GNSS劣化下での学習型小型無人機分離ポリシーに対する実行時安全性フィルタリング
Runtime Safety Filtering for Learned Small UAS Separation Policies under GNSS Degradation
GNSSが劣化する環境下で、学習された小型無人機の分離ポリシーに対し、行動フィルタリングと観測フィルタリングの2つの安全機構を比較し、観測フィルタリングが衝突を90%削減することを示した論文。
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著者: Alex Zongo, Peng Wei
分類: cs.RO, cs.LG, cs.MA, cs.SY, eess.SY
原文アブストラクト
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.
