Monitoring autonomous persistent surveillance missions using invariance

• URL: http://arxiv.org/abs/2605.06062v1
• Date: Thu, 07 May 2026 11:45:52 GMT
• Title: Monitoring autonomous persistent surveillance missions using invariance
• Abstract summary: This paper studies runtime monitoring for persistent surveillance by autonomous robots when the autonomy stack is a black box.<n>The environment is partitioned into finitely many parts, each carrying an uncertainty state that decreases when observed and increases otherwise.<n>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.
• Score: 0.42970700836450487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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