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A hierarchical OODA loop architecture can significantly improve the adaptability and efficiency of UAV swarms operating in dynamic, uncertain environments.
Multi-prototype-guided federated learning overcomes data heterogeneity in edge computing, boosting accuracy and reducing errors compared to single-prototype methods.
LLMs promise to revolutionize UAVs by enabling advanced environmental understanding, swarm coordination, and high-level task reasoning, paving the way for more adaptive and context-aware aerial operations.
