ROVON: An Ontology for Supporting Interoperability for Underwater Robots

Underwater robotics produces diverse and complex streams of sensor, image, video, and navigational data under challenging environmental conditions, creating obstacles for seamless integration and interpretation. This paper introduces ROVON (Remotely Operated Vehicle Ontology), a semantic framework designed to enhance interoperability and reasoning in underwater operations. While ROVON is conceptually scalable to large, heterogeneous datasets, its validation in this study focuses on controlled underwater inspection data collected for pipeline applications. ROVON enables the representation and analysis of multimodal underwater data by semantically annotating raw sensor feeds, enforcing data integrity, and leveraging knowledge graphs to convert disparate inputs into actionable insights. The ontology demonstrates how a structured semantic approach facilitates advanced analysis that improves decision-making, supports proactive maintenance strategies, and enhances operational safety. The proposed framework was validated through a controlled pipeline inspection scenario.