UAV Swarm Confrontation with Adaptive Attacking Strategy

With the rapid development of unmanned aerial vehicles (UAVs) technologies, a substantial increase on the employ of UAV swarms in a wide range of civilian and military tasks has been witnessed. Advanced confrontation control approach can greatly improve UAVs’ capabilities and effectively free pilots from dangerous, boring, and burdensome confrontation missions. How to efficiently control UAV swarms in the air-to-air confrontation is still a hard problem. In this paper, considering the influence of the defending angle of UAV, we propose a general attacking cost function and an adaptive attacking strategy (AAS) to improve the capability of UAV swarm against another UAV swarm in an airborne battlefield. A multi-agent based UAV swarm air-to-air confrontation model is established, where red UAV swarm versus blue UAV swarm were simulated in a visual 3D and discrete-event environment. Extensive simulations are performed to verify the performance of AAS, the results show that AAS outperforms other traditional strategies by a large margin. In particular, UAV swarm adopting AAS can obtain a very high winning percentage even though the size of the swarm is only half of its opposing swarm that uses random or low velocity strategy. Meanwhile, AAS is quite robust to cope with different UAV swarm sizes. To improve the usability and practicability of AAS, we also propose a lightweight strategy called empirical adaptive attacking strategy (EAAS). The simulation results indicate that EAAS is easy to use and can retain the similar effects to AAS especially for large scale UAV swarms. Our work will illuminate new insights into the area of UAV swarm versus UAV swarm.