Research on UAV Autonomous Trajectory Planning Based on Prediction Information in Crowded Unknown Dynamic Environments

When unmanned aerial vehicles (UAVs) operate autonomously in ultra-low-altitude environments, they encounter complex dynamic obstacles in the form of dense crowds. The high uncertainty and complex interactions in crowd movement pose significant challenges to the safe flight of UAVs. To address these issues, this paper proposes an integrated UAV trajectory planning method that combines pedestrian trajectory prediction with gradient-based planning. First, a Contrastive Distribution Latent Code Generator (CDLCG) is designed in the pedestrian trajectory prediction model to infer future trajectory distributions from pedestrians' historical trajectories and generate predicted trajectories via a decoder. The accuracy and effectiveness of this method are validated using simulation methods based on four public datasets and validated through physical experiments on the OptiTrack Motion Capture System, respectively. Furthermore, an adaptive gradient-based UAV trajectory planning method is proposed by designing adaptive cost weights based on optimization stages and obstacle types. The method is validated in dynamic environments with varying crowd densities constructed in the Gazebo simulation environment, with results demonstrating that this method significantly improves the success rate of UAV trajectory planning in crowded dynamic environments; effectively balances trajectory smoothness, safety, and feasibility; and ensures safe UAV flight.