Adaptive Cooperative Gene Regulatory Network Optimized by Elastic Deformation Algorithm for Multirobot Hunting

Compared with a single robot, a multirobot system exhibits better performance when dealing with different complex tasks. Recently, the application of multirobot systems to hunt targets has become a research hotspot, but it also faces many challenges such as excessive system communication, poor collaboration performance, and limited practical testing. Based on previous research on gene regulatory networks (GRNs), this study innovatively proposes an adaptive cooperative GRN (AC-GRN) optimized by an elastic deformation algorithm for multirobot hunting. It uses the protein concentration diffusion mechanism to integrate various situational information of the task environment; during the generation and construction of the hunting pattern, it can adaptively measure the weight of information from different sources in the environment to complete the hunting task more efficiently, accurately, and robustly. Finally, we conduct four sets of simulation experiments to validate the feasibility, efficiency, and stability of the proposed AC-GRN model. Simultaneously, the Kilobot robots are used for practical testing, which further proves the advantages and practicality of the model.

Automated summary

Compared with a single robot, a multirobot system exhibits better performance when dealing with complex tasks. This study innovatively proposes an adaptive cooperative gene regulatory network (AC-GRN) optimized by an elastic deformation algorithm for multirobot hunting. The AC-GRN model integrates various situational information of the task environment using the protein concentration diffusion mechanism, and adaptively measures the weight of information from different sources to complete the hunting task more efficiently, accurately, and robustly. Simulation experiments and practical testing using Kilobot robots validate the feasibility, efficiency, and practicality of the proposed AC-GRN model.