proposal has been made (Hamann and Worn, 2008; Berman et al., 2011; Brambilla et al., 2012).

In swarm robotics '''automatic design''' has been mostly performed using the evolutionary robotics approach (Nolfi and Floreano, 2004). Typically, individual robots are controlled by a neural network whose parameters are obtained via artificial evolution (Trianni and Nolfi, 2011). Evolutionary robotics has been used to develop several collective behaviors including aggregation (Trianni et al., 2003) and collective transport (GroÃŸ and Dorigo, 2008). One of the main limits of evolutionary robotics is that it is often difficult to define all the element of the evolutionary process that will produce control software that allows the robot swarm to effectively accomplish the given task.

TODO: remove.
An alternative approach to automatically design robot swarms has been recently proposed. In this approach, probabilistic finite state machines are automatically assembled starting from pre-available modular components using an optimization algorithm (Francesca et al., 2014).

====Analysis====
The analysis of a robot swarm usually relies on models. A model of a robot swarm can be realized in two ways: by modeling the behaviors of the individual robots, what it is called modeling the microscopic level; or by modeling the collective behavior of the swarm, what it is called modeling the macroscopic level.

Modeling the microscopic level allows one to represent in a detailed way the actions of all the individual robots composing the swarm. Unfortunately, microscopic modeling is problematic due to the very large number of robots involved. Often, microscopic modeling relies on computer-based simulations (Kramer and Scheutz, 2007; Pinciroli et al., 2012).

Macroscopic models avoid the complexity and scalability issues of having to model each individual robot, by considering only the collective behavior of the swarm. One of the most common macroscopic modeling approach is the use of ''rate'' or ''differential equations'' (Martinoli et al., 2004; Lerman et al., 2005). Rate equations describe the time evolution of the ratio of robots in a particular state, that is, of robots that are performing a specific action or are in a specific area of the environment. Another common approach is the use of ''Markov chains'', which allow researchers to formally verify properties of a robot swarm (Dixon et al., 2011; Konur et al., 2012). Control theory has also been used to analyze whether a robot swarm eventually converges to a desired macroscopic state (Liu and Passino, 2004; Hsieh et al., 2008).

A hybrid way of modeling robot swarms is based on ''Fokker-Plank'' and ''Langevin'' equations (Hamann and Worn, 2008; Berman et al., 2011; Prorok et al., 2011). Using these equations, one can model both the behavior of the individual robot, in the form of a deterministic component of the model; and the collective level of the swarm, in the form of a stochastic component of the model.

====Collective behaviors====
A large part of the research effort in swarm robotics is directed towards the study of collective behaviors.
Collective behaviors can be categorized into four main groups: spatially organizing behaviors, navigation behaviors, collective decision-making behaviors, and other behaviors.


Spatially-organizing behaviors focus on how to organize and distribute robots and objects in space.
Examples of such behaviors are aggregation (Soysal and á¹¢ahin, 2005), pattern formation (Spears et al. 2004), chain formation (Nouyan et al. 2009), self-assembly (O'Grady et al., 2010), and object clustering and assembling (Werfel et al., 2011).


Navigation behaviors focus on how to coordinate the movements of a robot swarm.
Examples of such behaviors are collective exploration (Ducatelle et al., 2011a), collective motion (Turgut et al., 2008), and collective transport (Baldassarre et al., 2006).


Collective decision-making focuses on how robots influence each other in making decisions.
In particular, collective decision-making can be used to achieve consensus on a single alternative (Garnier et al., 2005) or allocation to different alternatives (Pini et al., 2011).


Other behaviors that do not fall in the previously mentioned categories are collective fault detection (Christensen et al. 2009), group size regulation (Pinciroli et al, 2013), and human-swarm interaction (Naghsh et al. 2008).

