A Multiple Fire Zones Detection Method for UAVs Based on Improved Ant Colony Algorithm

In response to the increasing severity and frequency of forest fires caused by dry climate and temperature rise, and the difficulty of identifying the fire initiation point in the early stage due to the large forest area, an effective method of utilizing unmanned aerial vehicles (UAVs) for early fire search and extinguishing is used in this article. This is a path planning problem in multi-agent multi-objective search in uncertain environments. This article proposes an improved ant colony algorithm based on the idea of competition cooperation. Based on the advantages of ant colony algorithm in path planning, exclusion attraction pheromones are added to the original algorithm, which enhances the detection area and fire extinguishing speed. On the NetLogo simulation platform, by comparing the convergence time and coverage area of the improved ant colony algorithm with the random walk algorithm and ant colony algorithm, it is found that the detection performance of the improved ant colony algorithm has significantly improved. The proposed UAVs based forest wildfire detection model based on improved ant colony algorithm is of great significance for the suppression of forest fires.