4D Path Planning via Spatiotemporal Voxels in Urban Airspaces

This paper presents an approach to four-dimensional (4D) path planning for unmanned aerial vehicles (UAVs) in complex urban environments. We introduce a spa-tiotemporal voxel-based representation that effectively models both spatial and temporal dimensions of urban airspaces. By integrating the 4D spatiotemporal ID framework with reinforcement learning techniques, our system generates efficient and safe flight paths while considering dynamic obstacles and environmental constraints. The proposed method combines offline pretraining and on-line fine-tuning of reinforcement learning models to achieve computational efficiency without compromising path quality. Experiments conducted using PLATEAU datasets in various urban scenarios demonstrate that our approach outperforms traditional path planning algorithms by 24% in safety metrics and 18% in efficiency metrics. Our framework advances the state-of-the-art in urban air mobility by providing a scalable solution for airspace management in increasingly congested urban environments.