## 4 Cluster Path Planning Method That Resembles a Starling Flock
### Cluster Path Planning Method That Resembles a Starling Flock
Sample their position information at four time nodes (5 s, 10 s, 50 s, and 80 s, respectively) to better examine changes in the status of the cluster structure (Figures 22 and 23):  
We demonstrate the effectiveness of the cluster cooperative control method using the track-following UAS design suggested by [PERSON] and [PERSON]. In this design, the path manager configures the flight path between two attitudes and positions based on the trajectory command. The vector field guidance then generates speed, altitude, and heading commands for transmission to the fixed-wing UAV's autopilot. The UAV's sensors provide  
Figure 23: Sampling at 5 s, 10 s, 50 s, and 80 s positions. Figure 22: Cluster algorithm speed change graph. information to the state estimator, which generates the necessary state estimates. In this study, a fixed-wing UAV mathematical model based on the control framework of the appeal architecture and the UAV model is utilized in MATLAB to test the viability of the proposed method. The input frequency of the command operates the system in this study from 10 Hz to 50 Hz, as indicated in Figure 24, and the results are shown here in the form of 1 Hz for viewing purposes. The following trajectory of the model and the algorithm planning trajectory experience a significant error in the early stages due to the UAV position being randomly generated, as seen in Figures 25 from the overhead path. However, after a period of model parameter adaptation, it is then able to follow the algorithm planning trajectory more accurately. At altitude, the algorithm produced a larger following error in the early part of the algorithm and a smaller following error in the later section, as shown in Figure 15:0 However, since the goal of this paper is to design a cluster collaboration algorithm rather than concentrating on the UAV autopilot control system, the model and control system are sufficient for algorithm testing and can show that the algorithm has good robustness and can execute the UAV model in the procedure. Even in the face of significant variations in the UAV model's execution, the algorithm is strong enough to finish the task and maintain safe flying. Figure 15:1 (**a**) UAV model execution trajectory. (**b**) Comparison of trajectory command and execution trajectory of one of the drones. Figure 15:2 (**a**) Upward error in the north. (**b**) Upward error in the east. The algorithm can effectively maintain the cluster flight pattern of UAVs, keep the dispersed formation in the environment where pathfinding is necessary, solve the path more efficiently, tighten the cluster range in a safe environment, and form a tighter cluster pattern flight, according to comparisons between the heading and velocity change curves and the terrain map. A path planning scheme based on a fast search tree algorithm with dynamic prediction is added to improve the applicability of the algorithm to complex environments and the efficiency of the algorithm itself to maintain the flight pattern of the starling cluster in Europe. The starling cluster collaborative algorithm framework is applied to the UAV cluster, and the algorithm is targeted and optimized according to the characteristics of the UAV.