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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.20797 |
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| _version_ | 1866908794317963264 |
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| author | GP-Lenza, Guillermo Pita-Romero, Carmen DR. Fernandez-Cortizas, Miguel Campoy, Pascual |
| author_facet | GP-Lenza, Guillermo Pita-Romero, Carmen DR. Fernandez-Cortizas, Miguel Campoy, Pascual |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20797 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Methodology for Designing Knowledge-Driven Missions for Robots GP-Lenza, Guillermo Pita-Romero, Carmen DR. Fernandez-Cortizas, Miguel Campoy, Pascual Robotics 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. |
| title | A Methodology for Designing Knowledge-Driven Missions for Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.20797 |