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In this paper, we study a heterogeneous robot team composed of self-assembling robots and aerial robots that cooperate with each other to carry out global tasks. We introduce supervised morphogenesis - an approach in which aerial robots exploit their better view of the environment to detect tasks on the ground that require self-assembly, and perform on-board simulations to determine the morphology most adequate to carry out the task. In case existing morphologies on the ground do not match those determined in simulation, aerial robots use a series of enabling mechanisms to initiate and control (hence supervise) the formation of morphologies more adequate to carry out the task. Supervised morphogenesis solely employs LEDs and camera-based local communication between the two robot types. We validate the applicability of our approach in a real-world scenario, in which ground-based robots are given the task to cross an unknown, undulated terrain by forming ad-hoc morphologies under the supervision of an aerial robot.

@inproceedings{MatStrSch-etal2012:aamas,
author = {Nithin Mathews and Alessandro Stranieri and Alexander Scheidler and Marco Dorigo},
title = {Supervised Morphogenesis - Morphology Control of Ground-based Self-Assembling Robots by Aerial Robots},
booktitle = {Proceedings of 1th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012)},
year = {2012},
publisher = {IFAAMAS},
note = {Accepted for publication}
}

Advancements of the state of the art in swarm robotics can be pursued by relying on heterogeneous swarm systems composed of a large number of robots presenting behavioural and/or physical heterogeneities. To this end, it is necessary to develop tools and methodologies that enable the use of such heterogeneous systems. We identified relevant issues and challenges, in particular highlighting the difficulty of delivering the tightly integrated robotic hardware necessary to enable physical and behavioural interaction between different robot types. We present the swarmanoid as a new robotic concept in heterogeneous swarm robotics. The hardware and the software of the swarmanoid robots leverage common technologies to ensure seamless integration of the different platforms. The resulting compatibility of different robot types enable us to explore different coordination mechanisms and strategies in a heterogeneous swarm. The experimental scenario we define demonstrates the suitability of the swarmanoid robotic concept for tackling complex problems in 3D human-made environments.

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@inproceedings{ DorBirOGr-etal2011:movie,
title = { Swarmanoid: The Movie },
author = { M.~Dorigo, M.~Birattari, R.~O'Grady, L.M.~Gambardella, F.~Mondada, D.~Floreano, S.~Nolfi, T.~Baaboura, M.~Bonani, M.~Brambilla, A.~Brutschy, D.~Burnier, A.~Campo, A.L.~Christensen, A.~Decugni\`{e}re, G.~Di Caro, F.~Ducatelle, E.~Ferrante, J.~Martinez Gonzales, J.~Guzzi, V.~Longchamp, S.~Magnenat, N.~Mathews, M.~Montes de Oca, C.~Pinciroli, G.~Pini, J.~Roberts, P.~R\'{e}tornaz, V.~Sperati, F.~Rey, F.~Rochan, T.~Stirling, A.~Stranieri, T.~St\"{u}tzle, V.~Trianni, E.~Tuci, A.E.~Turgut, F.~Vaussard },
booktitle = { AAAI-11 Video Proceedings },
year = { 2011 },
publisher = { AAAI Press },
note = { Winner of the Best Video Award },
url = { http://www.youtube.com/watch?v=M2nn1X9Xlps }
}

We introduce enhanced directional self-assembly (EDSA) -- a novel mechanism for morphology growth through the creation of directed connections in a self-assembling multirobot system. In our approach, a robot inviting a physical connection actively recruits the best located neighboring robot and guides the recruit to the location on its chassis where the connection is required. The proposed mechanism relies on local, high-speed communication between connection inviting robots and their recruits. Communication is based on a hybrid technology that combines radio and infrared to provide local relative positioning information when messages are transmitted between adjacent robots. Experiments with real robotic hardware show that EDSA is precise (misalignment of only 1.2° on average), robust (100% success rate for the experiments in this study) and fast (16.1 seconds on average from a distance of 80 cm). We show how the speed and precision of the new approach enable adaptive recruitment and connection in dynamic environments, a high degree of parallelism, and growth of a moving morphology.

@inproceedings{MatChrOgr-etal2011:iros,
Author = {Nithin Mathews and Anders Lyhne Christensen and Rehan O'Grady and Philippe R\'{e}tornaz and Michael Bonani and Francesco Mondada and Marco Dorigo},
Title = {{E}nhanced {D}irectional {S}elf-{A}ssembly {B}ased on {A}ctive {R}ecruitment and {G}uidance},
Booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
Pages = {4762--4769},
Publisher = {IEEE Computer Society Press},
Address = {Los Alamitos, CA, USA},
Year = {2011}
}

We present ARGoS, a novel open source multirobot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

@inproceedings{PinTriOgr-etal2011:iros,
Author = {Carlo Pinciroli and Vito Trianni and Rehan O'Grady and Giovanni Pini and Arne Brutschy and Manuelle Brambilla and Nithin Mathews and Eliseo Ferrante and Gianni A. {Di Caro} and Frederick Ducatelle and Timothy Stirling and Alvaro Guti\'{e}rrez and Luca Maria Gambardella, and Marco Dorigo},
Title = {{ARGoS}: A Modular, Multi-Engine Simulator for Heterogeneous Swarm Robotics},
Booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
Pages = {5027--5034},
Publisher = {IEEE Computer Society Press},
Address = {Los Alamitos, CA, USA},
Year = {2011}
}

We propose a novel communication strategy inspired by explicit signaling mechanisms seen in vertebrates, in order to improve performance of self-organized flocking for a swarm of mobile robots. The communication strategy is used to make the robots match each other's headings. The task of the robots is to coordinately move towards a common goal direction, which might stay fixed or change over time.
We perform simulation-based experiments in which we evaluate the accuracy of flocking with respect to a given goal direction. In our settings, only some of the robots are informed about the goal direction. Experiments are conducted in stationary and non-stationary environments. In the stationary environment, the goal direction and the informed robots do not change during the experiment. In the non-stationary environment, the goal direction and the informed robots are changed over time. In both environments, the proposed strategy scales well with respect to the swarm size and is robust with respect to noise.

