A bio-inspired developmental approach to swarm robots self-organization

Morphogenesis is the biological process in which a fertilized cell divides, cells migrate and interact with each other, and finally resulting in the mature body plan under the control of gene regulatory networks (GRNs). Recently, biological discovered that 85% of the gene-gene regulation networks are composed of frequently recurring network patterns, which are called network motifs. Inspired by these biological studies, in this paper, we propose a developmental approach, i.e., network motifs based gene regulatory network model (NM-GRN), for self-organization of swarm robots to autonomously generate dynamic patterns to adapt to uncertain environments. First, a general GRN model is proposed with several predefined network motifs as basic building blocks, then an evolutionary algorithm is applied to evolve parameters and the structures of the NM-GRN model based on these basic building blocks. Experimental results demonstrate that the proposed bio-inspired model is effective for complex shape generation and robust to environmental changes in complex environments.