==Open issues==

Despite its potential to promote robustness, scalability and flexibility, swarm robotics has yet to be adopted for solving real-world problems. This is possibly due to the hardware limitations of the currently available robots and to the lack of an engineering approach for swarm robotics. In particular, the main open issues are the lack of a general methodology for designing robot swarms, the lack of well defined metrics and testbed to assess their performance, and the lack of formal ways to verify and guarantee their properties.

== References ==

G. Baldassarre, D. Parisi, and S. Nolfi. Distributed coordination of simulated robots based on self-organization. ''Artificial Life'', 12(3):289â€“311, 2006.

S. Berman, Ã. M. HalÃ¡sz, M. A. Hsieh, and V. Kumar. Optimized stochastic policies for task allocation in swarms of robots. ''IEEE Transactions on Robotics'', 25(4):927â€“937, 2009.

S. Berman, V. Kumar, and R. Nagpal. Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. ''IEEE International Conference on Robotics and Automation (ICRA)'', pp 378â€“385. 2011.

M. Brambilla, C. Pinciroli, M. Birattari, and M. Dorigo. Property-driven design for swarm robotics. In ''Proceedings of the AAMAS 2012'', pp 139â€“146, 2012.

M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo. Swarm robotics: a review from the swarm engineering perspective. ''Swarm Intelligence'', 7(1):1â€“41, 2013.

A. L. Christensen, R. Oâ€™Grady, and M. Dorigo. From fireflies to fault-tolerant swarms of robots. ''IEEE Transactions on Evolutionary Computation'', 13(4):754â€“766, 2009.

C. Dixon, A. Winfield, and M. Fisher. Towards temporal verification of emergent behaviours in swarm robotic systems. In ''Lecture notes in computer science: Vol. 6856. Towards autonomous robotic systems'', pp. 336â€“347, 2011.

F. Ducatelle, G. A. Di Caro, C. Pinciroli, F. Mondada, and L. M. Gambardella. Communication assisted navigation in robotic swarms: self-organization and cooperation. In ''Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2011)'', pp. 4981â€“4988, 2011.

S. Garnier, C. Jost, R. Jeanson, J. Gautrais, M. Asadpour, G. Caprari, and G. Theraulaz. Aggregation behaviour as a source of collective decision in a group of cockroach-like robots. In ''Lecture notes in artificial intelligence: Vol. 3630. Advances in artificial life'', pp. 169â€“178, 2005.

J. Halloy, G. Sempo, G. Caprari, C. Rivault, M. Asadpour, F. TÃ¢che, I. Said, V. Durier, S. Canonge, J.M. AmÃ©, C. Detrain, N. Correll, A. Martinoli, F. Mondada, R. Siegwart, J.-L. Deneubourg. Social integration of robots into groups of cockroaches to control self-organized choices. ''Science'', 318(5853):1155â€“1158, 2007.

H. Hamann and H. WÃ¶rn. A framework of space-time continuous models for algorithm design in swarm robotics. ''Swarm Intelligence'', 2(2â€“4):209â€“239, 2008.

M. A. Hsieh, Ã. HalÃ¡sz, S. Berman and V. Kumar. Biologically inspired redistribution of a swarm of robots among multiple sites. ''Swarm Intelligence'', 2(2â€“4):121â€“141, 2008.

S. Konur, C. Dixon, and M. Fisher. Analysing robot swarm behaviour via probabilistic model checking. ''Robotics and Autonomous Systems'', 60(2):199â€“213, 2012.

J. Kramer and M. Scheutz. Development environments for autonomous mobile robots: a survey. ''Autonomous Robots'', 22(2), 101â€“132, 2007.

K. Lerman, A. Martinoli, and A. Galstyan. A review of probabilistic macroscopic models for swarm robotic systems. In ''Lecture Notes in Computer Science: Vol. 3342. Swarm robotics'', pp 143â€“152, 2005.

Y. Liu and K. M. Passino. Stable social foraging swarms in a noisy environment. ''IEEE Transactions on Automatic Control'', 49(1):30â€“44, 2004.

