利用蜂群无人机网络优化城市空域机动性中的冲突管理
Optimized Conflict Management for Urban Air Mobility Using Swarm UAV Networks
AI总结：
本文提出了一种基于边缘AI的去中心化蜂群架构，用于优化城市空域机动性中的冲突管理，通过轻量级神经网络实现高效、低延迟的冲突检测与解决。
摘要：
城市空域机动性（UAM）带来了前所未有的交通协调挑战，尤其是在密集城市走廊中无人机密度增加的情况下。本文介绍了一种使用控制算法的数学模型，用于优化由边缘AI驱动的去中心化蜂群架构，以实现智能冲突解决，从而实现低延迟的实时决策。利用轻量级神经网络，系统利用边缘节点执行分布式冲突检测和解决。开发了一个模拟平台来评估该方案在不同无人机密度下的性能。结果表明，与传统集中式控制模型相比，冲突解决时间显著减少，快3.8倍，准确性也得到提升。所提出的架构在未来的UAM系统中具有高度的可扩展性、高效性和安全性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
