首页|Distributed dynamic task allocation for unmanned aerial vehicle swarm systems:A networked evolutionary game-theoretic approach

Distributed dynamic task allocation for unmanned aerial vehicle swarm systems:A networked evolutionary game-theoretic approach

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Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collabora-tive operations.With an continuous increase of UAVs'scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and real-time allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system's computational burden and enhance the individual's effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.

Task allocationUnmanned Aerial Vehicles(UAV)Game theoryLog-linear learningDistributed optimization algorithm

Zhe ZHANG、Ju JIANG、Haiyan XU、Wen-An ZHANG

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College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaPostgraduate Research and Practice Innovation Program of Jiangsu Province,China

7197111562173305KYCX22_0366

2024

10.1016/j.cja.2023.12.027

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(6)
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