发表机构
Virginia Polytechnic Institute and State University; NASA Langley Research Center（弗吉尼亚理工学院暨州立大学; NASA兰利研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出Plan-and-Avoid框架，针对多智能体空域环境，通过实时生成协作建议，在900余起强制着陆案例测试中，以5.7秒最坏响应时间实现优先级轨迹的低延迟安全间隔协调。
AI 中文摘要
本文提出了一种用于协调声明优先级轨迹周围的协作多智能体空域操作的实时Plan-and-Avoid（PAA）框架。优先级轨迹代表飞行器飞行计划，由于机动性受限、紧急情况、任务关键型任务或指定的操作优先级，必须保留该计划。该框架预测与周围交通的感知不确定性的安全间隔违规情况，当仅优先级计划无法维持间隔时，会生成受飞行器约束的单边建议，以修改附近飞行器的轨迹，从而维持所有交通的安全间隔。该方法适用于任何声明的优先级轨迹。本文使用应急着陆规划器演示了Plan组件，以生成候选优先级轨迹。PAA随后识别出太靠近该优先级轨迹的附近飞行器，并向这些飞行器发出Avoid规避建议。该框架使用来自华盛顿特区空域的真实世界广播式自动相关监视（ADS-B）流量进行测试，涉及900多起强制着陆案例，总计超过140小时的模拟飞行。PAA框架为全部575个独特冲突遭遇生成了可行的协作建议，在个人计算机上的最坏情况端到端响应时间为5.7秒，包括优先级轨迹规划、建议生成以及1秒双向数据链延迟。总体而言，93.5%的生成建议满足RTCA DO-365探测与避让的35秒时间阈值。这些结果证明了在保留优先级轨迹的同时，通过实时自动建议生成维持安全间隔的低延迟协调能力。未来工作将量化建议引发的延迟及其操作影响。
英文摘要
This paper presents a real-time Plan-and-Avoid (PAA framework for coordinating cooperative multi-agent airspace operations around a declared priority trajectory. The priority trajectory represents an aircraft flight plan that must be preserved because of constrained maneuverability, an emergency, a mission-critical task, or assigned operational priority. The framework predicts uncertainty-aware, well-clear separation violations with surrounding traffic and, when the priority plan alone cannot maintain separation, generates vehicle-constrained unilateral advisories that modify nearby aircraft trajectories to maintain well-clear separation for all traffic. The approach is applicable to any declared priority trajectory. This paper demonstrates the Plan component using a contingency landing planner to generate candidate priority trajectories. PAA then identifies nearby aircraft passing too close to this priority trajectory and issues Avoid resolution advisories to these aircraft. The framework is tested using real-world Automatic Dependent Surveillance-Broadcast (ADS-B) traffic from the Washington, D.C., airspace across more than 900 forced-landing cases, totaling over 140 hours of simulated flight. The PAA framework generates feasible cooperative advisories for all 575 unique conflict encounters, with a worst-case end-to-end response time of 5.7 s on a personal computer, including priority trajectory planning, advisory generation, and 1 s two-way datalink delay. In total, 93.5% of generated advisories satisfy the 35 s RTCA DO-365 Detect-and-Avoid temporal threshold. These results demonstrate low-latency coordination for preserving priority trajectories while maintaining well-clear separation through real-time automated advisory generation. Future work will quantify advisory-induced delays and their operational impacts.
