城市空中交通动态空域垂直起降飞行器路径优化

Path Optimization for Vertical Take-off and Landing Aircraft in Dynamic Urban Airspaces for Urban Air Mobility

针对目前城市空中交通动态空域航线优化方法难以保证最优性和计算效率上不足,以及对市区与市郊混合运行场景覆盖的缺陷,首先,提出一种适用于市郊和市区运行的城郊结合路网构建方法;其次,基于电动垂直起降飞行器(eVTOL)飞行动力学模型,提出准确的eVTOL功率消耗模型优化飞行路径;最后,基于涟漪扩散算法(RSA)提出适用于动态空域中的动态加权路网(RSA-DWRN)算法。通过构建包含时变气流和障碍区影响的城郊结合路网结构,以优化路径耗电量、飞行时间、计算时间和匹配度为指标,比较RSA-DWRN和传统动态路径优化算法DPO-A*在5种场景下600次实验优化效果。仿真结果表明:RSA-DWRN算法在4类指标下效果最好,且当动态空域环境因素越复杂时,RSA-DWRN表现越优;当空域中存在移动障碍物时,DPO-A*算法无法预测其运动轨迹且需频繁更新路网状态,大量提高了路径规划计算成本,而RSA-DWRN算法在相同情景下与动态环境变化过程协同进化,得到同时保证优化结果和计算效率的最优解。

To address the current challenges of achieving optimality and computational efficiency in dynamic airspace route optimization for urban air mobility,as well as the inadequacy in addressing mixed urban and suburban operational scenarios,an innovative approach to constructing a combined urban-suburban network is initially proposed to support both urban and suburban operations seamlessly.Based on the flight dynamics model of electric vertical take off and landing(eVTOL)aircraft,an accurate eVTOL power consumption model is developed to optimize flight paths.A Dynamically Weighted Routing Network(RSA-DWRN)algorithm for dynamic airspace is introduced by leveraging the Ripple Spreading Algorithm.With a combined urban-suburban network framework that incorporates time-varying airflow patterns and obstacle zones,the optimization performance of the RSA-DWRN's is compared against the traditional DPO-A*algorithm across five scenarios through 600 experiments,considering path power consumption,flight time,computation time,and matching degree as key metrics.Simulation results show that RSA-DWRN algorithm performs best under the four indexes,especially as the complexity of dynamic airspace environmental factors increases.In scenarios with moving obstacles,the DPO-A*algorithm fails to predict their trajectories and requires frequent updates to the network state,significantly increasing the computational cost of path planning.In contrast,the RSA-DWRN algorithm co-evolves with changes in the dynamic environment,finally obtaining optimal solutions that simultaneously ensure optimization results and computational efficiency.