W. Liu, A. F. T. Winfield, J. Sa, J. Chen, and L. Dou. Towards energy optimization: emergent task allocation in a swarm of foraging robots. ''Adaptive Behavior'', 15(3):289â€“305, 2007.

A. Martinoli, K. Easton, and W. Agassounon. Modeling swarm robotic systems: a case study in collaborative distributed manipulation. ''The International Journal of Robotics Research'', 23(4â€“5):415â€“436, 2004.

S. Mitri, D. Floreano, and L. Keller. The evolution of information suppression in communicating robots with conflicting interests. ''PNAS'', 106(37):15786-15790, 2009;

A. Naghsh, J. Gancet, A. Tanoto, and C. Roast. Analysis and design of human-robot swarm interaction in firefighting. In ''Proceedings of the 17th IEEE international symposium on the robot and human interactive communication (Ro-man 2008)'', pp. 255â€“260, 2008.

S. Nolfi and D. Floreano. ''Evolutionary robotics: intelligent robots and autonomous agents''. Cambridge: MIT Press, 2000.

S. Nouyan, R. GroÃŸ, M. Bonani, F. Mondada, and M. Dorigo. Teamwork in self-organized robot colonies. ''IEEE Transactions on Evolutionary Computation'', 13(4):695â€“711, 2009.

R. Oâ€™Grady, R. GroÃŸ, A. L. Christensen, and M. Dorigo. Self-assembly strategies in a group of autonomous mobile robots. ''Autonomous Robots'', 28(4):439â€“455, 2010.

C. Pinciroli, V. Trianni, R. O'Grady, G. Pini, A. Brutschy, M. Brambilla, N. Mathews, E. Ferrante, G. Di Caro, F. Ducatelle, M. Birattari, L. M. Gambardella and M. Dorigo. ARGoS: a Modular, Parallel, Multi-Engine Simulator for Multi-Robot Systems. ''Swarm Intelligence'', 6(4):271-295, 2012.

C. Pinciroli, R. O'Grady, A. L. Christensen, M. Birattari and M. Dorigo. Parallel Formation of Differently Sized Groups in a Robotic Swarm. ''SICE Journal of the Society of Instrument and Control Engineers'', 52(3):213-226, 2013.

G. Pini, A. Brutschy, M. Frison, A. Roli, M. Dorigo, and M. Birattari. Task partitioning in swarms of robots: An adaptive method for strategy selection. ''Swarm Intelligence'', 5(3-4):283-304, 2011.

A. Prorok, N. Correll, and A. Martinoli. Multi-level spatial modeling for stochastic distributed robotic systems. ''The International Journal of Robotics Research'', 30(5):574â€“589, 2011.

O. Soysal and E. Åžahin. Probabilistic aggregation strategies in swarm robotic systems. In ''Proceedings of the IEEE swarm intelligence symposium'', pp. 325â€“332, 2005

W. M. Spears, D. F. Spears, J. C. Hamann, and R. Heil. Distributed, physics-based control of swarms of vehicles. ''Autonomous Robots'', 17(2â€“3):137â€“162. 2004.

V. Trianni and S. Nolfi. Engineering the evolution of self-organizing behaviors in swarm robotics: A case study. ''Artificial Life'', 17(3):183â€“202, 2011.

A. E. Turgut, H. Ã‡elikkanat, F. GÃ¶kÃ§e, and E. á¹¢ahin. Self-organized flocking in mobile robot swarms. ''Swarm Intelligence'', 2(2â€“4): 97â€“120, 2008.

J. Werfel, K. Petersen, and R. Nagpal. Distributed multi-robot algorithms for the TERMES 3D collective construction system. In ''Proceedings of the IEEE/RSJ international conference on intelligent robots and systems'' (IROS), 2011.

