<p>An improved particle swarm optimization (PSO) algorithm with a trigger-based hierarchical coupling mechanism is proposed for UAV path planning in complex environments. The proposed method enhances convergence performance and solution quality by integrating adaptive parameter control, Gaussian perturbation, and conditionally triggered structural recombination within a unified PSO framework. Specifically, adaptive inertia weight and learning factors are introduced to regulate convergence speed and improve stability, effectively reducing the risk of premature convergence. A Gaussian perturbation mechanism is further incorporated to enhance local random diffusion, enabling particles to escape shallow local optima. In addition, a probabilistically triggered crossover mechanism embedded within a breeding pool is designed to enable structural recombination of high-quality path segments, achieving multi-scale collaborative optimization. Simulation experiments conducted in multiple environments demonstrate that the proposed method achieves 13.75–43.72% lower path cost compared with A*, RRT*, and GA, while maintaining acceptable computational efficiency. The results verify that the proposed algorithm significantly improves global search capability, solution quality, and robustness, making it suitable for complex UAV path planning problems.</p>