A Voxel-Based Optimal Path Planning Method for UAV Navigation in Smart Cities

Smart mobility has emerged as a sustainable solution to the challenges of traffic congestion and environmental pollution in cities. Within this concept, Urban Air Mobility (UAM) offers a promising approach to three-dimensional (3D) urban transportation. However, existing UAV path planning studies have primarily focused on obstacle avoidance in low-altitude airspace for small UAVs, with limited consideration of continuous and dynamic risks such as meteorological conditions. As UAM operates at higher altitudes than small UAVs, it is essential to expand the range of flight risks considered in path planning to ensure safe navigation. This study proposes a voxel-based optimal path planning method that integrates multiple flight risks to support various types of UAVs, including those in UAM systems. The proposed method generates a voxel-based flight risk map and extends a two-dimensional (2D) wavefront algorithm into a 3D voxel-based algorithm for deriving optimal paths. Validation through two scenarios, designed in a virtual 3D urban model, demonstrated a 57.59% reduction in the total flight risk index and a 40.72% increase in path length compared with the collision-free path. These results indicate that the proposed method effectively enhances the safety and reliability of UAV navigation in complex urban environments.