Stigmergy-Based Swarm Robots for Target Search and Trapping

As a classical but difficult problem, multi-target searching and entrapping in a swarm of robots have received more and more attention in recent years. However, most existing approaches for addressing this problem rely on unrealistic assumptions such as reliable communication links, available global coordinate system, known environmental information, and central coordination among robots. Therefore, in this paper, a stigmergy mechanism-based framework was proposed for the use of searching and entrapping targets in a swarm of robots. Improving the inverse ant colony system, the framework was designed by adding a variety of pheromones to help group robots to collaborate and explore the environment and generate pheromone maps. Meanwhile, combining the Hierarchical Gene Regulatory Network (H-GRN) model with pheromone maps generated in the previous stage, the framework was arranged for robotic systems to search and entrap dynamic targets in unknown and communication-limited environments. Simulation results show that, comparing with traditional methods, the proposed framework can achieve better performance in target searching and trapping.