A Decision-Making Method for Distributed Unmanned Aerial Vehicle Swarm considering Attack Constraints in the Cooperative Strike Phase

Abstract

In view of the growing military forces of various countries, unmanned aerial vehicle (UAV) swarms, as a new type of weapon, are gradually attracting the attention of more and more countries. Decision-making, as the core link in its application, has also become the focus of research in these countries. In this work, the distributed UAV swarm cooperative strike decision-making problems are separated from the distributed UAV swarm cooperative search strike decision-making problems, and the distributed UAV swarm cooperative strike multiobjective decision-making model is established, with the relevant constraints clarified. Besides, according to the analysis of the motion characteristics of UAV and the speed requirements of the distributed UAV swarm cooperative strike decision, an arc tangent trajectory planning method, conforming to the motion characteristics of UAV, is proposed. Moreover, a distributed cooperative strike decision-making method, based on the idea of “campaign endorsement and invitation cooperation,” is put forward, with the effectiveness and superiority validated by simulation experiments.

Probabilistic Chain-Enhanced Parallel Genetic Algorithm for UAV Reconnaissance Task Assignment

“…Cui [20] developed an improved chaotic self-adapting monkey algorithm, which enhances the efficiency of task distribution among multiple UAVs, dynamically assigning tasks based on real-time conditions, though it may face unpredictability in dynamic or complex environments. Wei et al [21] proposed a collaborative attack task assignment using a competition-cooperation mechanism designed to dynamically allocate offensive and defensive roles among UAVs, which may struggle to balance the short-term individual objectives against long-term collective swarm goals. Wang [22] introduced an adjustable fully adaptive cross-entropy algorithm, offering not only improved task coupling and precedence handling but also the ability to adjust algorithm parameters in response to environmental changes at the cost of higher computational demands.…”