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Evolving network motifs based morphogenetic approach for self-organizing robotic swarms

In order to traverse through a complex environment, swarm robotic systems need to self-organize themselves to form different yet suitable shapes dynamically to adapt to unknown environments. Biological morphogenetic networks, such as gene regulatory networks (GRNs), are modular with independent units and often show the reuse of recurring patterns termed network motifs. Inspired by biological morphogenesis and evolution and structure of network motifs in biology, in this paper, we propose an evolving GRN-based approach for self-organizing robotic swarms to autonomously generate dynamic patterns in unknown environments. The basic idea of this GRN-based model is: first, several network motifs are predefined as the basic building blocks for GRNs, then an evolutionary algorithm is applied to evolve parameters and the structures of the GRNs model. Then a previously proposed bio-inspired pattern formation algorithm is applied to move robots to generated patterns automatically in a distributed manner. Simulation and experimental results demonstrate that the proposed bio-inspired model is effective for complex shape generation and formation and robust to environmental changes in complex unknown environments.