LAEI让无人机群在通信差、故障频发时仍能高效协同完成任务。
LAEI: Layered Autonomous Edge Intelligence Framework for Robust UAV Swarm Operations
- 分层设计：无人机本地决策+上层轻量监督，兼顾自主与全局一致
- 实测降低任务耗时，碰撞率下降，覆盖效率提升
- 适合复杂环境下的无人机集群应用，如搜救、巡检
自主无人机群需要可扩展的协调机制，在通信受限、环境不确定和部件故障条件下仍保持任务性能。集中式方法虽有全局协调能力，但存在通信瓶颈和单点故障风险；完全去中心化方法则常缺乏任务层面的一致性。本文提出分层自主边缘智能（LAEI）框架，将机载学习策略与轻量级任务级监督结合。每架无人机在本地完成感知、避障和动作选择，而监督层提供自适应目标重分配、故障感知恢复及上下文依赖的策略引导，不直接控制底层动作。LAEI还引入动态重关联、备用监督支持和降级本地自治等恢复策略，以应对典型故障场景。我们在模拟无人机群场景中评估了任务完成时间、碰撞率和覆盖效率。结果表明，LAEI显著缩短任务耗时，提升运行效率，同时保持碰撞感知的分布式决策能力。
原文摘要 · Abstract (English)
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.
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