CEC_2014_EH_GRN_YJ.pdf (2.17 MB)
Evolving hierarchical gene regulatory networks for morphogenetic pattern formation of swarm robots
conference contribution
posted on 2015-06-15, 11:08 authored by Hd Oh, Yaochu JinMorphogenesis, the biological developmental process of multicellular organisms, is a robust self-organising mechanism for pattern formation governed by gene regulatory networks (GRNs). Recent findings suggest that GRNs often show the use of frequently recurring patterns termed network motifs. Inspired by these biological studies, this paper proposes a morphogenetic approach to pattern formation for swarm robots to entrap targets based on an evolving hierarchical gene regulatory network (EH-GRN). The proposed EH-GRN consists of two layers: The upper layer is for adaptive pattern generation where the GRN model is evolved by basic network motifs, and the lower layer is responsible for driving robots to the target pattern generated by the upper layer. Obstacle information is introduced as one of environmental inputs along with that of targets in order to generate patterns adaptive to unknown environmental changes. Besides, splitting or merging of multiple patterns resulting from target movement is addressed by the inherent feature of the upper layer and the k-means clustering algorithm. Numerical simulations have been performed for scenarios containing static/moving targets and obstacles to validate the effectiveness and benefit of the proposed approach for complex shape generation in dynamic environments.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014Pages
776 - 783Citation
OH, H. and JIN, Y., 2014. Evolving hierarchical gene regulatory networks for morphogenetic pattern formation of swarm robots. IN: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, 6th-11th July 2014, Beijing, pp. 776 - 783.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Publication date
2014Notes
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ISBN
9781479914883Publisher version
Language
- en