== External Links ==

* [http://www.springer.com/11721 Swarm Intelligence]: The main journal in the field.
* [http://iridia.ulb.ac.be/~ants ''ANTS - International Conference on Swarm Intelligence'']: This series of conferences, held for the first time in 1998, is the oldest in the swarm intelligence field.
* [http://www.computelligence.org/sis/2006/past.php ''IEEE Swarm Intelligence Symposia'']: Another series of conferences dedicated to swarm intelligence, started in 2013.
* [http://www.swarm-bots.org/ Swarm-bots]: research project 2001-2005
* [http://www.swarms.org/ Swarms]: research project 2003-2007
* [http://www.i-swarm.org/ iSwarm project]: research project 2005-2008
* [http://www.swarmanoid.org/ Swarmanoid]: research project 2006-2010
* [http://symbrion.org/tiki-index.php Symbrion]: research project 2008-2013
* [http://www.e-swarm.org/ E-Swarm]: research project 2010-2015

2013-10-18T12:38:52Z

Manubrambi: /* References */

'''Swarm robotics''' studies how to design a large group of robots that operate without relying on any external infrastructure and on any form of centralized control. In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act. The design of robot swarms is guided by swarm intelligence principles, which promote the realization of systems that are robust, scalable and flexible.
Swarm robotics appears to be a promising approach when different activities must be performed concurrently, when high redundancy and the lack of a single point of failure are desired, and when
it is technically unfeasible to setup an infrastructure to control the robots in a centralized way. Examples of tasks that could be profitably tackled using swarm robotics are demining, search and rescue, planetary or underwater exploration, and surveillance.

==Characteristics of swarm robotics==

A robot swarm is a large and highly redundant group of autonomous robots that act in a self-organized way and cooperate to accomplish a given task. Robotsâ€™ sensing and communication capabilities are local and robots do not have access to global information. The collective behavior of the robot swarm results from the interactions of each individual robot with its neighboring peers and with the environment.

==Desirable properties of swarm robotics systems==

The aforementioned characteristics of swarm robotics are deemed to promote the realization of systems that are robust, scalable and flexible.

A robot swarm is able to cope well with the failure of one or more of its individuals: the loss of individuals does not imply the failure of the whole swarm. Robustness to failures is promoted by the high redundancy of the system: the swarm does not rely on any centralized control entity, leaders, or any individual robot playing a predefined role.

A robot swarms is also able to cope well with changes in its group size: the introduction or removal of individuals does not result in a drastic change in the performance of the swarm. Scalability is promoted by local sensing and communication: provided that the introduction and removal does not dramatically change the density of the robots, each individual robot will keep interacting with approximately the same number of robots, those that are in its sensing and communication range.

Finally, robot swarms are flexible, that is, are able to cope well with a broad spectrum of different environment and tasks. Flexibility is promoted by relying only on local sensing and communication and behavioral mechanisms as, for example, task allocation.

== Potential applications of swarm robotics ==

The properties of swarm robotics systems make them appealing in several potential fields of application.


The use of robots for tackling dangerous tasks is clearly appealing as it eliminates or reduces the risks for humans. The dangerous nature of these tasks implies a high risk of losing robots. Therefore, a solution that is robust to failure is necessary, making dangerous tasks an ideal field of application for robot swarms. Example of dangerous tasks that can be tackled using robot swarms are demining, search and rescue, and toxic cleaning.


Other potential applications for robot swarms are those in which it is difficult or even impossible to estimate in advance the resources needed to accomplish the task. For instance, estimating the resources needed to manage an oil leak can be very hard because it is often difficult to estimate the oil output and foreseen its development. In these cases, a solution is needed that can scale and easily adapt. A robot swarm could be a appealing solution: robots can be added or removed in time to provide the appropriate amount of resources and fit the requirements of the specific task. Example of tasks that might require an ''a priori'' unknown amount of resources are search and rescue, transportation of large objects, tracking, cleaning.


Another potential field of application for swarm robotics are tasks that have to be accomplished in large or unstructured environments, in which there is no available infrastructure that can be used to control the robots - e.g., no available communication network or global localization system. Robot swarms are well suited for such applications because they are able to act autonomously without the need of any infrastructure or any form of external coordination. Examples of tasks in unstructured and large environments are planetary or underwater exploration, surveillance, demining, cleaning, search and rescue.


