首页|Learning Scalable Task Assignment with Imperative-Priori Conflict Resolution in Multi-UAV Adversarial Swarm Defense Problem

Learning Scalable Task Assignment with Imperative-Priori Conflict Resolution in Multi-UAV Adversarial Swarm Defense Problem

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The multi-UAV adversary swarm defense(MUASD)problem is to defend a static base against an adversary UAV swarm by a defensive UAV swarm.Decomposing the problem into task assignment and low-level interception strategies is a widely used approach.Learning-based approaches for task assignment are a promising direction.Existing studies on learning-based methods generally assume decentralized decision-making architecture,which is not beneficial for conflict resolution.In contrast,centralized decision-making architecture is beneficial for conflict resolution while it is often detrimental to scalability.To achieve scalability and conflict resolution simultaneously,inspired by a self-attention-based task assignment method for sensor target coverage problem,a scalable central-ized assignment method based on self-attention mechanism together with a defender-attacker pairwise observation preprocessing(DAP-SelfAtt)is proposed.Then,an imperative-priori conflict resolution(IPCR)mechanism is proposed to achieve conflict-free assignment.Further,the IPCR mechanism is parallelized to enable efficient training.To validate the algorithm,a variant of proximal policy optimiza-tion algorithm(PPO)is employed for training in scenarios of various scales.The experimental results show that the proposed algorithm not only achieves conflict-free task assignment but also maintains scalability,and significantly improve the success rate of defense.

Conflict resolutionreinforcement learningscalabilitytask assignment

ZHAO Zhixin、CHEN Jie、XIN Bin、LI Li、JIAO Keming、ZHENG Yifan

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School of Electronics and Information Engineering,Tongji University,Shanghai 201804,China

Shanghai Institute of Intelligent Science and Technology,Tongji University,Shanghai 201804,China

School of Automation,Beijing Institute of Technology,Beijing 100081,China

National Key Laboratory of Autonomous Intelligent Unmanned Systems,Beijing 100081,China

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National Natural Science Foundation of China Basic Science Research Center ProgramNational Natural Science Foundation of China Basic Science Research Center Program航空科学基金Shanghai Municipal Science and Technology Major ProjectChinese Academy of Engineering,Strategic Research and Consulting Program

7217117923671012023Z0660380012021SHZDZX01002023-XZ-65

2024

10.1007/s11424-024-4029-8

系统科学与复杂性学报(英文版)
中国科学院系统科学研究所

系统科学与复杂性学报(英文版)

EI
影响因子：0.181
ISSN：1009-6124
年,卷(期)：2024.37(1)
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