利用无人机自主能力实现最小4D飞行授权体积
Leveraging UAV Autonomy for Minimum 4D Flight Authorization Volumes
AI总结：
针对城市无人机交通问题，提出基于自主无人机的飞行授权框架，通过生成概率时空包络确定最小4D运行体积，仿真表明其空域预留更紧凑高效。
AI中文摘要：
城市地区无人机交通量的增长促使U-space（欧洲航空安全局EASA制定的用于安全高效无人机运行的框架）的建立。在此背景下，本研究提出一种利用自主无人机的飞行授权框架，借助无人机的运动模型与控制特性提升授权效率。该框架生成概率性时空包络，以预测期望置信水平内无人机的未来位置，再利用该信息确定形成任务有效授权请求的最小4D运行体积。通过仅预留必要空域，该方法提升了容量并支持无人机同时运行。将所提方法与传统基于规则的策略进行的仿真显示，在各类任务类型中，该方法的空域预留始终更紧凑高效。
英文摘要：
The increasing UAV traffic in urban areas has prompted the creation of U-space, an EASA framework for safe and efficient unmanned aerial vehicle (UAV) operations. Within this context, this work presents a flight authorization framework that leverages autonomous UAVs, using their motion models and control characteristics to improve authorization efficiency. In the proposed framework, probabilistic spatial-temporal envelopes are generated to predict future UAV locations within a desired confidence level, and this information is then used to determine the minimum 4D operational volumes that form a valid authorization request for the mission. By reserving only the necessary airspace, the approach enhances capacity and supports simultaneous UAV operations. Simulations comparing the proposed method with a conventional rule-based strategy demonstrate consistently more compact and efficient airspace reservations across a range of mission types.
