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Sensors (Basel). 2017 Mar 11;17(3). pii: E569. doi: 10.3390/s17030569.

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots.

Author information

1
Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM), Technical University of Madrid, Ctra. Valencia, Km 7, 28031 Madrid, Spain. xin.li@upm.es.
2
TECNALIA, Parque Tecnológico de Bizkaia, C/Geldo, Edificio 700, 48160 Bizkaia, Spain. sonia.bilbao@tecnalia.com.
3
Hi-iberia Ingeniería y Proyectos, C/Juan Hurtado de Mendoza 14, 28036 Madrid, Spain. tmartin@hi-iberia.es.
4
Instituto de Telecomunicações, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. jbastos@av.it.pt.
5
Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. jonathan@ua.pt.

Abstract

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

KEYWORDS:

MEBN; knowledge representation; ontology; uncertainty; underwater robots

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