Semantic knowledge to assess the capabilities of AUVs for planning

Semantic knowledge to assess the capabilities of AUVs for planning

Vredeveldt, Sietske (TU Delft Mechanical, Maritime and Materials Engineering)

Sijs, Joris (mentor)
De Schutter, B.H.K. (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2019-09-16

Autonomous Underwater Vehicles (AUVs) are unmanned vehicles that are often used for searching an area of the seabed for objects, such as naval mines. For autonomous planning of such search operations, it is useful to be able to infer what tasks an AUV can perform and how well it can do so. The objective for this final thesis project therefore was to develop and implement a semantic knowledge model in the form of an ontology for assessing the capabilities of AUVs that perform mine search operations. For developing an ontology, first the use case and existing ontologies for related topics were studied to obtain an overview of the concepts and relationships that are most relevant for AUVs that perform mine search operations. The ontology was then developed by formalising these concepts and relationships, and by identifying instances of these concepts that are specific to the use case. By implementing this ontology and developing a reasoning process for it, automatic inference of the capabilities of an AUV based on the components it is equipped with was realised. In addition, a data-driven methodology for performance assessment was developed and incorporated in the ontology to provide a basis for automatic inference of the expected performance of an AUV. This performance assessment methodology was tested by performing simulations with an AUV in a virtual underwater environment. These simulations showed that the developed methodology can successfully be used to predict performance under different conditions.

Ontology
AUV
Capability

http://resolver.tudelft.nl/uuid:a8c7d60f-7d7c-4fee-9ffc-6a1da0131fa6

Student theses

master thesis

© 2019 Sietske Vredeveldt