利用不变性监控自主持续监视任务
Monitoring autonomous persistent surveillance missions using invariance
AI总结：
本文研究了自主机器人在自主栈为黑盒时的运行时监控问题，通过将环境划分为有限区域并设计基于不变量的监控器，提出了一种分布式计算低维不变量集的方法，实现了对完整系统不变量的正确性和完备性。
摘要：
本文研究了自主机器人在自主栈为黑盒时的运行时监控问题。环境被划分为有限多个部分，每个部分携带一个不确定性状态，观察时该状态会减少，否则会增加。我们将闭环建模为一个状态依赖的混合系统，具有线性参数变化动力学，并设计了一个基于离线计算不变量的监控器。由于对于待调查空间较大的情况，通常难以获得此类不变量，我们提出了一种通过分布式计算每个不确定性区域的低维不变量集，并在线检查其联结的组合监控器。在常见的独立性假设下，该组合监控器相对于完整系统的不变量是正确且完备的。该方法在一项使用真实机器人持续监控迷宫的案例研究中得到了应用，强调了其在实际中的适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
