This paper develops a real-time, search-based aircraft contingency landing planner that minimizes traffic disruptions while accounting for ground risk. The airspace model captures dense air traffic departure and arrival flows, helicopter corridors, and prohibited zones and is demonstrated with a Washington, D.C., area case study. Historical Automatic Dependent Surveillance-Broadcast (ADS-B) data are processed to estimate air traffic density. A low-latency computational geometry algorithm generates proximity-based heatmaps around high-risk corridors and restricted regions. Airspace risk is quantified as the cumulative exposure time of a landing trajectory within congested regions, while ground risk is assessed from overflown population density to jointly guide trajectory selection. A landing site selection module further mitigates disruption to nominal air traffic operations. Benchmarking against minimum-risk Dubins solutions demonstrates that the proposed planner achieves lower joint risk and reduced airspace disruption while maintaining real-time performance. Under airspace-risk-only conditions, the planner generates trajectories within an average of 2.9 seconds on a laptop computer. Future work will incorporate dynamic air traffic updates to enable spatiotemporal contingency landing planning that minimizes the need for real-time traffic rerouting.

翻译：本文开发了一种基于搜索的实时飞机应急着陆规划器，该规划器在考虑地面风险的同时最小化交通干扰。空域模型捕捉了密集的空中交通离港与进港流、直升机走廊及禁飞区，并以华盛顿特区区域案例研究进行演示。处理历史广播式自动相关监视（ADS-B）数据以估算空中交通密度。一种低延迟计算几何算法围绕高风险走廊和限制区域生成基于邻近度的热力图。空域风险量化为着陆轨迹在拥堵区域内的累积暴露时间，而地面风险则根据飞越区域的人口密度进行评估，二者共同指导轨迹选择。着陆点选择模块进一步减轻了对正常空中交通运行的干扰。与最小风险Dubins解法的基准测试表明，所提出的规划器在保持实时性能的同时，实现了更低的综合风险并减少了空域干扰。在仅考虑空域风险的条件下，该规划器在笔记本电脑上平均2.9秒内生成轨迹。未来工作将纳入动态空中交通更新，以实现时空应急着陆规划，从而最小化实时交通改道的需求。