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arXiv · 2504.01006

A Parametric Model for Near-Optimal Online Synthesis with Robust Reach-Avoid Guarantees

Mario Gleirscher·Philip Hönnecke
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Abstract

Objective: To obtain explainable guarantees in the online synthesis of optimal controllers for high-integrity cyber-physical systems, we re-investigate the use of exhaustive search as an alternative to reinforcement learning. Approach: We model an application scenario as a hybrid game automaton, enabling the synthesis of robustly correct and near-optimal controllers online without prior training. For modal synthesis, we employ discretised games solved via scope-adaptive and step-pre-shielded discrete dynamic programming. Evaluation: In a simulation-based experiment, we apply our approach to an autonomous aerial vehicle scenario. Contribution: We propose a parametric system model and a parametric online synthesis.

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Mario Gleirscher, Philip Hönnecke. 2025-04-01. A Parametric Model for Near-Optimal Online Synthesis with Robust Reach-Avoid Guarantees. https://doi.org/10.1007/978-3-032-01377-4_14

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