In urban emergency response scenarios, the efficient deployment of Unmanned Aerial Vehicles (UAVs) is critical for timely and effective disaster management. In this paper, a 3D voxel-based collaborative path planning framework for UAVS is proposed, aimed at optimizing task completion time while ensuring obstacle avoidance and comprehensive area coverage. The study leverages voxel data for its simplicity and efficiency in handling large-scale urban environments, transforming traditional triangular mesh data into a voxel-based map for enhanced UAV navigation. The proposed methodology encompasses three key components: navigation map construction, local coverage path planning, and global coverage path planning. For local coverage, an improved Boustrophedon Cell Decomposition (BCD) algorithm is introduced, tailored for UAV operations, while global coverage is addressed through a multi-traveling salesman problem (MTSP) approach, optimized using the Jump Point Search (JPS) algorithm and Genetic Algorithm (GA). The experimental results verify the validity of this framework, compared to the conventional A* algorithm, the algorithm put forward in this study decreases the total route length by 23.80%, while also improving path smoothness. This study provides a robust foundation for multi-UAV collaborative operations in urban emergency response, offering improvements in efficiency and coverage.