Autonomous Uav Path Planning in Dynamic Environments: A Hybrid Framework of Trajectory Prediction and Priority-Aware DWA

Currently, path planning for unmanned aerial vehicles (UAVs) in dynamic environments still faces risks and challenges such as poor adaptability and high collision risks caused by frequent environmental changes. This paper proposes a hybrid planning framework that integrates trajectory prediction with the Priority-aware Dynamic Window Approach (P-DWA). The framework constructs a trajectory prediction model based on dynamic obstacle position data, integrating time weights and uncertainty quantification. During the path search process, a priority queue mechanism is implemented. This mechanism is combined with a risk-aware collision cost function to avoid local optima. Simulation results demonstrate that the proposed method outperforms EGOv2 and DP in dynamic obstacle scenarios, particularly in terms of planning success rate and obstacle avoidance. Real-world UAV flight tests further validate the method's effectiveness in complex dynamic environments, showcasing its robustness and reliability.