Monitoring autonomous persistent surveillance missions using invariance

TL;DR

This paper presents a method for runtime monitoring of autonomous surveillance missions using invariance, employing a compositional approach to handle large environments with uncertainty.

Contribution

It introduces a decentralized invariant-based monitoring framework for autonomous robots, enabling scalable and reliable persistent surveillance.

Findings

The compositional monitor is sound and complete under independence assumptions.

The approach is validated through a real robot case study in a labyrinth environment.

Decentralized invariant computation improves scalability for large environments.

Abstract

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.