Using Artificial Immune System to Prioritize Swarm Strategies for Environmental Monitoring

Swarms of Unmanned Aerial Vehicles (UAVs) are increasingly adopted to provide early situational awareness in environmental monitoring missions. Currently, a challenging problem is to manage swarms via responsive and adaptive coordination mechanisms. This study considers a cutting-edge swarm coordination algorithm called SFE, based on three strategies: stigmergy, flocking and evolution. Stigmergy is the release of digital pheromone by drones to generate a potential field that influences the steering in the spatial-temporal proximity. Flocking is a formation mechanism to spatially organize drones into local groups. Evolution is the parametrical adaptation of Stigmergy and Flocking to a specific type of mission. A novel algorithm called P-SFE is proposed, to overcome the limit of SFE related to the static priority of the three strategies. This prioritization is managed through an Artificial Immune System. A simulation testbed is developed and publicly released, based on commercially available technology and real-world scenarios. Experimental results show that the proposed P-SFE extends and sensibly outperforms the SFE.