基于机器学习与实验验证的复杂城市拓扑下风感知快速飞行规划
Wind-Informed Rapid Flight-Planning in Complex Urban Topologies via Machine Learning and Experimental Validation
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中文总结 AI 辅助
本研究提出一种新型风感知飞行规划框架，通过学习代理模型快速预测城市流场，经风洞实验验证可减少飞行器非期望位移、提升稳定性，为先进空中机动提供安全方案。
中文摘要 AI 辅助
先进空中机动作业有望改善和扩大人口密集区人员与货物的区域运输，但城市环境中风与建筑环境相互作用产生的危险飞行条件仍是飞行器面临的重大挑战。本研究提出一种面向多风城市环境中飞行器安全飞行规划的新型框架：训练基于学习的代理模型，从建筑几何、入射风等易获取信息快速预测流场；基于关键流参数及与建筑的距离，利用该代理预测计算体积飞行挑战标量场；再通过成本最小化路径规划器确定安全的流感知飞行轨迹。整套系统通过微型飞行器在大型风扇阵列风洞中放置的模型城市几何结构中的飞行测试得到实验验证。与无流场知识生成的轨迹相比，流感知方法可减少飞行器的非期望位移并提升飞行稳定性。本研究是城市环境中先进空中机动的安全风感知方法的首批实际演示之一。
英文摘要
Advanced air mobility operations hold the potential to enhance and expand regional transportation of both people and goods in populated areas. However, hazardous flight conditions arising from interactions between wind and the built environment remain a significant challenge for aerial vehicles in urban settings. This work proposes a novel framework towards safe flight planning of aerial vehicles in windy urban environments. A learning-based surrogate model is trained to rapidly predict flow fields from readily available information such as building geometry and incident wind. This surrogate prediction is used to calculate a volumetric flight challenge scalar field based on critical flow parameters and proximity to structures. A safe, flow-informed flight trajectory is then identified through a cost-minimizing pathfinder. The complete system is demonstrated experimentally through flight tests of a micro aerial vehicle through a model urban geometry placed in a large fan-array wind tunnel. Comparing this approach to trajectories generated without knowledge of the wind field, we find the flow-informed approach reduces undesired vehicle displacement and improves flight stability. This work is among the first practical demonstrations of safe, wind-aware methodologies for advanced air mobility in urban environments.
发表机构
- California Institute of Technology（加州理工学院）
- Technology Innovation Institute（技术创新研究院）
机构由 AI 辅助整理，请以论文原文为准。