Some applications take place in environments that might rapidly change over time. For instance, in a post earthquake situation, buildings might collapse changing the layout of the environment and creating new hazards. In these cases, it is necessary to adopt solutions that are flexible and can react fast to events. Robot swarms are flexible systems that can rapidly adapt to new operating conditions. Example of applications in environments that change over time are patrolling, disaster recovery, search and rescue, cleaning.

==Scientific implications of swarm robotics==

Beside being relevant to engineering applications, swarm robotics is also a valuable scientific tool. Indeed, several models of natural swarm intelligence systems have been refined and validated using robot swarms. For example, Garnier et al. (2005) validated the model of a collective decision-making behavior in cockroaches using robot swarms.

Swarm robotics has been used also to investigate, via controlled experiments, the conditions under which some complex social behaviors might result out of an evolutionary process. For example, robot swarms have been used to study the evolution of communication (Mitri et al., 2009) and collective decision making (Halloy et al., 2007).

==Current research axis==

In this section, we present the main research axis of the current research in swarm robotics. For a comprehensive review of the literature, see Brambilla et al. (2013).

====Design====

The design of a robot swarm is a difficult endeavor: requirements are usually expressed at the collective level, but, eventually, the designer needs to define what the individual robots should do so that their interactions result in the desired collective behavior.
Approaches to the design problem in swarm robotics can be divided in two categories: manual design and automatic design.

In '''manual design''', the designer follows a trial-and-error process in which the behaviors of the individual robot are developed, tested and improved until the desired collective behavior is obtained. The software architecture that is most commonly adopted in swarm robotics is the ''probabilistic finite state machine''. Probabilistic finite state machines have been used to obtain several collective behaviors, including aggregation (Soysal and Sahin 2005), chain formation (Nouyan et al. 2009), and task allocation (Liu et al. 2007). Another common approach is based on ''virtual physics''. In this approach, robots and environment interact through virtual forces. This approach is particularly suited for spatially organizing collective behaviors, such as pattern formation (Spears et al. 2004) and collective motion (Ferrante et al., 2012). Currently, the main limit of manual design is that it completely relies on the ingenuity and expertise of the human designer: designing a robot swarm is more of an art than a science. A systematic and general way to design robot swarms is still missing, even though some preliminary proposal has been made (Hamann and Worn, 2008; Berman et al., 2011; Brambilla et al., 2012).

In swarm robotics '''automatic design''' has been mostly performed using the evolutionary robotics approach (Nolfi and Floreano, 2004). Typically, individual robots are controlled by a neural network whose parameters are obtained via artificial evolution (Trianni and Nolfi, 2011). Evolutionary robotics has been used to develop several collective behaviors including aggregation (Trianni et al., 2003) and collective transport (GroÃŸ and Dorigo, 2008). One of the main limits of evolutionary robotics is that it is often difficult to define all the element of the evolutionary process that will produce control software that allows the robot swarm to effectively accomplish the given task.

TODO: remove.
An alternative approach to automatically design robot swarms has been recently proposed. In this approach, probabilistic finite state machines are automatically assembled starting from pre-available modular components using an optimization algorithm (Francesca et al., 2014).

====Analysis====
The analysis of a robot swarm usually relies on models. A model of a robot swarm can be realized in two ways: by modeling the behaviors of the individual robots, what it is called modeling the microscopic level; or by modeling the collective behavior of the swarm, what it is called modeling the macroscopic level.

Modeling the microscopic level allows one to represent in a detailed way the actions of all the individual robots composing the swarm. Unfortunately, microscopic modeling is problematic due to the very large number of robots involved. Often, microscopic modeling relies on computer-based simulations (Kramer and Scheutz, 2007; Pinciroli et al., 2012).

