用知识图谱提升机器人自主任务效率
A Methodology for Designing Knowledge-Driven Missions for Robots
- 分步构建知识图谱驱动的机器人任务流程
- 仿真搜救中无人机自主定位成功率显著提升
- 适合智能机器人系统设计者参考
本文提出一套完整的在 ROS 2 系统中实现知识图谱的方法论，旨在提升自主机器人任务的效率与智能化水平。该方法包括：定义初始与目标状态、分解任务与子任务、规划执行顺序、以知识图谱表示任务相关数据，并使用高层语言设计任务。各步骤逐层递进，确保从初始配置到最终执行的连贯性。通过在 Aerostack2 框架中基于 Gazebo 环境的仿真搜救任务演示，验证了该方法在提升决策能力与任务表现方面的有效性。
原文摘要 · Abstract (English)
This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence of autonomous robotic missions. The methodology encompasses several key steps: defining initial and target conditions, structuring tasks and subtasks, planning their sequence, representing task-related data in a knowledge graph, and designing the mission using a high-level language. Each step builds on the previous one to ensure a cohesive process from initial setup to final execution. A practical implementation within the Aerostack2 framework is demonstrated through a simulated search and rescue mission in a Gazebo environment, where drones autonomously locate a target. This implementation highlights the effectiveness of the methodology in improving decision-making and mission performance by leveraging knowledge graphs.
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