Gene Regulatory Networks with Asymmetric Information for Swarm Robot Pattern Formation

Gene Regulatory Networks GRNs play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by limited neighbors' information in the real environment, we propose a GRN-based algorithm with asymmetric information for swarm-robot pattern formation. Through this algorithm, the neighbors' information will be only used once, swarm robots can collect limited neighbors' information to self-organize autonomously to different predefine shapes. Furthermore, a discrete dynamic evolvement model of cellular automaton of pattern formation is provided to demonstrate the efficiency and convergence of the proposed method. Various cases have been conducted in the simulation, and the results illustrate the effectiveness of the method.