Low-altitude Trajectory Planning Method for Fixed-wing VTOL Aircraft Based on Adaptive Airspace Structure Topology
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摘要: 在当前空中交通管理体系中，基于均匀栅格的飞行器航迹规划方法广泛应用，但存在局限性。空域的均匀栅格划分无法自适应匹配空域的障碍物变尺度分布，导致特定空域航迹规划效率较低、计算代价较大，且在动态变化的空域环境中，航迹动态调整效率难以满足实际需求。针对以上问题，研究空域网格尺度自适应拓扑算法，根据障碍物分布特点实现空域的自适应Delaunay三角划分，并能够实现空域结构随着障碍物的动态调整快速高效局部重构；进一步基于空域结构自适应拓扑构建航迹搜索网络，采用A*算法在搜索网络上搜索初始航迹，针对初始航迹较长，折角尖锐的问题，设计了1种航迹实效化算法，通过局部检测优化航迹，使其转弯处圆弧过渡，不符合要求的圆弧，通过调整安全边界半径进行优化，以契合固定翼垂直起降飞行器（vertical take-off and landing，VTOL）飞行器运行特性。具体算例仿真结果表明空域网格尺度自适应拓扑算法能够使空域栅格数量减少69.33%，显著压缩搜索空间；航迹实效化算法能够使航迹长度缩短8%~15%，且航迹的光滑度显著提升，有效降低飞行控制难度与能耗。综上所述，本文的研究为固定翼VTOL飞行器的低空航迹规划提供了1种高效、实用的解决方案。
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关键词:
- 航空运输 /
- 低空短途运输 /
- 固定翼VTOL飞行器 /
- 空域结构自适应拓扑 /
- 航迹规划
Abstract: In current air traffic management systems, aircraft trajectory planning methods based on uniform gridsare widely adopted but exhibit inherent limitations. The uniform grid partitioning of airspace fails to adaptivelymatch variable-scale obstacle distributions, resulting in reduced planning efficiency, higher computational costs inspecific airspace sectors, and diminished responsiveness to dynamic trajectory adjustments. To address these challenges, an adaptive airspace mesh-scaling topology algorithm is studied in this paper. This approach applies adaptive Delaunay triangulation to match the spatial distribution of obstacles and enables rapid local reconstruction ofthe airspace structure in response to the updates from dynamic obstacles. Subsequently, a trajectory-searching network is constructed using this adaptive topology. The A* algorithm generates the initial trajectories on this network.To mitigate excessive length and sharp turns in initial paths, a trajectory refinement algorithm is de-signed. This optimization method replaces sharp turns with circular arcs through local detection. Non-conforming arcs are optimized by adjusting the safety boundary radius to comply with the operational characteristics of fixed-wing VTOL aircraft.Simulation results from the specific test examples show that the adaptive airspace mesh-scaling topology algorithmreduces the number of airspace grid cells by 69.33%, significantly compressing the search space. The trajectory refinement algorithm can reduce trajectory length by 8%-15% while markedly enhancing smoothness of the trajectory, there-by decreasing the complexity and energy consumption of flight control. In summary, this study provides an efficient and practical solution for low-altitude trajectory planning of fixed-wing VTOL aircraft. -
图 15 障碍物未变化前的空域及航迹
Figure 15. The airspace and flight path before the obstacle changed
图 16 障碍物出现后的空域及航迹调整
Figure 16. The adjustment of airspace and flight path after the appearance of obstacles
图 17 障碍物消失后的空域及航迹调整
Figure 17. The adjustment of airspace and flight path after the disappearance of obstacles
图 18 同一起止点下单条航迹规划
Figure 18. Single - track planning with the same starting and ending points.
图 19 15%障碍物密度不同障碍物分布航迹网络图
Figure 19. Trajectory network diagrams with 15% obstacle density and different obstacle distributions.
图 20 25%障碍物密度不同障碍物分布航迹网络图
Figure 20. Trajectory network diagrams with 25% obstacle density and different obstacle distributions.
表 1 空域处理前后的航迹长度对比
Table 1. Comparison of track lengths before and after airspace processing
OD对 空域未处理航迹长度 本文算法航迹长度 空域处理后航迹缩短比例（$\pm 0.005 \%$） O1D1 42.87 38.47 10.27 O2D2 33.14 29.93 9.70 O3D3 32.97 28.06 14.89 O4D4 37.80 33.33 11.81 O5D5 32.73 29.80 8.95 O6D6 30.14 26.87 10.85 O7D7 30.24 27.78 8.14 O8D8 30.73 26.86 12.59 O9D9 31.90 29.22 8.40 O10D10 37.46 34.50 7.90 
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