With the development of unmanned systems technology, drones have been widely applied in both civilian and military fields. Among these applications, multi-UAVs’ collaborative operations in urban scenarios serve as a crucial example of drone swarm applications, directly impacting the operational effectiveness. However, as the swarm densities increase,with the complex urban airspace environments, traditional trajectory planning methods based solely on spatial dimensions are no longer suitable for this environment due to their low planning density. Therefore, this paper first introduces the time dimension to extend the three-dimensional combat environment into a four-dimensional dynamic airspace . Moreover, with the goal of minimizing planning costs, the cost estimation function of the A* algorithm is reconstructed in the time dimension, and a dynamic path planning algorithm, 4D-A*, tailored for urban combat, is proposed. Simulation experiments demonstrate that 4D-A* not only effectively enhances the multi-UAVs’ collaborative combat capability in high-density operational scenarios by avoiding the inherent “detour” problem caused by the absence of the time dimension, but also reduces time cost consumption. Compared to basic methods, 4D-A* also offers higher reliability and robustness, avoiding the decoupling of spatial and temporal attributes in complex real-world scenarios, effectively preventing multi-UAV’ conflicts and reducing flight time, thereby meeting the demands of urban combat missions.