Efficient Swarm Robotic Persistent Surveillance by use of Stigmergy

Abstract

Persistent surveillance is the act of covering an environment persistently, as fast as possible. By exploiting the intelligence of the swarm, it is possible to create a swarm robotic persistent surveillance method that can deal with unknown dynamic environments without the need for complex computations or excessive storage. Using stigmergy, which is communication via the environment, robots drop pheromones to signal that a specific location is covered. Other robots sense these pheromones and avoid going there. This is, in essence, the inverted stigmergic behaviour of ants. While ants deploy pheromones to attract other ants to that location, our robots repel other robots by deploying pheromones.
This thesis proposes a stigmergic swarm robotic persistent surveillance method that can deal with unknown environments and dynamic obstacles. Stigmergy is used as the sole communication mean. Additionally, an extension is included in the model that renders the model more efficient with respect to the pheromone usage. Subsequently, to demonstrate the potential for real-life application, the model is simulated in Webots, an open-source 3-D Robot simulator.
Concludingly, this thesis demonstrates that stigmergy lends itself perfectly for persistent surveillance. It minimizes the computations and memory storage needed, while ensuring performance. The proposed model outperforms current literature and deploys pheromones more efficient. The model is inherently robust and flexible.