## 2 Related Work  
Path planning, a key element of autonomous drone navigation, is actually the selection of a shortest obstacle-avoidance path from the starting point to the target point within the flight area. Its essence is the problem of finding the optimal solution of the path [20]. The feasibility constraints and dynamic threats that occur in the operational airspace should be fully considered in the path-planning process of drones [21]. More precisely, the resultant path generated by the path planning of the drones should be the optimal path that satisfies the dynamic-threat avoidance condition. In previous research, various algorithms have been developed for dynamic-obstacle-avoidance design. In the paper [22], a method based on the combination of D\({}^{\star}\)Lite and a probabilistic roadmap (RPM) is proposed to implement the path planning of drones. The article constructs the initialized node roadmap by RPM using random sampling and then  
Figure 1: No-Fly Zone in Montreal from DJI. uses D*lite for local corrections. For grid-discretized maps, local modification based on initialized paths is a simple and effective method. Local modifications can avoid global path corrections due to obstacle changes [23]. However, if there are many dynamic obstacles in the workspace, frequent local modifications will lead to a significant decrease in the computational efficiency of the algorithm. In addition, the poor smoothness of the path can also have a negative effect on the efficiency of the drones and their energy consumption. Model predictive control (MPC) is an online-based optimal control strategy that is often used for trajectory tracking [24] and dynamic-obstacle avoidance [25] of drones and mobile robot. MPC relies on historical information and future inputs of objects in the finite time domain and predicts their future outcomes by means of predictive models of the objects [26]. MPC is different from the optimal-control method (OCM) [27]. The OCM tends to emphasize the optimality of the results, thus making it difficult to solve for nonlinear cases containing complex constraints. Instead, MPC adopts a compromise strategy that is more supportive of nonlinear systems, but sacrifices optimality to some extent. In the article [28], a nonlinear, nonconvex solver based on proximal averaged Newton for optimal control (PANOC) and fusing penalty functions is proposed to implement drone navigation and obstacle avoidance. However, MPC requires a high accuracy of the model [29], and the accuracy of the model has a very high impact on the performance of the MPC results. The modeling problem is too complex for hybrid systems with complex objects, such as unmanned aircraft systems. The artificial potential field (APF) method is a virtual-force approach proposed by [PERSON] [30]. A safe and smooth path is easily obtained by introducing an artificial potential field [31]. The basic idea of the artificial-potential-field method is to control the drone's movement by the combined force generated by the virtual gravitational field at the target location and the virtual repulsive field around the obstacle. The APF method is more intuitively defined than other methods, has a simple model structure, and does not require a large amount of computation to achieve real-time obstacle avoidance and complete the path-planning task [32]. On the other hand, the APF method has low objective reachability due to the drawback of local optimal solutions. To solve such a problem, the paper [33] used the random generation of virtual target points within a local field of very small points to break the equilibrium of gravitational and repulsive forces and bring the drones out of the local-optimum point. This approach ensures the safety and accessibility of the path-planning process to a large extent; however, it neglects the path quality issue. In recent years, jump point search methods (JPS) using heuristic search methods have been proposed, driven by the efficiency of path-planning algorithms [34].