AI 中文总结
本文提出一种利用数据驱动可达集的实时监控框架，将其应用于海事导航，经仿真与硬件实验验证，该框架在不确定性下可实现稳健监控，风险检测性能优于现有方案。
AI 中文摘要
机器人系统必须在不确定性下运行，同时满足复杂的任务与安全规范。在不确定性下监控此类规范仍具挑战性，因为现有方案通常需要大量数据或明确的不确定性分布。本文提出一种实时监控框架，该框架通过利用数据驱动的可达集进行规范评估，降低了数据需求。我们将该框架实例化应用于海事导航领域，该领域的复杂规范源于交通规则。我们开发了一种数据高效的可达集构建流程，并推导了适用于实时部署的监控方案。仿真与硬件实验表明，该框架在现实干扰下能实现稳健的监控，与现有最先进指标相比，风险检测能力得到提升。
英文摘要
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.
