无人机协同优化算法综述

doi:10.11896/jsjkx.250700048

Abstract

Abstract: As an important carrier of multimodal intelligent system,UAV has shown revolutionary potential for cross domain applications in the past decade.From precision agricultural monitoring,urban 3D modeling to military reconnaissance and target tracking,its technology evolution continues to expand the application boundary.However,single machine operation has inherent limitations in execution efficiency,system robustness and other aspects,which is difficult to meet the needs of diverse tasks.The UAV cluster based on the distributed cooperation mechanism shows significant advantages in terms of large-scale coverage,fault tolerance,through real-time information interaction and collective decision-making.For the core technical bottlenecks such as formation control and path planning,scholars at home and abroad have carried out extensive research for decades and proposed many classic algorithms.In the past decade,various optimization methods have emerged,covering collision avoidance,path planning,task allocation and formation reorganization,laying a solid foundation for the efficiency and practicality of the swarming cluster system.In order to clearly understand the research status of cooperative optimization algorithms for UAV clusters at home and abroad,the commonly used optimization algorithms are classified and summarized.According to the principle of each algorithm,the cooperative optimization algorithms are firstly divided into traditional heuristic algorithms and machine learning algorithms.Then it introduces the application and improvement of various algorithms by some scholars in four directions.Finally,the future deve-lopment direction of UAV collaborative optimization algorithm is prospected to provide reliable reference for beginners.

Key words: UAV cluster, Collision avoidance, Task allocation, Path planning, Formation reorganization

CLC Number:

TP393

WANG Yan, SHI Junling, LI Hanyu. Survey of UAV Cooperative Optimization Algorithms[J].Computer Science, 2026, 53(6A): 250700048-9.

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