# Motion Planning in UAV-Aided Data Collection with Dynamic Jamming
## 4 Simulations and Discussion  
In this section, we present the results of numerical simulations to compare the algorithm in this paper with the SCA algorithm foriinitialization proposed in [6] and the traditional DWA algorithm for dynamic environments. We mainly consider two dynamic sources of interference and several obstacles in the environment. To facilitate the simulation, we simulate a data collection task for an SN. Table 1 lists some crucial parametersand their values. We made some changes to ensure that the SCA algorithm for AStar initialization from [6] is applicable to the scenario presented. In this paper, we divide the path into two segments: the paths between the origin and the SN and between the SN and the end point. The two paths are initialized separately with the A star algorithm. We further interpolate the A star initial path points in order to obtain initial conditions that can meet the required data volume. For the DWA algorithm, we design different cost functions depending on the stage of the task, as shown in Appendix A. Figure 5 presents the motion plan for 200 kbit data collection in an environment without jamming and environments with static and dynamic jamming. In the jamming-free environment, the SN has the largest communication range, which covers the entire mission path of the UAV. In the static environment, the communication range of the SN is smaller and \"pear-shaped\"; the UAV can only collect data within this area. The communication range of the SN varies according to the motion of a jammer in a dynamic environment. The red dashed line in Figure 6 represents the global path that is initialized via the CFC, the blue dashed line denotes the global path that is initialized via the AStar algorithm, and the green dashed line denotes the local path that is initialized through the DWA. The light green region is the SN's effective communication range; in the absence of interference, it should be a circle in the 2D top view. The effective communication range of the SN becomes \"pear-shaped\" when suppressed by two sources of interference. The grey circle is the obstacle, and the 3D shape is a cylinder. Evidently, both the red dashed and blue dashed lines are at safe distances from the obstacle and pass through the SN. This is exactly what we want: a safe initial path to collect data. Figure 5: 200 kbit data collection with three algorithms for motion planning in environments without jamming and with static and dynamic jamming. Figure 6: Initial path. Figure 7 illustrates the residual amount of data collected by the UAV during the mission. In the jamming-free environment, the CFC and AStar algorithms complete the mission in approximately 25 s, while the DWA algorithm needs 30 s. In the static jamming environment, the CFC and AStar algorithms complete the entire mission in approximately 40 s, whereas the DWA algorithm needs 45 s. In the jamming interference environment, the CFC and AStar algorithms require similar times to complete the task, while the DWA algorithm needs more time. Figure 8 shows the normalized energy consumption of the UAV. We used the integral of the UAV acceleration to present the UAV energy consumption. According to the figure, the DWA algorithm consumes much more energy than the CFC and AStar algorithms for the three environments. The AStar algorithm consumes slightly less energy than the CFC algorithm in the jamming-free environment, whereas the CFC consumes slightly less energy than the AStar algorithm in the jamming environment. It is also worth noting that the complexity of the AStar algorithm is \(O(N^{2}\log N)\), while the complexity of the CFC algorithm is only \(O(KJ+2 KM+N)\). Therefore, collecting data with the CFC algorithm is much less expensive than with the AStar algorithm. Figure 8: Variation in flight energy consumption for three algorithms in different environments. Figure 7: The residual amount of data collected for three algorithms in different environments.