Autonomous UAV swarms require scalable coordination mechanisms that maintain mission performance under limited communication, environmental uncertainty, and component failures. Centralized approaches provide global coordination but suffer from communication bottlenecks and single-node vulnerabilities, whereas fully decentralized methods often lack mission-level consistency. This paper presents Layered Autonomous Edge Intelligence (LAEI), a UAV-swarm framework that combines onboard learned policies with lightweight mission-level supervision. Each UAV performs local perception, obstacle avoidance, and action selection onboard, while the supervisory layer provides adaptive goal reassignment, fault-aware recovery, and context-dependent policy guidance without directly controlling low-level actions. LAEI further incorporates recovery strategies, including dynamic reassociation, backup supervisory support, and fallback local autonomy, to maintain mission continuity under representative failure scenarios. We evaluate LAEI in simulated UAV-swarm scenarios using mission completion time, collision rate, and coverage efficiency. The results show that LAEI reduces mission completion time and improves operational efficiency while maintaining collision-aware distributed UAV-level decision-making.

翻译：自主无人机蜂群需要可扩展的协调机制，以在有限通信、环境不确定性和组件故障下维持任务性能。集中式方法提供全局协调，但存在通信瓶颈和单节点脆弱性，而完全分散式方法往往缺乏任务级一致性。本文提出分层自主边缘智能（LAEI），一种结合机载学习策略与轻量级任务级监督的无人机蜂群框架。每架无人机在机载端执行局部感知、避障和动作选择，而监督层提供自适应目标重新分配、故障感知恢复和上下文相关策略指导，但不直接控制底层动作。LAEI进一步整合恢复策略，包括动态重新关联、备份监督支持和回退局部自主性，以在典型故障场景下维持任务连续性。我们在模拟无人机蜂群场景中评估LAEI，使用任务完成时间、碰撞率和覆盖效率作为指标。结果表明，LAEI在保持碰撞感知的分布式无人机级决策的同时，缩短了任务完成时间并提升了运行效率。