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ISSN: 2090-4908

International Journal of Swarm Intelligence and Evolutionary Computation
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Research Article

Robust Swarm Robotics System Using CMA-NeuroES with Incremental Evolution

Kazuhiro Ohkura1*, Tian Yu1, Toshiyuki Yasuda1, Yoshiyuki Matsumura2 and Masanori Goka3

1Graduate School of Engineering, Hiroshima University, Japan

2Graduate School of Science and Technology, Shinshu University, Japan

3Graduate School of Engineering, Fukuyama University, Japan

Corresponding Author:
Kazuhiro Ohkura
Graduate School of Engineering, Hiroshima University
1-4-1, Kagamiyama, Higashi-Hiroshima, Japan
Tel: +81 824247550
E-mail: [email protected]

Received date: October 06, 2015; Accepted date: Octoberber 26, 2015; Published date: October 31, 2015

Citation: Ohkura K, Yu T, Yasuda T, Matsumura Y, Goka M (2015) Robust Swarm Robotics System Using CMA-NeuroES with Incremental Evolution. Int J Swarm Intel Evol Comput 4:125. doi:10.4172/2090-4908.1000125

Copyright: © 2015 Ohkura K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Swarm robotics (SR) is a novel approach to the coordination of large numbers of homogeneous robots; SR takes inspiration from social insects. Each individual robot in an SR system (SRS) is relatively simple and physically embodied. Researchers aim to design robust, scalable, and flexible collective behaviours through local interactions between robots and their environment. In this study, a simulated robot controller evolved by a recurrent artificial neural network with the covariance matrix adaptation evolution strategy, i.e., CMANeuroES is adopted for incremental artificial evolution. Cooperative food foraging is conducted by our proposed controller as one of the most complex simulation applications. Since a high level of robustness is expected in an SRS, several tests are conducted to verify that incremental artificial evolution with CMANeuroES generates the most robust robot controller among the ones tested in simulation experiments.

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