A UAV Cluster's Boundary Performance Evaluation Probablity Method Based on Discrete Artificial Potential Field

The application of multiple UAVs cooperatively in complex missions has been widely explored in recent years. To achieve self-organization and self-synchronization, UAVs as an intelligent cluster system will emerge many social behaviours such as cooperation, competition, and mutual learning. At the same time, these social behaviours will profoundly affect the effectiveness and survivability of autonomous clusters, so studying the performance of UAV clusters has become a new problem for experimental identification. In this paper, this study first analyze the circular and linear formations of UAV clusters and derive the results of the indicator functions for the two basic shapes. Then, by establishing a 3-probability model, the relationship between the three probabilities and the area function and the search function in the discrete gridded area, while adding the confrontation background. The optimal probabilistic paths are derived by randomly setting up points, and after several simulations, the boundary performance of the UAV cluster is improved by nearly 10%.