Evidence map›Paper›PMID 41157349›Full record

ArticleSensors (Basel, Switzerland)2025

Multi-Domain Robot Swarm for Industrial Mapping and Asset Monitoring: Technical Challenges and Solutions.

Fethi Ouerdane, Ahmed Abubaker, Mubarak Badamasi Aremu, Mohammed Abdel-Nasser, Ahmed Eltayeb, Karim Asif Sattar, Abdulrahman Javaid, Ahmed Ibnouf, Sami El Ferik, Mustafa Alnasser

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Fethi OuerdaneControl & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Ahmed AbubakerControl & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID 0009-0005-6942-3698
Mubarak Badamasi AremuControl & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID 0009-0006-9246-8533
Mohammed Abdel-NasserControl & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID 0009-0003-0288-4348
Ahmed EltayebControl & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID 0000-0002-9151-4116
Karim Asif SattarInterdisciplinary Research Center for Smart Mobility & Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID 0000-0002-4170-5360
Abdulrahman JavaidResearch & Development, Yokogawa Saudi Arabia Company, Dhahran 31952, Saudi Arabia.ORCID 0000-0003-3905-9176
Ahmed IbnoufInterdisciplinary Research Center for Smart Mobility & Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Sami El FerikControl & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.ORCID 0000-0001-5648-4786
Mustafa AlnasserResearch & Development, Yokogawa Saudi Arabia Company, Dhahran 31952, Saudi Arabia.ORCID 0000-0002-1447-9863

Funding

King Fahd University of Petroleum and Minerals COE02538
6 · The paper itself

Abstract

Industrial environments are complex, making the monitoring of gauge meters challenging. This is especially true in confined spaces, underground, or at high altitudes. These difficulties underscore the need for intelligent solutions in the inspection and monitoring of plant assets, such as gauge meters. In this study, we plan to integrate unmanned ground vehicles and unmanned aerial vehicles to address the challenge, but the integration of these heterogeneous systems introduces additional complexities in terms of coordination, interoperability, and communication. Our goal is to develop a multi-domain robotic swarm system for industrial mapping and asset monitoring. We created an experimental setup to simulate industrial inspection tasks, involving the integration of a TurtleBot 2 and a QDrone 2. The TurtleBot 2 utilizes simultaneous localization and mapping (SLAM) technology, along with a LiDAR sensor, for mapping and navigation purposes. The QDrone 2 captures high-resolution images of meter gauges. We evaluated the system's performance in both simulation and real-world environments. The system achieved accurate mapping, high localization, and landing precision, with 84% accuracy in detecting meter gauges. It also reached 87.5% accuracy in reading gauge indicators using the paddle OCR algorithm. The system navigated complex environments effectively, showcasing the potential for real-time collaboration between ground and aerial robotic platforms.

Indexed as

AprilTagsdigital meter readinghardware-in-the-loop (HIL) testingreal-time communicationROSSimulinkSLAMUAV–UGV collaborationvision-based landingYOLOv5

Identifiers

PMID41157349
PMCPMC12567462

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.