## 5 Conclusions and Prospect  
This study first establishes an environmental information model and an information fusion mechanism based on detection interval time, proposes a path grid determination algorithm to extract situation information along planned paths, and enables UAVs to use the PSO algorithm for rapid decision making with the goal of minimizing the path situation information cost and yaw cost \(Cost_{turn}\). Finally, the impacts of \(D_{wp,1}\) and \(Cost_{turn}\) on swarm search effectiveness are investigated through multiple simulation comparisons. The main contributions and conclusions of this study are as follows:  
1. A path grid determination algorithm is proposed to acquire situation information from grid cells along planned paths and calculate path costs, transforming the path-planning problem into a waypoint selection problem, which facilitates UAV decision making using the PSO algorithm.
2. The impact of \(D_{wp,1}\) on search effectiveness is investigated through simulations. An analysis of the simulation results reveals that increasing \(D_{wp,1}\) has no significant impact on search effectiveness but significantly reduces communication and decision-making consumption.
3. The impact of \(Cost_{turn}\) on search effectiveness is comparatively analyzed. Incorporating \(Cost_{turn}\) into the path-planning algorithm reduces the time UAVs spend circling near the previous waypoint, avoiding redundant searches, while also decreasing the time to reach the next waypoint, accelerating the OODA cycle, and improving search effectiveness.
4. Through simulation experiments, the impact of a GPS positioning error and UAV disconnection on collaborative search efficiency was analyzed. It was found that although the average capture time increased, the UAV swarm could still complete the task of searching for moving targets.  
The work presented in this study focuses on real-time task allocation for UAVs, addressing the problem of determining optimal flight directions based on current situation awareness. However, the collaborative search algorithm discussed in this article is only applicable to situations where there is no no-fly zone and the shape of the task area is a regular quadrilateral, and it does not consider the two limiting factors of UAV energy and  
Figure 18: The collaborative search efficiency of a UAV swarm under the influence of a GPS positioning error and UAV disconnection. (**a**) coverage rate; (**b**) capture rate; (**c**) capture time; (**d**) decision-making consumption; (**e**) ratio of average turning time.  
communication limitations. Therefore, further research will be conducted in the future to address these two deficiencies. After optimizing the collaborative search algorithm framework, the multi-UAV formation control law will be explored to achieve UAV cluster assembly, formation, turning, and obstacle avoidance in the task area and then collaborate to complete the search task.  
Conceptualization, W.M. and Y.L.; methodology, H.Z.; formal analysis, G.L.; writing--original draft preparation, H.Z.; writing--review and editing, W.M.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to regulation of laboratory data management.  
The authors declare no conflicts of interest regarding this paper.