Equidistributed Search+Probability Based Tracking Strategy to Locate an Air Pollutant Source With Two UAVs

Abstract

As the atmospheric pollution becomes an increasingly serious problem, finding accurately the location of pollutant sources is still challenging. In the present work, a probability-based tracking strategy is proposed for guiding two cooperative unmanned aerial vehicles (UAVs) within a quest area to find an atmospheric pollutant source. This tracking strategy implies deploying algorithmically two phases: exploration and exploitation. During the exploration phase each vehicle follows a trajectory based on plane coordinates generated from a Hammersley sequence. The overlapping between UAVs’ trajectories is avoided by splitting guidance points into two groups by using the k-means algorithm. The navigation trajectories are smoothed by an TSP solver and a cubic spline planning algorithm. The exploitation phase redirects the search to specific locations where the probability of finding the source is higher. This is achieved by considering the quest area as a mesh, where each cell is assigned a probability computed with information collected by the UAVs measurement system. Every time a high concentration is found, the probabilities are recalculated, and flight trajectories are adjusted. The trajectories are semicircular, and the radius is decreased when a new high concentration is found. Simulation data of the proposed tracking strategy shows promising results on the accuracy achieved in the finding of the pollutant source, in comparison with three other tracking strategies: leader-follower, random walk with particle swarm optimization, and a hill climb traceability algorithm.