SUNDER: Self-organized grouping and entrapping method for swarms in multitarget environments

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For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication, algorithms that rely on global-based information are infeasible. Typically, traditional gene regulatory networks (GRNs) that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots. This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms (SUNDER-GRN) to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information. A hierarchical self-organized grouping method (HSG) is proposed to structure subswarms in a distributed way. In addition, a modified distributed controller, with a relative coordinate system that is established to relieve the need for global information, is leveraged to facilitate subswarms entrapment toward different targets, thus improving the global multi-target entrapping performance. The results demonstrate the superiority of SUNDER-GRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.