A chemotactic pollution-homing UAV guidance system

Due to their deployment flexibility, Unmanned Aerial Vehicles have been found suitable for many application areas, one of them being air pollution monitoring. In fact, deploying a fleet of Unmanned Aerial Vehicles (UAVs) and using them to take environmental samples is an approach that has the potential to become one of the key enabling technologies to enforce pollution control in industrial or rural areas. In this paper, we propose to use an algorithm called Pollution-driven UAV Control (PdUC) that is based on a chemotaxis metaheuristic and a Particle Swarm Optimization (PSO) scheme that only uses local information. Our approach will be used by a monitoring Unmanned Aerial Vehicle to swiftly cover an area and map the distribution of its aerial pollution. We show that, when using PdUC, an implicit priority is applied in the construction of pollution maps, by focusing on areas where the pollutants' concentration is higher. In this way, accurate maps can be constructed in a faster manner when compared to other strategies. We compare PdUC against various standard mobility models through simulation, showing that our protocol achieves better performances, by finding the most polluted areas with more accuracy, within the time bounds defined by the UAV flight time.