🤖 AI Summary
This work addresses the challenge of real-time robotic monitoring under uncertainty, where existing approaches often rely heavily on large datasets or explicit uncertainty modeling, hindering practical deployment. The authors propose a lightweight, real-time monitoring framework that evaluates task specification satisfaction through data-driven reachable set estimation, specifically tailored for maritime navigation scenarios with an efficient pipeline for reachable set generation and monitoring. By integrating formal specification monitoring with maritime rule-based reasoning, the method substantially reduces dependence on both data volume and prior knowledge of uncertainty distributions. Extensive simulations and hardware-in-the-loop experiments demonstrate that the approach achieves strong robustness under realistic disturbances and outperforms state-of-the-art methods in risk detection performance.
📝 Abstract
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.