On the representation and awareness of context for underwater robots in marine environments

Tracking #: 1508-2720

This paper is currently under review
Authors: 
Xin Li
Jose-Fernan Martinez
Gregorio Rubio

Responsible editor: 
Oscar Corcho

Submission type: 
Ontology Description
Abstract: 
Enhanced context awareness is a necessity for underwater vehicles to behave intelligently and achieve potential coordination and cooperation. In order to present a complete picture of the marine environment for underwater vehicles, this paper presents an ontology to abstract heterogeneous contexts obtained from the marine environment and model their associated uncertainty based on the Bayesian Network (BN) theory. The proposed ontology is a networked ontology consisting of several modules, including users, sensors, vehicles, environmental context, probability, and external sources etc. It could formally represent heterogeneous contexts, integrate data from different sources, facilitate information reusing, and enable vehicles with context awareness. In addition, it could support multiple reasoning, including ontology, rule and BN based inference. An oil spill monitoring scenario is presented to verify the proposed ontology in terms of its extensibility, applicability, interoperability, reusability and capability of supporting multiple reasoning mechanisms.
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