Storage Availability Prediction in Unmanned Aerial Vehicle Swarms Using Agent‐Based Simulation

Abstract

In practice, when an unmanned aerial vehicle (UAV) swarm is not executing a mission, its UAVs will be stored as inventory. To ensure that the UAV swarm can be quickly deployed when needed, it is necessary to assess and predict its storage state. Due to the flexible configuration of UAV swarms and the complex factors that affect them during storage, existing storage state indicators and prediction methods cannot meet the requirements of UAV swarm storage. In order to address these issues, a UAV swarm storage availability prediction method based on agent‐based simulation (ABS) is proposed. Considering the degradation of health status, maintenance, support, and other factors during the storage period of UAVs, a UAV swarm storage state measurement metric that covers the storage cycle is proposed. Based on this metric, a UAV swarm storage availability model is established. Then, considering the dynamic adaptability and internal complex interactions of UAV swarms, the ABS is used to realize the modeling and prediction of UAV swarm storage availability. Finally, a UAV swarm rescue case is used to illustrate its scientific validity and accuracy. Therefore, this study offers a scientific and efficient method for measuring the availability of UAV swarms, providing valuable insights for rapid response and decision‐making during the transition from storage to deployment. It also presents a viable approach for availability modeling and prediction in complex, emergent swarm systems.