Research on the resilient planning model of multiple task chains in UAV combat networks

In the existing healing strategies, the consideration of the general homogeneous network model prevails, wherein conceptual nodes and edges are treated as the same type. The heterogeneity of combat entities within equipment systems is not considered in these studies, rendering the proposed algorithm inapplicable to the analysis of heterogeneous UAV combat networks. Furthermore, the current research on combat task allocation exhibits a relatively singular focus, primarily addressing tasks such as covering reconnaissance operation areas or segmenting tasks into independent categories like reconnaissance, decision-making, and strike, without delving into the coordinated operations of distinct tasks. This paper delves into the realm of task assignment within heterogeneous UAV combat networks through optimal trace planning. A novel trace planning model is devised based on the optimal subtask division, fragmenting all tasks into individual subtasks. In the event of a collapse in the UAV combat swarm following an attack, a robust task-adjusting algorithm is formulated to mitigate various faults. Two adjustment schemes are proposed: dynamic task adjustment and swarm healing in the face of communication link breakdowns. In instances where UAV swarm construction encounters failure, the task adjustment model should possess a degree of elasticity to facilitate the dragging of the failed swarm for restoration. The task adjustment is designed with significant elasticity to enhance network robustness and compatibility. Simulation results indicate that our algorithms and models exhibit high efficiency and incur minimal adjustment costs in adapting to the battlefield.