ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems

Pinciroli, Carlo; Trianni, Vito; O’Grady, Rehan; Pini, Giovanni; Brutschy, Arne; Brambilla, Manuele; Mathews, Nithin; Ferrante, Eliseo; Di, Gianni A.; Ducatelle, Frederick; Birattari, Mauro; Gambardella, Luca Maria; Dorigo, Marco

doi:10.1007/s11721-012-0072-5

Cited by 453 publications

(285 citation statements)

References 19 publications

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“…Vaughan (2008) proposed a benchmark to study scalability in multi-robot simulators and applied it to the Stage simulator. Pinciroli et al (2012) developed a simulator for swarm robotics by focusing explicitly on the scalability issue which was able to simulate 10 5 robots in real time. For a survey of various simulation platforms in robotics see Kramer and Scheutz (2007).…”

Section: Microscopic Modelsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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Section: Microscopic Modelsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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“…All experiments are performed in the ARGoS simulation environment (Pinciroli et al 2012). 1 The simulation takes place in continuous space and it is updated 10 times per second.…”

Section: Simulation Environment and Swarm Missionsmentioning

confidence: 99%

The Information-Cost-Reward framework for understanding robot swarm foraging

2017

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Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the InformationCost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm's ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the informationcentred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

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“…More recently, there have been more generic simulators proposed like Player/Stage [17], gazebo [18], USSR [19] or MORSE [20] but network access is not sufficiently detailed, as focus is made on the robots themselves. The most interesting initiative is ARGoS [21] which includes its own network simulator or that could be plugged to NS2 or NS3 [22]. ARGoS is fast and can simulate up to 100,000 of robots.…”

Section: Wireless Network Simulation For Modular Robotsmentioning

confidence: 99%

Efficient Simulation Environment of Wireless Radio Communications in MEMS Modular Robots

Boillot

Dhoutaut

Bourgeois

2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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Abstract-Modular robots needs networking for coordination and it is particularly true for MEMS microrobots. A promising communication technology is nanowireless networking which could be integrated directly into MEMS microrobots, in our case, the catoms of the Claytronics project. A first step towards this objective is to design a wireless simulator able to deal with modular robots. This simulator called Vouivre is integrated in DPRSim a modular robot simulator developed by Intel Research. This paper describes Vouivre and its integration in DPRSim which is an interesting case of integrating different timelines in one simulator. Experiments validate our design and show the interest of using wireless communication in modular robots.

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ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems

Cited by 453 publications

References 19 publications

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics: a review from the swarm engineering perspective

The Information-Cost-Reward framework for understanding robot swarm foraging

Efficient Simulation Environment of Wireless Radio Communications in MEMS Modular Robots

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