An Ontology-driven Dynamic Knowledge Base for Uninhabited Ground Vehicles

2026-02-11

AI Summary

This study addresses the limitations of unmanned ground vehicles (UGVs) in dynamic environments, where reliance on static prior knowledge often leads to insufficient situational awareness and constrained autonomous decision-making. To overcome this, the authors propose an ontology-driven dynamic knowledge base framework that, for the first time, integrates ontological representations with Dynamic Contextual Mission Data (DCMD), enabling near real-time updating and machine-executable modeling of environmental knowledge during mission execution. This approach transcends the constraints of traditional static knowledge systems, significantly enhancing UGV adaptability and autonomy in complex scenarios. Experimental validation in a laboratory-based reconnaissance task involving four cooperating UGVs demonstrates that the proposed system effectively improves situational awareness and ensures efficient mission completion.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Uninhabited Ground Vehicles
• Dynamic Contextual Mission Data
• Situation Awareness
• Autonomous Decision Making
• A Priori Information

Innovation

• ontology-driven
• dynamic knowledge base
• Uninhabited Ground Vehicles
• Dynamic Contextual Mission Data
• situation awareness