<p>Efficient and reliable path planning in complex three-dimensional (3D) environments remains a critical challenge for unmanned aerial vehicles (UAVs). Although the dynamic window approach (DWA) has demonstrated effectiveness in local planning, it suffers from significant limitations: (1) most existing variants are restricted to two-dimensional (2D) scenarios, neglecting the solution space required to satisfy 3D constraints; and (2) traditional DWA relies on short-term optimization with a lack of foresight, making it prone to local optima and increased collision risks in dynamic environments. To address these critical issues, we propose a novel 3D dynamic window approach integrated with receding horizon optimization (3DDWA-RHO) for real-time UAV path planning. The proposed method extends the classical DWA into 3D space by incorporating kinematic and environmental constraints during velocity sampling and trajectory generation processes. Subsequently, a receding horizon optimization mechanism is introduced to generate predicted trajectory segments based on the terminal state of candidate trajectories. Finally, a composite objective function is constructed by considering target proximity, obstacle avoidance, and path smoothness, thereby enabling a comprehensive evaluation of candidate trajectories. Simulation experiments conducted in both static and dynamic 3D environments demonstrate that 3DDWA-RHO outperforms traditional DWA in terms of path optimality, convergence speed, and environmental adaptability. The results validate the effectiveness and robustness of the proposed method, highlighting its potential for real-time UAV path planning in complex 3D scenarios.</p>