为无人地面车构建动态知识库，实时更新环境信息提升自主决策能力
An Ontology-driven Dynamic Knowledge Base for Uninhabited Ground Vehicles
- 基于本体构建动态知识库，融合任务上下文数据实现实时更新
- 四辆无人车在实验室任务中成功实现情境感知与及时响应
- 适合军事边缘计算、复杂环境下的自主无人系统研发者
本文提出动态情境任务数据（DCMD）概念，构建面向无人地面车辆（UGVs）的本体驱动动态知识库，部署于战术边缘。该知识库通过近实时信息获取与分析，支持在任务中对平台级DCMD进行更新，以增强态势感知、提升自主决策能力，并适应复杂动态环境。由于无人车高度依赖任务前预设信息，任务中突发情况常导致识别模糊并增加人工干预。通过引入上下文信息动态更新先验知识，可充分释放无人车潜力。研究在四辆无人车组成的团队上实现了一项实验室级监视任务，结果表明，该本体驱动的动态环境表征具有机器可操作性，生成的上下文信息有效支撑了任务成功与及时执行，直接提升了态势感知水平。
原文摘要 · Abstract (English)
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.
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