LBA-CBF: 並列ダイナミクス推論による高速適応型安全フィルタ
LBA-CBF: Rapidly Adaptive Safety Filters via Parallel Dynamics Inference
複数の候補ダイナミクスモデルを直近の予測誤差で順位付けし、最良モデルに近いもの全てに対して高次CBF条件を課すことで、急な環境変化にも素早く適応する安全フィルタを提案。ドローンや車両実験で有効性を示した。
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著者: Maitham F. AL-Sunni, Timeea-Andreea Radu, Hassan Almubarak, Henry Z. Liao, Michael Görner, Francesco Maurelli, John M. Dolan
分類: cs.RO, eess.SY
原文アブストラクト
Control barrier functions (CBFs) certify commands through an assumed dynamics model, so an abrupt, unmeasured regime change can undermine the certificate exactly when safety matters most. We present Look-Back Adaptive Control Barrier Functions (LBA-CBF), which rank a finite bank of candidate dynamics by recent prediction error over a short look-back window and enforce the high-order CBF condition against every model within a tolerance of the best, spanning best-fit adaptation to full-bank robust filtering. The dynamics may depend nonlinearly on the unknown parameters, and no switching model or continuously parameterized estimator is required. We prove that any feasible filtered input satisfies the true CBF condition whenever a safety-representative candidate is retained. In quadrotor simulation with abrupt wind reversals and an unknown payload, LBA-CBF is safe and reaches the goal from all random initial conditions, matching an oracle, while adaptive and robust baselines achieve 0-88% success. Banks of up to 250,000 models run inside the control loop, and Crazyflie 2.1 and F1TENTH experiments demonstrate adaptation to wind, payload release, and varying tire-road friction. Code, videos, and project details are available at: https://lla-control.github.io