Toward a novel design of swarm robots based on the dynamic Bayesian network

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Abstract

In this chapter, we describe a novel design method of swarm robots based on the dynamic Bayesian network. Recently, an increasing attention has been paid to swarm robots due to their scalability, flexibility, cost-performance, and robustness. Designing swarm robots so that they exhibit intended collective behaviors is considered as the most challenging issue and so far ad-hoc methods which heavily rely on extensive experiments are common. Such a method typically faces a huge amount of data and handles them possibly using machine learning methods such as clustering.We argue, however, that a more principled use of data with a probabilistic model is expected to lead to a reduced number of experiments in the design and propose the fundamental part of the approach. A simple but a real example using two swarm robots is described as an application.

Original languageEnglish
Title of host publicationAdvances in Data Management
EditorsZbigniew Ras, Agnieszka Dardzinska
Pages299-310
Number of pages12
DOIs
Publication statusPublished - Jul 31 2009

Publication series

NameStudies in Computational Intelligence
Volume223
ISSN (Print)1860-949X

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Suzuki, E., Hirai, H., & Takano, S. (2009). Toward a novel design of swarm robots based on the dynamic Bayesian network. In Z. Ras, & A. Dardzinska (Eds.), Advances in Data Management (pp. 299-310). (Studies in Computational Intelligence; Vol. 223). https://doi.org/10.1007/978-3-642-02190-9_14