Dynamic Reallocation Model of Multiple Unmanned Aerial Vehicle Tasks in Emergent Adjustment Scenarios

Due to their strong risk tolerance, low manufacturing cost and good maneuverability, unmanned aerial vehicles (UAVs) have been widely used in various fields. Among related challenges, coordinated task assignment is a key scientific issue for autonomous control of UAVs. In this paper, based on the idea of fuzzy C-means (FCM) clustering and the ant colony optimization (ACO) algorithm, a cooperative multiple task reallocation problem with target precedence constraints for heterogeneous UAVs is proposed. The contributions of this research are the performance evaluation of the original algorithms in a dynamic context, consideration of changes in some attributes of the environment, and the extension of these algorithms to properly address more realistic dynamic emergent adjustment scenarios. According to the corresponding task reallocation strategy, the scenarios are divided into three categories: the complete redistribution strategy can effectively cope with scenarios where tasks have changed significantly, the partial adjustment strategy can induce partial responses to the changes of individual tasks, and group redistribution can effectively solve the problem of task target threat rating changes. The simulation results show that the dynamic reallocation model of multi-UAV tasks in dynamic emergent adjustment scenarios can achieve better performance to complete the corresponding tasks based on the proposed scheme. In addition, we deployed the developed graphical modeling and analysis software (GMAS) platform to implement the dynamic reallocation model of multi-UAV tasks in dynamic emergent scenarios, and the validity and reliability of the proposed task reallocation model were verified.