Collective decision-making based on social odometry

Gutiérrez, Álvaro; Campo, Alexandre; Monasterio-Huelin, Félix; Magdalena, Luis; Dorigo, Marco

doi:10.1007/s00521-010-0380-x

Cited by 50 publications

(61 citation statements)

References 51 publications

(41 reference statements)

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“…Similar trends in relative performances of various robot control algorithms with and without recruitment have been identified by other authors (e.g. Krieger and Billeter 2000;Gutiérrez et al 2010;Wawerla and Vaughan 2010;Sarker and Dahl 2011;Lee et al 2013;Hecker and Moses 2015).…”

Section: Discussionsupporting

confidence: 85%

“…A similar setup has been previously used in, e.g. Balch and Arkin (1994), Pitonakova et al (2016b), Gutiérrez et al (2010).…”

Section: Simulation Environment and Swarm Missionsmentioning

confidence: 99%

“…-Local broadcaster (also in, e.g. Gutiérrez et al 2010;Wawerla and Vaughan 2010;Ducatelle et al 2014): workers broadcast information about a worksite that they are currently working on to scouts that are nearby in order to recruit them. -Bee (also in, e.g.…”

Section: Robots and Robot Control Strategiesmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 85%

“…A similar setup has been previously used in, e.g. Balch and Arkin (1994), Pitonakova et al (2016b), Gutiérrez et al (2010).…”

Section: Simulation Environment and Swarm Missionsmentioning

confidence: 99%

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

2017

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“…The authors used evolutionary robotics to replicate the results obtained by Amé et al (2006) comparing the two works also using a macroscopic model. Gutiérrez et al (2010) developed a strategy for consensus achievement through direct communication in a swarm of robots performing foraging. The robots are able to decide between two foraging areas.…”

Section: Source Of Inspirationmentioning

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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“…Like pheromone-based methods, however, also this approach assumes that all robots of the swarm navigate back and forth between two targets. Also in [66], robots use direct communication to help each other navigate between a nest and a food source. Here, the robots exchange the estimated position of targets (nest or food source), and a robot searching a target can move directly towards the indicated location.…”

Section: Related Workmentioning

confidence: 99%

Cooperative navigation in robotic swarms

Ducatelle

Förster

et al. 2013

Swarm Intell

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Abstract-We present a cooperative navigation algorithm for robotic swarms. Its purpose is to let a robot find a given target robot, while being guided by the other robots of the swarm. The system is based on wireless communication: the robots forward messages containing navigation information over the ad hoc network among them, and the searching robot uses this information to find its target. We study the algorithm in two different scenarios. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. We show that the communication based navigation system allows the robots of the swarm to guide the searching robot without the need to adapt their own movements. In the second scenario, we study collective navigation: all robots of the swarm need to navigate back and forth between two targets. We show that in this case, the proposed navigation algorithm gives rise to synergies in robot navigation, and lets the swarm self-organize into a robust dynamic structure. This selforganization improves navigation efficiency, and lets the swarm find shortest paths in cluttered environments. We test our system both in simulation and on real robots.

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Collective decision-making based on social odometry

Cited by 50 publications

References 51 publications

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

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

Swarm robotics: a review from the swarm engineering perspective

Cooperative navigation in robotic swarms

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