文献类型：期刊文献

英文题名：UAV Swarm Confrontation with Adaptive Attacking Strategy

作者：Hong, Chen[1,2,3];Yu, Nan[3];He, Ning[4];Chen, Aidong[1,2];Xiao, Mingming[4];Xue, Jian[5];Liu, Xiaofeng[6]

第一作者：宏晨;Hong, Chen

通讯作者：Chen, AD[1];Chen, AD[2]

机构：[1]Beijing Union Univ, Multiagent Syst Res Ctr, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]State Key Lab Air Traff Management Syst, Nanjing 210014, Peoples R China;[4]Beijing Union Univ, Coll Smart City, Beijing 100101, Peoples R China;[5]Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China;[6]Hohai Univ, Coll IoT Engn, Changzhou 213022, Peoples R China

第一机构：北京联合大学

通讯机构：[1]corresponding author), Beijing Union Univ, Multiagent Syst Res Ctr, Beijing 100101, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2024

外文期刊名：UNMANNED SYSTEMS

收录：EI(收录号：20244217226533);Scopus(收录号：2-s2.0-85206525346);WOS:【ESCI(收录号:WOS:001328679300001)】；

基金：The research described in this paper was supported by the National Key R&D Program of China (Grant No.2018AAA0100804), the State Key Laboratory of Air Traffic Management System (NO: SKLATM202201), the Academic Research Projects of Beijing Union University (ZK30202304 and ZK50201911).

语种：英文

外文关键词：UAV swarm; multi-agent; attacking strategy

摘要：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.