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.

Efficient autonomous exploration in unknown environments is a core challenge for Unmanned Aerial Vehicle (UAV) applications in unstructured settings. The primary challenges are exploration speed, coverage efficiency, and the autonomous, efficient, and obstacle-/threat-avoiding global guidance of UAV under local observational information. This paper proposes an autonomous exploration method driven by simultaneous incremental map prediction and the fusion of global frontier information to enhance the exploration efficiency of UAVs in unknown unstructured environments. Based on generative deep learning, we introduce an incremental map prediction method for 3D unstructured mountainous terrain, enabling the simultaneous acquisition of map predictions and their uncertainty estimates. Map prediction and trajectory planning are conducted concurrently: by utilizing the simultaneously predicted 3D map and its confidence (i.e., the uncertainty estimates), an overlap analysis is conducted between the flyable areas in the predicted map and the high-confidence regions. Dynamic guidance subspaces are generated by extracting global frontier points, within which shortest-time optimization is adopted for trajectory planning to maximize information gain and coverage per step. Experimental results demonstrate that compared to classical methods, our proposed approach achieves significant performance improvements in key metrics, including map coverage rate, total exploration time, and average path length.