用边缘智能集群优化城市低空飞行器冲突，实时响应更快更准。
Optimized Conflict Management for Urban Air Mobility Using Swarm UAV Networks
- 基于边缘AI的分布式集群架构，实现去中心化冲突检测与处理。
- 冲突解决速度比传统中心化模型快3.8倍，精度更高。
- 适合未来高密度城市空中交通管理，可扩展性强。
城市空中交通（UAM）面临前所未有的交通协调挑战，尤其在高密度城市通道中无人机数量增多时更为突出。本文提出一种数学模型，结合控制算法优化边缘AI驱动的去中心化蜂群架构，实现智能冲突化解，支持低延迟实时决策。系统采用轻量级神经网络，由边缘节点执行分布式冲突检测与解决。搭建仿真平台，在不同无人机密度下评估该方案。结果表明，冲突解决时间较传统集中式控制模型显著缩短，最快提升达3.8倍，且准确性更高。所提架构在未来的可扩展、高效、安全空中交通管理中极具应用前景。
原文摘要 · Abstract (English)
Urban Air Mobility (UAM) poses unprecedented traffic coordination challenges, especially with increasing UAV densities in dense urban corridors. This paper introduces a mathematical model using a control algorithm to optimize an Edge AI-driven decentralized swarm architecture for intelligent conflict resolution, enabling real-time decision-making with low latency. Using lightweight neural networks, the system leverages edge nodes to perform distributed conflict detection and resolution. A simulation platform was developed to evaluate the scheme under various UAV densities. Results indicate that the conflict resolution time is dramatically minimized up to 3.8 times faster, and accuracy is enhanced compared to traditional centralized control models. The proposed architecture is highly promising for scalable, efficient, and safe aerial traffic management in future UAM systems.
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