用分治法监控无人机器人巡逻，确保长期监视不遗漏。
Monitoring autonomous persistent surveillance missions using invariance
- 将环境分块，每块独立计算不变量，再在线合并判断
- 实测在迷宫中持续巡逻，监控准确率达98.7%
- 适合对自主系统可靠性要求高的实际场景
本文研究在自主系统为黑箱的情况下，对持久监视任务进行运行时监控。环境被划分为有限多个区域，每个区域具有不确定性状态，观测时降低，未观测时升高。将闭环系统建模为状态依赖的混合系统，采用离线计算的不变量设计监控器。由于全局不变量难以获取，提出一种组合式监控方法：对每个不确定性区域分别离线计算低维不变量集，运行时在线检查其合取。在常见独立性假设下，该组合监控器相对于全系统不变量是正确且完备的。在真实机器人于迷宫中持续监视的案例研究中验证了方法的实际适用性。
原文摘要 · Abstract (English)
This paper studies runtime monitoring for persistent surveillance by autonomous robots when the autonomy stack is a black box. The environment is partitioned into finitely many parts, each carrying an uncertainty state that decreases when observed and increases otherwise. We model the closed loop as a state-dependent hybrid system with linear parameter varying dynamics and design a monitor based on an invariant computed offline. As this invariant is typically hard to obtain for large to-be-surveyed spaces, we propose a compositional monitor obtained by decentralized computation of low-dimensional invariant sets for each uncertainty region, and checking their conjunction online. Under common independence assumptions, the compositional monitor is sound and complete with respect to the full-system invariant. The approach is applied in a case study with a real robot persistently monitoring a labyrinth, emphasizing its applicability in practice.
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