Macroscopic models avoid the complexity and scalability issues of having to model each individual robot, by considering only the collective behavior of the swarm. One of the most common macroscopic modeling approach is the use of ''rate'' or ''differential equations'' (Lerman et al. 2005). Rate equations describe the time evolution of the ratio of robots in a particular state, that is, of robots that are performing a specific action or are in a specific area of the environment. Another common approach is the use of ''Markov chains'', which allow researchers to formally verify properties of a robot swarm (Dixon et al., 2011; Massink et al., 2013). Control theory has also been used to analyze whether a robot swarm eventually converges to a desired macroscopic state (Liu and Passino, 2004; Hsieh et al., 2008).

A hybrid way of modeling robot swarms is based on ''Fokker-Plank'' and ''Langevin'' equations (Hamann and Worn, 2008; Berman et al., 2011; Prorok et al., 2011). Using these equations, one can model both the behavior of the individual robot, in the form of a deterministic component of the model; and the collective level of the swarm, in the form of a stochastic component of the model.

====Collective behaviors====
A large part of the research effort in swarm robotics is directed towards the study of collective behaviors.
Collective behaviors can be categorized into four main groups: spatially organizing behaviors, navigation behaviors, collective decision-making behaviors, and other behaviors.


Spatially-organizing behaviors focus on how to organize and distribute robots and objects in space.
Examples of such behaviors are aggregation (Trianni et al., 2003), pattern formation (Spears et al. 2004), chain formation (Nouyan et al. 2009), self-assembly (O'Grady et al., 2010), and object clustering and assembling (Werfel et al., 2011).


Navigation behaviors focus on how to coordinate the movements of a robot swarm.
Examples of such behaviors are collective exploration (Ducatelle et al., 2011a), collective motion (Ferrante et al., 2012), and collective transport (Baldassarre et al., 2006).


Collective decision-making focuses on how robots influence each other in making decisions.
In particular, collective decision-making can be used to achieve consensus on a single alternative (Garnier et al., 2005) or allocation to different alternatives (Pini et al., 2009).


Other behaviors that do not fall in the previously mentioned categories are collective fault detection (Christensen et al. 2009), group size regulation (Pinciroli et al, 2013), and human-swarm interaction (Naghsh et al. 2008).

==Open issues==

Despite its potential to promote robustness, scalability and flexibility, swarm robotics has yet to be adopted for solving real-world problems. This is possibly due to the hardware limitations of the currently available robots and to the lack of an engineering approach for swarm robotics. In particular, the main open issues are the lack of a general methodology for designing robot swarms, the lack of well defined metrics and testbed to assess their performance, and the lack of formal ways to verify and guarantee their properties.

== References ==

G. Baldassarre, D. Parisi, and S. Nolfi. Distributed coordination of simulated robots based on self-organization. ''Artificial Life'', 12(3):289â€“311, 2006.

S. Berman, Ã. M. HalÃ¡sz, M. A. Hsieh, and V. Kumar. Optimized stochastic policies for task allocation in swarms of robots. ''IEEE Transactions on Robotics'', 25(4):927â€“937, 2009.

S. Berman, V. Kumar, and R. Nagpal. Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. ''IEEE International Conference on Robotics and Automation (ICRA)'', pp 378â€“385. 2011.

M. Brambilla, C. Pinciroli, M. Birattari, and M. Dorigo. Property-driven design for swarm robotics. In ''Proceedings of the AAMAS 2012'', pp 139â€“146, 2012.

M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo. Swarm robotics: a review from the swarm engineering perspective. ''Swarm Intelligence'', 7(1):1â€“41, 2013.

A. L. Christensen, R. Oâ€™Grady, and M. Dorigo. From fireflies to fault-tolerant swarms of robots. ''IEEE Transactions on Evolutionary Computation'', 13(4):754â€“766, 2009.

C. Dixon, A. Winfield, and M. Fisher. Towards temporal verification of emergent behaviours in swarm robotic systems. In ''Lecture notes in computer science: Vol. 6856. Towards autonomous robotic systems'', pp. 336â€“347, 2011.

