Robotic airship for measurement and validation of a CERN detector stray magnetic field.

Authors

Francesco Mazzei, Paolo F Scaramuzzino, Luca Bernardi, Fabio Curti, Corrado Gargiulo

Published in

Frontiers in robotics and AI. Volume 13. Pages 1926428. Epub Oct 16, 2026.

Abstract

The detector caverns of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) are harsh environments for robotic operation: confined, without satellite navigation, close to fragile hardware, and permeated by intense stray magnetic fields that can disturb onboard electronics, actuators, and sensors. Characterising these fields is a prerequisite for deploying robots in the caverns, and is also relevant to detector operation, since anomalous fringe fields can affect the surrounding infrastructure and particle tracking. Yet conventional approaches based on fixed sensors, mechanical systems, or manual surveys provide only sparse spatial coverage. This paper presents a robotic airship that combines a three-axis Hall-effect sensor with ArUco marker-based visual localisation to measure the three-dimensional stray field in the ALICE (A Large Ion Collider Experiment) detector cavern. Two campaigns were conducted with the magnets at nominal current: static measurements at twenty known positions, isolating sensor accuracy from localisation uncertainty, and dynamic flights over the same instrumented region. The measurements were compared with an electromagnetic simulation through a dedicated uncertainty-propagation framework. The simulation reproduces the transverse field components well (R² = 0.86-0.98), whereas the vertical component shows a lower agreement (R² = 0.75-0.79) and a systematic positive bias of 0.65-1.12 mT, more consistent with simplifications in the electromagnetic model than with measurement uncertainty. These results demonstrate the feasibility of aerial robotic platforms for stray field characterisation in large experimental facilities and lay the groundwork for a fully autonomous survey system.

PMID:
42964332
Bibliographic data and abstract were imported from PubMed on 31 Oct 2026.

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