TH-GRN Model Based Collective Tracking in Confined Environment

Abstract

Collective task in swarm robots has been studied widely because of the ability limitation of a single robot. Collective tracking is an important ability for swarm, and many of previous tracking tasks are based on leader-follower model. Unfortunately, simple following behavior brings much tracking uncertainty in constrained environment and difficulty for a convergence tracking pattern. To address this issue, we propose a new model for tracking by combining tracking-based hierarchical gene regulatory network with leader-follower model named (TH-GRN) for swarm robots. The TH-GRN model simulates the process that proteins are generated and diffused to control swarm activities. The concentration diffusion forms a tracking pattern and guides swarm robots to designated pattern. In order to be adaptive to confined environment, some flexible strategies are devised and integrated into our proposed TH-GRN model to achieve better performance. Besides, the TH-GRN model is also used to generate dynamic and complex environment. In our experiments, we design three obstacle scenarios, i.e., fixed obstacles, mobile (dynamic) obstacles, and hybrid obstacles. We conduct some simulation to validate the effectiveness of tracking-based TH-GRN model, and the experiment results demonstrate the superiority of our model.