Evolving hierarchical gene regulatory networks for morphogenetic pattern formation of swarm robots

Morphogenesis, 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

Morphogen diffusion algorithms for tracking and herding using a swarm of kilobots

© 2016 Springer-Verlag Berlin Heidelberg This paper investigates self-organised collective formation control using swarm robots. In particular, we focus on collective tracking and herding using a large number of very simple robots. To this end, we choose kilobots as our swarm robot test bed due to its low cost and attractive operational scalability. Note, however, that kilobots have extremely limited locomotion, sensing and communication capabilities. To handle these limitations, a number of new control algorithms based on morphogen diffusion and network connectivity preservation have been suggested for collective object tracking and herding. Numerical simulations of large-scale swarm systems as well as preliminary physical experiments with a relatively small number of kilobots have been performed to verify the effectiveness of the proposed algorithms

Classification accuracy results for 10 initialisations for each combination of plasticity rule, connectivity method and time-series task.

<p>Classification accuracy results for 10 initialisations for each combination of plasticity rule, connectivity method and time-series task.</p

The two predominantly studied STDP learning windows.

<p>The two predominantly studied STDP learning windows.</p

Class separation results for 10 initialisations for each combination of plasticity rule, connectivity method and time-series task.

<p>Class separation results for 10 initialisations for each combination of plasticity rule, connectivity method and time-series task.</p

Pearson's Correlation between Metrics and Performance.

<p>Pearson's Correlation between Metrics and Performance.</p

Each of the metrics for all simulation results plotted against classification accuracy in both tasks.

<p>This indicates the extent that each metric can be used to predict performance.</p

Illustration of two types of connectivity model.

<p>A uniform connection policy produces variable length chains of connections with some groups of neurons disconnected from others. A scale-free connection policy leads to a structure of a few highly connected hubs and many sparsely connected leaves.</p

Lyapunov's exponent results plotted against kernel quality in both tasks to show the similarity between the metrics.

<p>Lyapunov's exponent results plotted against kernel quality in both tasks to show the similarity between the metrics.</p

Spectral radius results for 10 initialisations for each combination of plasticity rule, connectivity method and time-series task.

<p>Spectral radius results for 10 initialisations for each combination of plasticity rule, connectivity method and time-series task.</p