Oh, H., and Jin, Y. 

Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

2014 IEEE Congress on Evolutionary Computation (CEC)

Morphogenesis, the biological developmental process of multicellular organisms, is a robust self-organising mechanism for pattern formation governed by gene regularoty networks (GRNs). Recent findings suggest that GRNs often show the use of frequently recurring patterns termed network motifs. Inspired by these bioolgical studies, this paper proposes a morphogenetic approach to pattern formation for swarm robots to entrap targents based on an evolving hierarchical gene regulatory network (EH-GRN). The proposed EH-GRN consists of two layers: the upper layer is for the 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.

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