UAV 3D path planning based on improved grey wolf optimization algorithm

Abstract

In this paper, an improved grey wolf optimization algorithm is proposed for the research of UAV path planning in a complex 3D environment. Firstly, a new nonlinear convergence factor is proposed to balance the performance of global search and local development. Secondly, a cubic chaotic mapping is adopted to initialize the wolf population, diversifying the population while improving the uniformity of the population distribution. Finally, a mutation operation is introduced to mutate the individual gray wolf, which enhances the ability of the algorithm to jump out of the local optimum. Three-dimensional environment model is established by elevation data. The simulation results show that the optimal fitness of the improved algorithm is improved by 2.34% compared with that before the improvement, which proves the effectiveness of the algorithm in this paper.