## 2 Related Work
### Task-Planning Problem  
Task planning plays the role of the top command center in the whole autonomous decision-making and control system, which will determine the general direction of UAV task execution in the cluster. Therefore, the establishment of an effective task planning system is the key requirement to achieve high autonomy and intelligence of multiple UAVs. The task-planning problem of multiple UAVs consists of several subproblems, which usually include task assignment, path planning, etc. Task assignment is used to establish the association and mapping relationship between UAVs and tasks. It is a discrete space combinatorial optimization problem under multiple constraints. The key to its implementation is to establish mathematically expressed objective functions and effectively deal with various constraints. The role of path planning is to establish an executable path arrangement for each UAV to perform its assigned tasks. In essence, it is a path optimization problem in continuous space and is the underlying realization of the hierarchical strategy of task planning. It can be seen that task assignment and path planning are closely related and both are complex optimization problems. Therefore, how to design a task-planning algorithm to jointly optimize complex subproblems is the focus of current academic research. [PERSON] et al. proposed a mathematical method based on mixed-integer linear programming, which is flexible and can include more detailed constraints [27]. However, it can only be solved by professional mathematical software, because the solution is very complex. [PERSON] et al. proposed a new task-planning algorithm based on a genetic algorithm [33]. This algorithm can effectively plan tasks by combing constraints such as task priority, coordination, time constraints, and trajectory. [PERSON] et al. designed a chaos optimization method [34] based on the particle swarm optimization theory and applied it to the task assignment of multi-UAV which achieved good results. Recently, [PERSON] et al. have designed a new algorithm [35] to achieve task planning for UAV clusters under the condition that the multiple UAVs arrive at the destination in a specified order, while minimizing the task execution time. [PERSON] et al. designed a model of priority attack and target threat of UAV based on cell-winning suppression [36] and proved the efficiency of the algorithm through experiences. The algorithms described above can achieve efficient task planning under different constraints, but they cannot be applied to dynamic environments because they need to rely on global environment information.  
In the field of task planning, there are relatively few studies considering the dynamic environment. [PERSON] et al. designed a collaborative path planning for multi-UAV in a dynamic environment based on mixed-integer linear programming [37]. [PERSON] et al. [38] proposed the receding-horizon task assignment algorithm, which is a replanning algorithm. They applied it to the dynamic environment and achieved good results. When dealing with new environments, these algorithms recalculate to obtain new task-planning schemes, which will incur huge computing costs.