Task allocation and saturation attack approach for unmanned surface vehicles
Qiangqiang Chen, Baisheng Liu, Mingkai Yang, Haonan Guo, Changdong Yu · 2024
In modern naval warfare, it is crucial to achieve rapid and efficient attacks on enemy targets by unmanned surface vehicles (USVs). However, existing research on USV attack strategies is limited and often overlooks the requirement to allocate USVs effectively across regions of varying strategic value, which restricts performance in dynamic maritime combat environments. To this end, we proposed a novel task allocation and saturation attack strategy for USVs. First, we divided the areas according to the concentration of enemy USVs, and then reasonably allocated tasks according to the value of the enemy area and attack capability of our USVs. Then, our USVs sail towards the enemy area at a uniform angle and at the same time to form an encirclement and carry out precise saturation attacks. In task allocation, we introduce Logistic chaos mapping and differential evolution mechanism to improve the Grey Wolf Optimizer, thereby improving search efficiency and task allocation accuracy. In addition, we combine the optimal matching algorithm with the dynamic path control of Bezier curve to ensure the accuracy and flexibility of the coordinated saturation attack. The simulation experiment results show that the proposed approach exhibits high attack efficiency and practicality in different combat scenarios, and provides an effective solution for the attack missions of USVs in complex environments.