用数据驱动可达集实现低数据需求的实时监控，提升海上导航风险检测能力。
Staying on Spec: Real-Time Monitoring under Uncertainty with a Maritime Case Study
- 基于数据驱动的可达集构建监控框架，减少对大量历史数据的依赖。
- 在真实干扰下仍能稳定运行，相比现有方法提升风险识别准确率。
- 适用于复杂规则下的实时系统监控，尤其适合海上航行场景。
机器人系统需在不确定性下满足复杂的任务与安全规范。现有监控方法通常需要大量数据或明确的不确定性分布，难以应用。本文提出一种实时监控框架，通过数据驱动的可达集降低数据需求，用于规范评估。以海上航行为例，复杂规范源于交通规则。我们设计了一种数据高效的可达集构建流程，并推导出适合实时部署的监控公式。仿真与硬件实验表明，在现实扰动下仍具鲁棒性，相比当前最优指标显著提升风险检测能力。
原文摘要 · Abstract (English)
Robotic systems must operate under uncertainty while satisfying complex task and safety specifications. Monitoring such specifications under uncertainty remains challenging, as existing formulations typically require extensive data or explicit uncertainty distributions. In this paper, we propose a real-time monitoring framework that reduces data requirements by leveraging data-driven reachable sets for specification evaluation. We instantiate the framework for maritime navigation, where complex specifications arise from traffic rules. We develop a data-efficient pipeline for constructing reachable sets and derive a monitoring formulation suitable for real-time deployment. Simulation and hardware experiments demonstrate robust monitoring under realistic disturbances, achieving improved risk detection compared to state-of-the-art metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。