UAV (Unmanned Aerial Vehicle) swarm search has the advantages of flexible deployment, no casualties, and high cost-effectiveness. It has become a force that cannot be ignored in the battlefield. Aiming at the task planning problem in the UAV swarm search, this paper treats each UAV as a subsystem based on the self-organization idea, and proposes a search algorithm based on the IAPF (Improved Artificial Potential Field ). First, in order to improve search efficiency and reduce computational complexity, a new type of target attraction field function was constructed. Subsequently, in order to solve the problem of repeated search by the UAV in a short time interval, a search repulsion field generated by the UAV search path was proposed. Finally, a collaborative search process based on the direction standard deviation of the artificial potential field was designed. The simulation results show that compared with the scanning search and the HAPF-ACO (Hybrid Artificial Potential Field and Ant Colony Optimization) algorithm, this method can significantly improve the target discovery rate while achieving similar task area coverage. At the same time, the disturbance experiment proves that the method in this paper is robust in the case of some UAV failures.
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