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Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Formation Control Optimization for Odor Localization

Authors : Bambang Tutuko, Siti Nurmaini, Rendyansyah, P. P. Aditya, Saparudin

Published in: Recent Advances on Soft Computing and Data Mining

Publisher: Springer International Publishing

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Abstract

This paper presents a swarm robots formation control by using a new hybrid algorithm Fuzzy-Kohonen Networks and Particle Swarm Optimization (FKN-PSO). The FKN-PSO approach is proposed, to overcome the formation control problem due to the loss of a source of odor, caused by the failure in sensor detection, fail in robot motion control and environmental uncertainty in odor localization. The experiments are conducted by using simple swarm robots in the real environment with on-board sensor and processor. The results are compared between FKN-PSO and Fuzzy-PSO to look at the performance of the swarm robots in the process of odor localization. As the results found that the propose algorithm produce fast response and efficiently process than Fuzzy-PSO, they are able to locate the source of odor in a short time and capable for keeping in formation to find the target.

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Metadata
Title
Formation Control Optimization for Odor Localization
Authors
Bambang Tutuko
Siti Nurmaini
Rendyansyah
P. P. Aditya
Saparudin
Copyright Year
2017
Book
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_6

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