状態推定誤差下での障害物回避のための学習ベース計測ロバスト制御バリア関数
Learning-Based Measurement-Robust Control Barrier Functions for Obstacle Avoidance under State Estimation Error
状態推定誤差にロバストな新しい制御バリア関数（DMR-CBFとNMR-CBF）を提案し、障害物回避の安全性を向上させつつ保守性と計算コストを低減した。シミュレーションと実機（Unitree Go2）で有効性を実証した。
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著者: Nicholas Rober, Yixuan Jia, Jonathan P. How
分類: eess.SY, cs.RO
原文アブストラクト
Safety filters are an effective tool for enforcing constraints in safety-critical systems, but most existing methods assume perfect state information, which is rarely available in practice. Recent work has begun to close this gap by developing filtering mechanisms that are robust to state estimation error, but these methods can still exhibit safety violations or overly conservative behavior as estimation error grows. Focusing on obstacle avoidance, we develop two new control barrier function (CBF) formulations: drift-measurement-robust (DMR)-CBFs and neural measurement-robust (NMR)-CBFs. The DMR-CBF augments the standard CBF condition with an inner optimization over the worst-case uncertainty in the drift dynamics, improving robustness to estimation error. This DMR-CBF then supervises a pretraining phase for the NMR-CBF, which replaces the inner optimization with a learned term. The NMR-CBF is subsequently finetuned through differentiable trajectory rollouts, yielding a filter that achieves empirical safety comparable to the DMR-CBF while reducing both conservativeness and computational cost. We provide theoretical analysis of the DMR-CBF along with numerical results on a planar double integrator and a 12D quadrotor, where both proposed approaches prevent collisions while other robust methods either fail or are overly conservative. Finally, we deployed the NMR-CBF on a Unitree Go2, enabling successful navigation of an obstacle field under odometry errors that caused a standard CBF to collide.