An RHC-QPSO path planning algorithm is proposed for the dynamic path planning of UAVs in complex environments based on the quantum particle swarm optimization algorithm.The algorithm uses quadtree to establish the real-time environment model.The UAV path optimization is made based on QPSO algorithm, and Kalman filter is used to estimate the trajectory of dynamic threats in space.Combined with the RHC method, an active avoidance strategy is adopted against the dynamic threats.The minimized Φ, ψ, θ and a are selected as process performance indicators, and used as the optimization indicators for each rolling optimization window.The simulation experiments show that, the algorithm can not only effectively realize the dynamic path planning of UAVs with certain prior knowledge of the map in real time, but also prevent the UAV from making large maneuvering to avoid dynamic threats during the planning process, and the smoothness of the path is improved to some extent, which can improve the safety of the UAV.