An Ontology-driven Dynamic Knowledge Base for Uninhabited Ground Vehicles
Abstract
In this paper, the concept of Dynamic Contextual Mission Data (DCMD) is introduced to develop an ontology-driven dynamic knowledge base for Uninhabited Ground Vehicles (UGVs) at the tactical edge. The dynamic knowledge base with DCMD is added to the UGVs to: support enhanced situation awareness; improve autonomous decision making; and facilitate agility within complex and dynamic environments. As UGVs are heavily reliant on the a priori information added pre-mission, unexpected occurrences during a mission can cause identification ambiguities and require increased levels of user input. Updating this a priori information with contextual information can help UGVs realise their full potential. To address this, the dynamic knowledge base was designed using an ontology-driven representation, supported by near real-time information acquisition and analysis, to provide in-mission on-platform DCMD updates. This was implemented on a team of four UGVs that executed a laboratory based surveillance mission. The results showed that the ontology-driven dynamic representation of the UGV operational environment was machine actionable, producing contextual information to support a successful and timely mission, and contributed directly to the situation awareness.
Cite
@article{arxiv.2602.10555,
title = {An Ontology-driven Dynamic Knowledge Base for Uninhabited Ground Vehicles},
author = {Hsan Sandar Win and Andrew Walters and Cheng-Chew Lim and Daniel Webber and Seth Leslie and Tan Doan},
journal= {arXiv preprint arXiv:2602.10555},
year = {2026}
}
Comments
10 pages, 11 figures, 2025 Australasian Conference on Robotics and Automation (ACRA 2025)