UAV Flight Path Planning Based on Multi-Strategy Improved White Sharks Optimization
Ran Zhang, Xingda Li, Honghong Ren, Yuanming Ding, Yifei Meng, Qingyu Xia
Abstract
Due to the development of its own technology, the Unmanned Aerial Vehicle (UAV) play an increasingly important role in today’s social production practice. The complex and changeable environment requires the development of innovative UAV path planning algorithms. In order to meet the requirements of the increasingly complex UAV flight environment, a new UAV flight path planning algorithm based on a version of the White Sharks Optimization (WSO) is proposed in this research. Firstly, the terrain matrix is used to establish the three-dimensional terrain environment and constraint function, and then WSO is improved for handling the path planning. In the process of path planning, multi-trajectory search, nonlinear convergence factor and the model of fish movement behavior are adopted to enrich the population diversity, excavate the search space, speed up the convergence and reduce the likelihood of falling into local optima. Based on the simulation results, it can be observed that the proposed algorithm outperforms in terms of optimization accuracy, convergence speed, and robustness, leading to improved outcomes in UAV flight path planning.