構造的抽象化と選択的洗練による形式検証
Structural Abstraction and Selective Refinement for Formal Verification
ロボット環境のボクセル表現に構造的抽象化を導入し、反例に基づく選択的洗練とモデル検査を組み合わせることで、複雑な環境での安全性検証を効率化する手法を提案した。
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著者: Christoph Luckeneder, Ralph Hoch, Hermann Kaindl
分類: cs.RO, cs.SE
原文アブストラクト
Safety verification of robot applications is extremely challenging due to the complexity of the environment that a robot typically operates in. Formal verification with model-checking provides guarantees but it may often take too long or even fail for complex models of the environment. A usual solution approach is abstraction, more precisely behavioral abstraction. Our new approach introduces structural abstraction instead, which we investigated in the context of voxel representation of the robot environment. This kind of abstraction leads to abstract voxels. We also propose a complete and automated verification workflow, which is based on an already existing methodology for robot applications, and inspired by the key ideas behind counterexample-guided abstraction refinement (CEGAR) - performing an initial abstraction and successively introducing refinements based on counterexamples, intertwined with model-checker runs. Hence, our approach uses selective refinement of structural abstractions to improve the runtime efficiency of model-checking. A fully-automated implementation of our approach showed its feasibility, since counterexamples have been found for a realistic scenario with a fairly high (maximal) resolution in a few minutes, while direct model-checker runs led to a crash after a couple of days.
