Mission analysis, dynamics and robust control of an indoor blimp in a CERN detector magnetic environment.

CERN particle detectors aerial inspection and mapping harsh environment indoor blimp magnetic disturbances robotic systems robust control techniques unmanned aerial vehicle

Journal

Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350

Informations de publication

Date de publication:
2023
Historique:
received: 10 06 2023
accepted: 02 10 2023
medline: 30 10 2023
pubmed: 30 10 2023
entrez: 30 10 2023
Statut: epublish

Résumé

At the European Organization for Nuclear Research (CERN), a Research and Development (R&D) program studies robotic systems for inspection and maintenance of the next-generation of particle detectors. The design and operation of these systems are affected by the detector's cavern harsh environment consisting of high magnetic fields and radiations. This work presents a feasibility study for aerial inspection and mapping around a CERN particle detector using a robotic Lighter-than-Air (LtA) Unmanned Aerial Vehicle (UAV), specifically a blimp. Firstly, mission scenarios and the detector environment are introduced; in this context a new empirical model is proposed for the estimation of magnetic disturbances resulting from the interaction of electromagnetic motors with the external magnetic field. Subsequently, the design of a reference blimp and the control system is presented, comparing different control techniques, namely, Computed Torque Control (CTC), Sliding Mode Control (SMC) and Nonsingular Terminal Sliding Mode Control (NTSMC). Finally, the results of trajectory tracking simulations are reported, considering both the uncertainties of the dynamic parameters and the estimated magnetic disturbances. This work demonstrates that the blimp successfully follows desired trajectory, navigating complex environments while maintaining stability and accuracy. Despite the challenges posed by high magnetic fields, indoor blimps can effectively offer safer and more efficient approaches to facility surveillance and maintenance, reducing radiation exposure for human personnel and minimizing detector downtime.

Identifiants

pubmed: 37901165
doi: 10.3389/frobt.2023.1238081
pii: 1238081
pmc: PMC10611505
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1238081

Informations de copyright

Copyright © 2023 Mazzei, Teofili, Curti and Gargiulo.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

Sci Robot. 2017 Jun 28;2(7):
pubmed: 33157901

Auteurs

Francesco Mazzei (F)

Automation Robotics and Control for Aerospace (ARCA) Laboratory, School of Aerospace Engineering, University of Rome La Sapienza, Rome, Italy.

Lorenzo Teofili (L)

Detector Mechanics, Experimental Physics (EP) Department, European Organization for Nuclear Research (CERN), Geneva, Switzerland.

Fabio Curti (F)

Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ, United States.

Corrado Gargiulo (C)

Detector Mechanics, Experimental Physics (EP) Department, European Organization for Nuclear Research (CERN), Geneva, Switzerland.

Classifications MeSH