Cooperative Search Self-Organizing Strategy for Multiple Unmanned Aerial Vehicles Based on Probability Map and Uncertainty Map

Multiple unmanned aerial vehicles (UAVs) cooperative search is a typical task in life. This paper focuses on self-organizing search strategy. First, the environment model of the mission area and the UAV model are established. Second, in order to make full use of the UAV's detection information to guide search, the updating rules of probability map and uncertainty map are established in this paper. Third, the distributed decision method based on receding horizon techniques is used to make decisions on the search direction of each UAV, and the improved genetic algorithm is used to optimize. Experiments show that the self-organizing search strategy is more efficient than formation search and random search, and the environment model adopted in this paper is more efficient compared with the single probability map model. Experiments show that the distributed decision method not only ensures the search effect, but also reduces the computation and communication of the fusion center, which makes UAVs search system more robust.