状態制約から制御バリア関数を設計するデータ駆動型安全制御手法
A Data-driven Method for Safety-critical Control: Designing Control Barrier Functions from State Constraints
ロボットなどの自律システムの安全性を保証するため、明示的な状態制約から制御バリア関数をデータ駆動で導出する手法を提案し、制御不変な安全集合を最大限近似する。
著者: Jaemin Lee, Jeeseop Kim, Aaron D. Ames
分類: cs.RO
原文アブストラクト
This paper addresses the challenge of integrating explicit hard constraints into the control barrier function (CBF) framework for ensuring safety in autonomous systems, including robots. We propose a novel data-driven method to derive CBFs from these hard constraints in practical scenarios. Our approach assumes that the forward invariant safe set is either a subset or equal to the constrained set. The process consists of two main steps. First, we randomly sample states within the constraint boundaries and identify inputs meeting the time derivative criteria of the hard constraint; this iterative process converges using the Jaccard index. Next, we formulate CBFs that enclose the safe set using the sampled boundaries. This enables the creation of a control-invariant safe set, approaching the maximum attainable level of control invariance. This approach, therefore, addresses the complexities posed by complex autonomous systems with constrained control input spaces, culminating in a control-invariant safe set that closely approximates the maximal control invariant set.
