Path planning for Unmanned Aerial Vehicles (UAVs) in complex environments presents several challenges. Traditional algorithms often struggle with the complexity of high-dimensional search spaces, leading to inefficiencies. Additionally, the non-linear nature of cost functions can cause algorithms to become trapped in local optima. Furthermore, there is often a lack of adequate consideration for real-world constraints, for example, due to the necessity for obstacle avoidance or because of the restrictions of flight safety. To address the aforementioned issues, this paper proposes a dynamic weighted spherical particle swarm optimization (DW-SPSO) algorithm. The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation, as well as a lens-based opposition learning one to improve search flexibility and solution diversity. Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization (PSO) variants in terms of path safety, smoothness, and convergence speed. The performance superiority is statistically validated by the Wilcoxon signed-rank test. The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions, offering a robust solution for autonomous navigation systems.
Yao et al. (Thu,) studied this question.