AI 中文总结
研究在GNSS退化时小型无人机分离策略的运行时安全过滤问题，对比动作过滤和观测过滤两种方法，发现动作过滤安全改进小，观测过滤能减少近90%的空中碰撞且更稳健，表明保留策略决策权限更优。
AI 中文摘要
基于学习的小型无人机系统（sUAS）分离保证在模拟中实现了接近零的碰撞率，但假定能从全球导航卫星系统（GNSS）获得准确的位置和速度信息。在城市环境中该假设不成立，因为多径传播、信号阻塞和故意干扰会降低导航完整性。这就引出了在GNSS退化情况下部署学习到的分离策略的一个基本架构问题：运行时安全机制应过滤策略的动作还是其观测值？本文评估了在对抗性GNSS退化情况下多智能体sUAS分离的这两种方法。两种架构首先估计与有界观测不确定性一致的最坏情况交通状态，然后产生分歧：动作过滤通过在最坏情况状态下评估的离散时间控制障碍函数来约束策略输出，而观测过滤将最坏情况状态作为校正后的输入直接呈现给策略。实验结果表明，动作过滤带来的安全改进可忽略不计，而观测过滤将近空中碰撞减少了90%，并且对障碍函数在分离距离和接近速度之间的权衡保持稳健。这些结果表明，对于具有学习到的安全行为的策略，保留策略的决策权限优于用手工设计的约束来覆盖其动作。
英文摘要
Learning-based separation assurance for small Unmanned Aircraft Systems (sUAS) achieves near-zero collision rates in simulation, but assumes accurate position and velocity information from Global Navigation Satellite Systems (GNSS). This assumption fails in urban environments, where multipath propagation, signal blockage, and intentional interference degrade navigation integrity. This raises a fundamental architectural question for deploying learned separation policies under GNSS degradation: should runtime safety mechanisms filter the policy's actions or its observations? This work evaluates both approaches for multi-agent sUAS separation under adversarial GNSS degradation. Both architectures first estimate a worst-case traffic state consistent with bounded observation uncertainty, then diverge: action filtering constrains policy outputs via discrete-time control barrier functions evaluated at the worst-case state, while observation filtering presents the worst-case state directly to the policy as corrected input. Experimental results show that action filtering provides negligible safety improvement, while observation filtering reduces near mid-air collisions by 90% and remains robust to the barrier function's tradeoff between separation distance and closing rate. These results suggest that, for policies with learned safety behaviors, preserving the policy's decision authority outperforms overriding its actions with hand-designed constraints.
CommentsAccepted for publication at the 2026 IEEE/AIAA Digital Avionics Systems Conference (DASC). 9 pages, 8 figures
