群知能UAVネットワークによる都市航空モビリティの最適化されたコンフリクト管理
Optimized Conflict Management for Urban Air Mobility Using Swarm UAV Networks
エッジAIと軽量ニューラルネットワークを用いた分散型群UAVアーキテクチャを提案し、都市航空モビリティにおけるコンフリクト検出・解消をリアルタイムかつ低遅延で行う。シミュレーションで従来の集中制御より最大3.8倍高速かつ高精度な解消を実現した。
著者: Rishit Agnihotri, Sandeep Kumar Sharma
分類: cs.RO
原文アブストラクト
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.