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ThinkMind // SEMAPRO 2021, The Fifteenth International Conference on Advances in Semantic Processing // View article semapro_2021_1_50_30024
A Reference Ontology for Collision Avoidance Systems and Accountability
 Authors:
David Martín-Lammerding
José Javier Astrain
Albert Córdoba Keywords: Semantic reasoning, ontology, UAS, knowledge, conflicts, anti-collision, sensor, embedded, air traffic. Abstract:
Unmanned Aerial Systems (UASs) are deployed in Intelligence, Surveillance, and Reconnaissance (ISR) applications with less cost and more flexibility rather than manned aircraft. An increasing number of UAS missions requires an improvement of their safety capabilities by equipping them with Collision Avoidance Systems (CASs). It is recognized that the use of small UAS at lower altitudes is now a driving force of economic development, but a safety risk when its number increases. UAS generates heterogeneous data from multiple sources, like the Flight Control Unit (FCU), the Global Navigation Satellite System (GNSS), a radio receiver, an onboard-camera, etc. Each CAS implementation receives this data and processes it to avoid collisions. There are many CAS implementations, but each one has a specific design and data repository structure. There is a lack of standards that simplify their development and homologation. This paper presents a reference knowledge model for any CAS for UAS implemented as a novel application ontology called Dronetology-cas. It transforms data to knowledge by combining heterogeneous telemetry and onboard-sensor data using linked- data and an ontology for semantic interoperability across het- erogeneous UAS traffic management systems. Dronetology-cas provides a unified semantic representation within an ontology- based triplet store designed to run in a low cost computer. Its semantic model provides advantages, such as interoperability between systems, machine-processable data and the ability to infer new knowledge. It is implemented using semantic web standards, which contribute to simplify an operational safety audit. Pages: 22 to 28 Copyright: Copyright (c) IARIA, 2021 Publication date: October 3, 2021 Published in: conference ISSN: 2308-4510 ISBN: 978-1-61208-888-4 Location: Barcelona, Spain Dates: from October 3, 2021 to October 7, 2021
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