Evidence map›Paper›PMID 41038870›Full record

ArticleScientific reports2025

Ontology-driven integration of advertised and operational capabilities in robots.

Muhammad Raza Naqvi, Arkopaul Sarkar, Farhad Ameri, Linda Elmhadhbi, Thierry Louge, Mohamed Hedi Karray

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Muhammad Raza NaqviLaboratoire Génie de Production (LGP), Université de Technologie Tarbes Occitanie Pyrénées (UTTOP), 47 Av. d'Azereix, 65000, Tarbes, France. razisyed4@gmail.com.
Arkopaul SarkarDepartment of Physics, Georgetown University, 37th St NW, Washington, DC, 20057, USA.
Farhad AmeriSchool Of Manufacturing Systems and Networks, Ira A. Fulton Schools of Engineering, Arizona State University, 6075 Innovation Way W, Tempe, AZ, 85283, USA.
Linda ElmhadhbiINSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570, 69621, Villeurbanne, France.
Thierry LougeLaboratoire Génie de Production (LGP), Université de Technologie Tarbes Occitanie Pyrénées (UTTOP), 47 Av. d'Azereix, 65000, Tarbes, France.
Mohamed Hedi KarrayLaboratoire Génie de Production (LGP), Université de Technologie Tarbes Occitanie Pyrénées (UTTOP), 47 Av. d'Azereix, 65000, Tarbes, France.

Funding

Agence Nationale de la Recherche ANR-21-CE10-0004-01
6 · The paper itself

Abstract

The adaptability of robotic systems is expanding the horizons of manufacturing flexibility. However, fully leveraging the potential of these systems poses considerable challenges. A key requirement is the ability to understand and model their diverse capabilities through a standardized and semantically well-defined framework. In this paper, we introduce the Robotic Capability Ontology (RCO), developed through a systematic investigation of various types of robotic capabilities, including those related to function, quality, and process performance. We define two types of capabilities: Advertised capabilities, as specified by manufacturers, and Operational capabilities, which reflect real-world performance. The RCO framework provides an ontology-based approach to representing these capabilities in a structured and interpretable manner. Within the manufacturing context, RCO serves as a reference ontology that bridges manufacturer specifications and empirical performance data to support more accurate, explainable, and interoperable representations of robotic capabilities.

Indexed as

CapabilitiesKnowledge technologiesManufacturingOntologyRobotics

Identifiers

PMID41038870
PMCPMC12491535

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Registered trials

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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.