PG-DWA: A Pheromone-Guided Global Planning and Local Obstacle Avoidance Method for UAVs in Complex Urban 3D Environments

Efficient obstacle avoidance in urban 3D environments is a key challenge for Unmanned Aerial Vehicles (UAVs) in urban security, logistics, and environmental monitoring. Traditional path planning algorithms, such as A*, struggle in dynamic environments due to static heuristics, while the Dynamic Window Approach (DWA) often falls into local optima, causing inefficiencies and instability. Herein, we introduce the Pheromone-Guided Dynamic Window Approach (PG-DWA), a hybrid method that integrates global path planning with local obstacle avoidance. It utilizes a time-varying 3D pheromone field as shared environmental memory, encoding high-quality paths and high-risk areas with positive and negative pheromones to guide global path planning. The pheromone concentration and gradient are integrated into the DWA trajectory function, along with geometric projection, to balance target approach and safety for local dynamic obstacle avoidance, while smoothing the path during stable flight. Simulation results confirm the efficiency in 3D obstacle avoidance, alleviating stagnation and angular velocity oscillations in concave obstacles and narrow passages, ensuring smooth flight paths, leading to a 34% reduction in average flight time and a 53% increase in flight speed (from 0.984 m/s to 1.51 m/s). This significant improvement demonstrates an efficient and stable solution for UAV navigation in complex urban environments.