UAV trajectory planning method based on improved Grey Wolf algorithm and its improvement research

Aiming at the problems of slow convergence and local optimality of traditional Grey Wolf algorithm (GWO) in UAV track planning, an improved Grey Wolf algorithm is proposed. Firstly, the strategy based on Tent chaotic mapping was used to initialize the Grey Wolf population, which laid a foundation for enriching the population diversity in the global search process of the algorithm. Secondly, an improved strategy of a new nonlinear convergence factor is proposed to improve the global search capability. Again, an improved position update strategy with dynamic weights is proposed for the grey wolf position update, so that the grey wolf has an active search ability to avoid its loss of population diversity and falling into local optimality. Finally, static and dynamic simulation experiments are carried out in different space environments. The experimental results show that in the static environment, the improved Grey Wolf algorithm has better convergence speed and optimization accuracy, and has good feasibility and effectiveness in the problem of UAV path planning. In the dynamic environment, the improved Grey Wolf algorithm combined with DWA algorithm can effectively avoid dynamic obstacles and complete dynamic flight path planning.