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Immune-inspired search strategies for robot swarms

Published online by Cambridge University Press:  11 July 2016

G. M. Fricke*
Affiliation:
Department of Computer Science, The University of New Mexico, Albuquerque, USA. E-mails: [email protected], [email protected]
J. P. Hecker
Affiliation:
Department of Computer Science, The University of New Mexico, Albuquerque, USA. E-mails: [email protected], [email protected]
J. L. Cannon
Affiliation:
Department of Molecular Genetics and Microbiology, The University of New Mexico, Albuquerque, USA. E-mail: [email protected] Department of Pathology, The University of New Mexico, Albuquerque, USA
M. E. Moses
Affiliation:
Department of Computer Science, The University of New Mexico, Albuquerque, USA. E-mails: [email protected], [email protected] Department of Biology, The University of New Mexico, Albuquerque, USA Santa Fe Institute, Santa Fe, USA
*
*Corresponding author. E-mail: [email protected]

Summary

Detection of targets distributed randomly in space is a task common to both robotic and biological systems. Lévy search has previously been used to characterize T cell search in the immune system. We use a robot swarm to evaluate the effectiveness of a Lévy search strategy and map the relationship between search parameters and target configurations. We show that the fractal dimension of the Lévy search which optimizes search efficiency depends strongly on the distribution of targets but only weakly on the number of agents involved in search. Lévy search can therefore be tuned to the target configuration while also being scalable. Implementing search behaviors observed in T cells in a robot swarm provides an effective, adaptable, and scalable swarm robotic search strategy. Additionally, the adaptability and scalability of Lévy search may explain why Lévy-like movement has been observed in T cells in multiple immunological contexts.

Type
Articles
Copyright
Copyright © Cambridge University Press 2016 

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