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.
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Suzuki, E., Hirai, H., Takano, S. (2009). Toward a Novel Design of Swarm Robots Based on the Dynamic Bayesian Network. In: Ras, Z.W., Dardzinska, A. (eds) Advances in Data Management. Studies in Computational Intelligence, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02190-9_14
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DOI: https://doi.org/10.1007/978-3-642-02190-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02189-3
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