## 5 Simulation  
For drones performing near-ground search and rescue, the complexity of the environment, the high number of obstacles, and the presence of dynamic threats all pose significant challenges to the path planning of drones. Based on the above problems, the main work of this paper lies in three aspects, which are summarized as follows. 1. For the static obstacles in the drone's working environment, this paper extended the JPS algorithm in three dimensions to guide the drone to perform the mission in the 3D space. The algorithm comparison experiments showed that the 3D JPS algorithm had good expressiveness in terms of time complexity and space complexity. The 3D JPS algorithm reduced the computation time by 88.45% to 30.18% compared to the 3D A* algorithm and by 34.83% to 4.75% compared to the RRT algorithm. The 3D JPS algorithm met the demand for high efficiency in drone search-and-rescue missions. However, it was also found in the comparison experiments that the quality of the final generated paths of the 3D JPS algorithm did not reach the optimum. The 3D JPS algorithm increased the path length by 0.583% to 5.9% and the total path-turning angle by 27% to 98.2% compared to the Theta* algorithm. 2. In this paper, the parent node transfer law was used to optimize the generation path for the 3D JPS algorithm. The any-angle path planning of the 3D JPS algorithm was achieved by changing the parent-child node relationship chain among the path nodes to eliminate redundant turning points. Second, this paper introduced seventh-order polynomial-interpolation optimization based on minimum snap to further improve the smoothness and continuity of the path generated by the 3D JPS algorithm. As can be seen from experiment 2, the paths optimized based on the parent node transfer law were 4.8% to 8.1% shorter than the paths obtained by the 3D JPS algorithm, and the total turning angle of the paths was reduced by 51.6% to 93.2%, and the overall optimization effect of the paths is obvious. At the same time, the time complexity as well as the space complexity of the algorithm increased due to the parent node transfer law and polynomial-interpolation optimization, and the computing time increased by 2.5% to 5.4% and the memory usage increased by 0.0014% to 0.0018% compared to the 3D JPS algorithm. 3. Finally, this paper used an improved artificial-potential-field-based obstacle avoidance strategy to avoid the dynamic threats that may occur in drone low-level flight operations. Firstly, this paper improved the artificial-potential-field method to address the problem of falling into local optimum, by adding a virtual-target gravitational field and the three-dimensional [PERSON]'s line algorithm to induce the drone to achieve local-optimum escape. Secondly, the 3D JPS algorithm combined with an improved artificial-potential-field method was used to avoid obstacles for dynamic threats that come within the alert range. Experiment 3 showed that the dynamic-obstacle-avoidance strategy based on the improved artificial-potential-field method successfully accomplished the obstacle avoidance task in the cases of a single intruder, a reverse intruder with the same trajectory, and multiple intruders. However, the conversion of the 3D JPS algorithm to the artificial-potential-field method and the characteristics of the 3D Bresenham's line algorithm still led to the problem of unsmooth and excessive cornering in the final result path. The next work in this paper needs to further optimize the dynamic-obstacle-avoidance strategy of the 3D JPS algorithm and further reduce the computation time of the 3D JPS algorithm in order to improve the quality of the algorithm-generated path and the computation efficiency of the algorithm. Data curation, Q.Q. ; Investigation, Q.Q. and Y.L. ([PERSON]); Resources, Y.L. ([PERSON]) and Y.Z. ; Software, J.L. and Y.L. ([PERSON]); Supervision, Y.Z. ; Writing--original draft, J.L. ; Writing--review & editing, J.L. All authors have read and agreed to the published version of the manuscript.