A Unmanned Aerial Vehicle (UAV) Path Planning Based on Golden Section Grey Wolf Optimization Algorithm

The existing gray wolf optimization algorithms (GWO) are known to exhibit slow convergence speeds and the tendency to converge on locally optimal solutions in path planning. In this paper, an improved golden section gray wolf optimization algorithm (GGWO) is proposed. The UAV path exploration and exploitation capabilities are balanced by the golden section coefficient through the addition of a new position update equation based on the GWO. The search diversity and adaptivity are enhanced by the sine function. Furthermore, this paper employs the use of triple B-spline curves to enhance the smoothness of the flight paths, thereby rendering them more suitable for UAV applications in complex environments. Simulation experiments are conducted with three traditional path planning algorithms in comparison with the algorithm of this paper, and the efficacy of the algorithm in successfully achieving feasible and effective path planning in complex environments is verified in three cases.