考虑天气的变换器用于无人机即服务操作中的实时路线优化
Weather-Aware Transformer for Real-Time Route Optimization in Drone-as-a-Service Operations
AI总结：
本文提出一种天气感知的变换器框架，用于实时优化无人机即服务操作的路线，通过天气启发式方法提升动态环境下的路由决策效率与安全性。
摘要：
本文提出了一种新的框架，通过天气感知的深度学习模型来加速无人机即服务操作中的路线预测。虽然经典的路径规划算法，如A*和Dijkstra，能提供最优解，但它们的计算复杂性限制了在动态环境中实时应用的可能性。我们通过在经典算法模拟生成的合成数据集上训练机器学习和深度学习模型来解决这一限制。我们的方法结合了基于变换器和注意力的架构，利用天气启发式方法来预测最优的下一个节点选择，同时考虑影响无人机操作的气象条件。注意力机制动态加权环境因素，包括风向、风速和温度，以在恶劣天气条件下增强路由决策。实验结果表明，我们的天气感知模型在传统算法上实现了显著的计算加速，同时保持了路线优化性能，其中基于变换器的架构在动态环境约束下表现出更优越的适应性。所提出的框架为大规模DaaS操作实现了实时、天气响应的路线优化，代表着在自主无人机系统效率和安全性方面的重大进步。
英文摘要：
This paper presents a novel framework to accelerate route prediction in Drone-as-a-Service operations through weather-aware deep learning models. While classical path-planning algorithms, such as A* and Dijkstra, provide optimal solutions, their computational complexity limits real-time applicability in dynamic environments. We address this limitation by training machine learning and deep learning models on synthetic datasets generated from classical algorithm simulations. Our approach incorporates transformer-based and attention-based architectures that utilize weather heuristics to predict optimal next-node selections while accounting for meteorological conditions affecting drone operations. The attention mechanisms dynamically weight environmental factors including wind patterns, wind bearing, and temperature to enhance routing decisions under adverse weather conditions. Experimental results demonstrate that our weather-aware models achieve significant computational speedup over traditional algorithms while maintaining route optimization performance, with transformer-based architectures showing superior adaptation to dynamic environmental constraints. The proposed framework enables real-time, weather-responsive route optimization for large-scale DaaS operations, representing a substantial advancement in the efficiency and safety of autonomous drone 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。