@inproceedings{FerTurMat-etal2010:ppsn,
author = {Eliseo Ferrante and Ali Emre Turgut and Nithin Mathews and Mauro Birattari and Marco Dorigo},
title = {Flocking in Stationary and Non-Stationary Environments: A Novel Communication Strategy for Heading Alignment},
booktitle = {Proceedings of 11th International Conference on Parallel Problem Solving from Nature (PPSN XI), Part II},
year = {2010},
publisher = {Springer-Verlag},
address = {Berlin Heidelberg, Germany}
editors = {R. Schaefer et al. (Eds.)}
pages = {331--340}
}

We consider a heterogeneous swarm robotic system composed of wheeled and aerial robots called foot-bots and eye-bots, respectively. The foot-bots are able to physically connect to one another autonomously and thus form collective robotic entities. Eye-bots have a privileged overview of the environment since they can fly and attach to metal ceilings. In this paper, we show how the heterogeneous swarm can benefit from cooperation. By using so-called spatially targeted communication, the eye-bot is able to communicate with selected groups of foot-bots and instruct them on how to overcome obstacles in their path by forming morphologies appropriate to the obstacle encountered. We conduct experiments in simulation to quantify separately the benefits of cooperation and of spatially targeted communication.

@inproceedings{MatChrOgr-etal2010:ants,
author = {Nithin Mathews and Anders Lyhne Christensen and Rehan O'Grady and Marco Dorigo},
title = {Cooperation in a Heterogeneous Robot Swarm through Spatially Targeted Communication},
booktitle = {Proceedings of the 7th International Conference on Swarm Intelligence (ANTS 2010)},
series = {LNCS},
volume = {6234},
year = {2010},
publisher = {Springer},
address = {Berlin, Germany},
editors = {M. Dorigo et al. (Eds.)}
pages = {400--407}
}

In this paper, we study how an opinion dynamics model can be the core of a collective decision-making mechanism for swarm robotics. Our main result is that when opinions represent action choices, the opin- ion associated with the action that is the fastest to execute spreads in the population. Moreover, the spread of the best choice happens even when only a minority is initially advocating for it. The key elements involved in this process are consensus building and positive feedback. A foraging task that involves collective transport is used to illustrate the potential of the proposed approach.

@inproceedings{MonFerMat-etal2010:ants,
author = {Marco A. {Montes de Oca} and Eliseo Ferrante and Nithin Mathews and Mauro Birattari and Marco Dorigo},
title = {Opinion Dynamics for Decentralized Decision-Making in a Robot Swarm},
booktitle = {Proceedings of the Seventh International Conference on Swarm Intelligence (ANTS 2010)},
year = {2010},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
editors = {M. Dorigo et al. (Eds.)}
pages = {252--263}
note = {Nominated for the Best Paper Award}
}

We consider a heterogeneous swarm consisting of aerial and wheeled robots. We present a system that enables spatially targeted communication. Our system enables aerial robots to establish dedicated communication links with individual wheeled robots or with selected groups of wheeled robots based on their position in the environment. The system does not rely on any form of global information. We show how a spatially targeted one-to-one communication link can be established using a simple LED and camera based communication modality. We provide a probabilistic model of our approach to derive an upper bound on the average time required for establishing communication. In simulation, we show that our approach scales well. Furthermore, we show how our approach can be extended to establish a spatially targeted one-to-many communication link between an aerial robot and a specific number of co-located wheeled robots. The heterogeneous swarm robotic hardware is currently under development. We therefore demonstrate the proposed approach on an existing multirobot system consisting of only wheeled robots by letting one of the wheeled robots assume the role of an aerial robot.

@inproceedings{MatChrFer-etal2010:aamas,
author = {Nithin Mathews and Anders Lyhne Christensen and Eliseo Ferrante and Rehan O'Grady and Marco Dorigo},
title = {Establishing Spatially Targeted Communication in a Heterogeneous Robot Swarm},
booktitle = {Proceedings of 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
year = {2010},
publisher = {IFAAMAS},
address = {Toronto, Canada},
pages = {939--946},
note = {Nominated for the CoTeSys Best Robotics Paper Award}
}

In nature, there are examples of large groups of animals that are capable of making optimal collective-level decisions without the need for global control or information. Understanding the underlying mechanisms of such decentralized decision-making processes may help us to design artifcial systems that exhibit some of the desirable properties, like scalability or fault tolerance, that are usually observed in these natural systems. In this paper, we show how a simple social influence mechanism, based on the binary particle swarm optimization algorithm, can make a whole population of agents achieve consensus on one of two possible choices in a completely decentralized way. Furthermore, we show that, if the conditions for achieving consensus are met and each choice is bound to an action that takes time to perform, the population converges to the choice associated with the shortest execution time. We illustrate the applicability of the decision-making mechanism presented in this paper on an example scenario in swarm robotics.

@incollection{MonFerMat-etal2009:ecal,
author = {Marco A. {Montes de Oca} and Eliseo Ferrante and Nithin Mathews and Mauro Birattari and Marco Dorigo},
title = {Optimal Collective Decision-Making through Social Influence and Different Action Execution Times},
booktitle = {Proceedings of the Workshop on Organisation, Cooperation and Emergence in Social Learning Agents of the European Conference on Artificial Life (ECAL 2009)},
year = {2009},
publisher = {Springer-Verlag},
address = {Berlin, Germany}
}