オンラインでのロバスト安全フィルタ構築：方策制御バリア関数によるアプローチ
Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime
価値関数をオンライン推定することでロバストな制御バリア関数を近似し、高相対次数・入力制約系でも外乱下で安全を保証するRPCBFを提案。シミュレーションとクアッドコプター実機で有効性を実証。
著者: Luzia Knoedler, Oswin So, Ji Yin, Mitchell Black, Zachary Serlin, Panagiotis Tsiotras, Javier Alonso-Mora, Chuchu Fan
分類: math.OC, cs.RO
原文アブストラクト
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies as a valid CBF and demonstrate the effectiveness of the RPCBF-safety filter in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of our method in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. Website including code: www.oswinso.xyz/rpcbf/
