Target Entrapment Based on Adaptive Transformation of Gene Regulatory Networks

The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task, which in turn impacts their efficiency in complex and dynamic settings. To address these challenges, this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks (AT-GRN). This innovative model enables swarm robots to dynamically adjust entrapment strategies by assessing current environmental conditions via real-time sensory data. Furthermore, an improved motion control model for swarm robots is designed to dynamically shape the formation generated by the AT-GRN. Through two sets of rigorous experimental environments, the proposed model significantly enhances the trapping performance of swarm robots in complex environments, demonstrating remarkable adaptability and stability.