## 2 Related Work  
There are many studies on dynamic path planning. The Voronoi map algorithm [8] is the most famous one. Although the Voronoi map algorithm can solve this kind of complicated problem and find the shortest planning path, the computational complexity increases with the complexity of the problem, and it does not handle sudden threats well. A potential field function was introduced by [PERSON] [9] to describe the geometrical structure of space and depict the motion planning problem accordingly. Although the artificial potential field method has a small amount of calculation and a fast planning speed, there exists a local minimum where the attraction equals the repulsion. Consequently, the path may become a trap area during planning and vibrate in a narrow channel easily. [PERSON]'s method [10] mends this bug using a tangent-plus-Lyapunov vector field in the dynamic path planning for UAV, conquering the problem of the local minimum; at the same time, this method does not take the planning influence into account, which was caused by the change incontroly strategy. In recent years, swarm intelligent optimization algorithms including particle swarm optimization [11], ant colony optimization [12], and artificial bee colony algorithm [13] have emerged for path planning. These algorithms generally adopt a parallel mechanism to deal with the calculations. Although they are more suitable for solving complex continuous optimization problems, they are not quite fit for asituation in which there are multiple objective constraints and their interaction/influence. In addition, these algorithms are prone to a local minimum, or the calculation complexity increases exponentially with the size of the problem, even to the extent of leading to a combination explosion. Artificial intelligent information theory methods for motion planning problems are specifically summarized in the literature [14]. Notwithstanding, the genetic algorithm [15], evolutionary algorithm [16; 17], and artificial neural network algorithm [18] can handle some specific path planning applications, yet necessary compensation for the whole planning system is seldom included in these artificial intelligent algorithms because the UAV's motion state is changed by the control strategy or environmental changes are notconsidered. In the last two or three years, many new autonomous decision-making as well as path planning methods have emerged. [PERSON] and [PERSON] [19] improved the particle filter(PF)-based terrain-aided navigation(TAN) method, which has been commonly used to obtain stable real-time navigation solutions in cases where the UAV operates at a high altitude. They designed a Rao-Blackwellized PF (RBPF)-based TAN, used long short-term memory (LSTM) networks to endure flat and repetitive terrains, and trained the noise covariances and measurement model of RBPF, and they confirmed that the proposed algorithm can enable more precise navigation performance than conventional RBPF-based TAN through simulations. With the development of deep learning theory, some machine learning methods have emerged one after another in UAV flight control and flight decision-making. [PERSON] [20] developed an off-policy reinforcement learning (RL) algorithm to solve optimal synchronization of multiagent systems, which is a model-free approach in that it solves the optimal synchronization problem without havingany knowledge of the agent dynamics, and it can synchronize all agents to the leader. [PERSON] [21] studied an optimal consensus tracking problem of heterogeneous linear multiagent systems, and found a Nashequilibrium solution by solving associated coupled Hamilton-Jacobi equations. The optimal cooperative control can be obtained by the input-output (I/O) Q-learningalgorithm, and the numerical example proved that the algorithm does not rely on the model of multiagent systems. These two methods could be used to handle the multi-UAVs collaborative planning and control problem. Recent research has also focused on decision-making, formation control, and 3D/4D path planning.