Quality-dependent adaptation in a swarm of drones for environmental monitoring

Recently, individual or groups of drones have been used increasingly more frequently for applications in environmental monitoring. Groups of drones add larger robustness, lower vulnerability, higher accuracy and flexibility with respect to the use of single drones. These groups are called swarms when designed to make collective decisions trough local mutual interactions, as real social insects swarms. Natural environments are characterized by intrinsic dynamics that are hard to predict. Since a main issue faced by swarms of drones is the absence of adaptability to changes of the environment, in this paper we proposed a principled approach that can potentially be used to develop monitoring system based on drones swarm, able to adapt to changes of the environment thanks to the presence of stubborn individuals. Furthermore, we study how the level of consensus is affected by the interplay between the proportion of stubborn individuals and the difficulty of the problem, expressed by the ratio between the qualities of the different sites.