Self-organized cooperation between robotic swarms

Ducatelle, Frederick; Di, Gianni A.; Pinciroli, Carlo; Gambardella, Luca Maria

doi:10.1007/s11721-011-0053-0

Cited by 104 publications

(75 citation statements)

References 32 publications

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“…In the second, all the robots are moving between two points, creating a dynamic chain. Ducatelle et al (2011b) studied collective exploration using a heterogeneous robotic swarm (see Fig. 9(b)).…”

Section: Collective Exploration Descriptionmentioning

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: Collective Exploration Descriptionmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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“…Similar missions were explored e.g. in Krieger and Billeter (2000), Lemmens et al (2008), Ducatelle et al (2011).…”

Section: Simulation Environment and Swarm Missionsmentioning

confidence: 94%

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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“…For example, Garnier et al [14] used a downward-pointing LCD projector mounted above their robots arena to project light trails onto the floor. Other works that apply this similar approach were presented in [15,16,17].…”

Section: Related Workmentioning

confidence: 99%

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Palmieri

Yang

Rango

et al. 2017

Neural Comput & Applic

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Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two subproblems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete

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Self-organized cooperation between robotic swarms

Cited by 104 publications

References 32 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

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

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