IMPROVEMENT OF UNMANNED AERIAL VEHICLE CLUSTER ATMOSPHERIC MONITORING ALGORITHMS

摘要

In order to realise real-time and efficient monitoring of atmospheric environment by UAV (Unmanned Aerial Vehicle) cluster, a new route planning algorithm for UAV is proposed. Based on the idea of artificial potential field method, obstacle threat is combined with distance cost in the form of fitness function. Smooth potential functions with finite cutoff points are constructed by A and B and used to calculate obstacle threat values. Due to the fast convergence speed of PFSGA (Periodic Fast Search Genetic Algorithms), the air monitoring model of UAV cluster based on PFSGA is designed, and all kinds of costs are taken into account in the model. The simulation results show that the improved algorithm has better optimisation effect and can realise atmospheric monitoring quickly and dynamically. It also provides a new idea for environmental monitoring.