## 1 Introduction
In addition, in computing, the coupling method is very difficult to solve [24]. Decoupling methods usually divide the problem of multi-UAV cooperative path planning into two steps, i.e., the coordination of the path planning sequence and the single UAV cooperative path planning [25]. As the most representative algorithm of the decoupling method, the prioritized planning algorithm (PP) [26] has become a very popular algorithm in cooperative path planning [27], along with other methods such as the revised prioritized planning algorithm (RPP) [28], the decentralized prioritized planning (DPP) [29], and the asynchronous decentralized prioritized planning algorithm (ADPP) [30]. In the prioritized planning algorithm, each UAV has its own priority and calculates its path according to the relative order of its priority. Therefore, the final path of each UAV is able to avoid static threats in the environment and UAVs with a higher priority than it. The common single UAV cooperative path planning algorithms in prioritized planning algorithms are the grid-based A* algorithm [31] and the sample-based RRT* algorithm [32; 33]. The calculation time of the A* algorithm usually increases exponentially with the increase in spatial dimension and grid resolution, and it does not perform well in large planning spaces of UAVs [34]. The RRT* algorithm is able to reach the optimal solution with fewer samples than the fixed high-resolution grid method by sampling the extended tree structure. Therefore, RRT* is widely used in path planning in high-dimensional space [35]. Furthermore, if the priority can be assigned reasonably, the prioritized planning algorithm can usually plan the cooperative path for multiple UAVs more efficiently. The existing decoupling methods also focus on conflict avoidance and do not conduct in-depth research on the time consistency of all UAV planning paths. In addition, in complex environments with unknown threats, the ability of the existing decoupling methods to avoid dynamic sudden threats is unknown. Therefore, combining the practical applications of the environment and the complex task categories of multiple UAV systems, as well as the possible improvement of existingpath planning algorithms for multiple UAVs, the main motivations of this paper are as follows. 1. The actual flight environment of UAVs is mostly wide open space. Due to the complexity of the actual flight environment, certain detected static threats can suddenly start to move and previously unknown static or moving threats can suddenly appear. Only relying on the known environment broadcast by the navigation system for cooperative path planning often carries the risk of unfeasible paths being followed. 2. The tasks performed by multi-UAV systems have gradually become more complex and diverse. Three-dimensional cooperative planning based on conflict-free space no longer meets the needs of most tasks. 3. Existing algorithms based on the local collision avoidance method, the coupling method, and the decoupling method are rarely studied in the field of 4D cooperative path planning [36; 37]. In addition, the local collision avoidance method is able to adapt to the frequently changing dynamic environment but cannot ensure the success of global cooperative path planning. The decoupling method can quickly plan cooperative paths for multiple UAVs but it cannot guarantee the efficiency of cooperative replanning brought about by environmental changes. Therefore, this paper focuses on the research of a 4D dynamic cooperative path planning algorithm for multiple UAVs. The algorithm needs to be consistent in terms of the conflict-free space and flight time of multiple UAVs simultaneously and must be able to adapt to frequent changes in the environment so that the algorithm can quickly avoid threats and suddenly encountered threats and ensure a lack of conflict between UAVs.