F. Ducatelle, G. A. Di Caro, C. Pinciroli, F. Mondada, and L. M. Gambardella. Communication assisted navigation in robotic swarms: self-organization and cooperation. In ''Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2011)'', pp. 4981â€“4988, 2011.

E. Ferrante, A. E. Turgut, C. Huepe, A. Stranieri, C. Pinciroli, and M. Dorigo, Self-organized flocking with a mobile robot swarm: a novel motion control method. ''Adaptive Behavior'', 20(6):460-477, 2012.

S. Garnier, C. Jost, R. Jeanson, J. Gautrais, M. Asadpour, G. Caprari, and G. Theraulaz. Aggregation behaviour as a source of collective decision in a group of cockroach-like robots. In ''Lecture notes in artificial intelligence: Vol. 3630. Advances in artificial life'', pp. 169â€“178, 2005.

J. Halloy, G. Sempo, G. Caprari, C. Rivault, M. Asadpour, F. TÃ¢che, I. Said, V. Durier, S. Canonge, J.M. AmÃ©, C. Detrain, N. Correll, A. Martinoli, F. Mondada, R. Siegwart, J.-L. Deneubourg. Social integration of robots into groups of cockroaches to control self-organized choices. ''Science'', 318(5853):1155â€“1158, 2007.

H. Hamann and H. WÃ¶rn. A framework of space-time continuous models for algorithm design in swarm robotics. ''Swarm Intelligence'', 2(2â€“4):209â€“239, 2008.

M. A. Hsieh, Ã. HalÃ¡sz, S. Berman and V. Kumar. Biologically inspired redistribution of a swarm of robots among multiple sites. ''Swarm Intelligence'', 2(2â€“4):121â€“141, 2008.

J. Kramer and M. Scheutz. Development environments for autonomous mobile robots: a survey. ''Autonomous Robots'', 22(2), 101â€“132, 2007.

K. Lerman, A. Martinoli, and A. Galstyan. A review of probabilistic macroscopic models for swarm robotic systems. In ''Lecture Notes in Computer Science: Vol. 3342. Swarm robotics'', pp 143â€“152, 2005.

Y. Liu and K. M. Passino. Stable social foraging swarms in a noisy environment. ''IEEE Transactions on Automatic Control'', 49(1):30â€“44, 2004.

W. Liu, A. F. T. Winfield, J. Sa, J. Chen, and L. Dou. Towards energy optimization: emergent task allocation in a swarm of foraging robots. ''Adaptive Behavior'', 15(3):289â€“305, 2007.

M. Massink, M. Brambilla, D. Latella, M. Dorigo, and M. Birattari. On the use of bio-pepa for modelling and analysing collective behaviours in swarm robotics. ''Swarm Intelligence'', 7(2-3):201-228, 2013.

S. Mitri, D. Floreano, and L. Keller. The evolution of information suppression in communicating robots with conflicting interests. ''PNAS'', 106(37):15786-15790, 2009;

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== External Links ==

* [http://www.springer.com/11721 Swarm Intelligence]: The main journal in the field.
* [http://iridia.ulb.ac.be/~ants ''ANTS - International Conference on Swarm Intelligence'']: This series of conferences, held for the first time in 1998, is the oldest in the swarm intelligence field.
* [http://www.computelligence.org/sis/2006/past.php ''IEEE Swarm Intelligence Symposia'']: Another series of conferences dedicated to swarm intelligence, started in 2013.
* [http://www.swarm-bots.org/ Swarm-bots]: 2001-2005
* [http://www.swarms.org/ Swarms]: 2003-2007
* [http://www.i-swarm.org/ iSwarm project]: 2005-2008
* [http://www.swarmanoid.org/ Swarmanoid]: 2006-2010
* [http://symbrion.org/tiki-index.php Symbrion]: 2008-2013