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Conference Paper

Object Recognition in Swarm Systems: Preliminary Results

MPS-Authors
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Stegagno,  P
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Massidda,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://homepages.laas.fr/afranchi/events/icra2014mrs/w/Home.html
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ICRA-2014-Stegagno2.pdf
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Citation

Stegagno, P., Massidda, C., & Bülthoff, H. (2014). Object Recognition in Swarm Systems: Preliminary Results. In Workshop on the Centrality of Decentralization in Multi-Robot Systems: Holy Grail or False Idol? (IEEE ICRA 2014) (pp. 1-3).


Cite as: https://hdl.handle.net/11858/00-001M-0000-0027-8098-B
Abstract
Object recognition is a fundamental topic for the development of robotic systems able to interact with the environment. Most existing methods are based on vision systems and assume a broad point of view over the objects, which are observed in their entirety. This assumption is sometimes difficult to fulfill in practice, and in particular in swarm systems, constituted by a multitude of small robots with limited sensing and computational capabilities. We have developed a method for object recognition with a heterogeneous swarm of low-informative spatially-distributed sensors employing a distributed version of the naive Bayes classifier. Simulation results show the effectiveness of this approach highlighting some nice properties of the developed algorithm.