Pattern formation in constrained environments: A swarm robot target trapping method

Abstract

Inspired by the morphogenesis of biological organisms, gene regulatory network (GRN) based methods have been used in complex pattern formation of swarm robotic systems. In this paper, obstacle information was embedded into the GRN model to enhance the robots trap targets with a expected pattern while avoiding the obstacles in a distributed manner. Based on the modified GRN model, we adopt implicit function method to represent the expected pattern which is easily adjusted by adding extra feature points. With the existence of environmental constraints (e.g. tunnels or gaps in which robots have to adjust their pattern to conduct trapping task), we proposed a pattern adaptation strategy for the pattern modeler to adaptively adjust the expected pattern. The proposed model and strategies were verified through a set of simulation with complex environmental constraints.